title string | paper_decision string | review_1 string | rebuttals_1 string | review_2 string | rebuttals_2 string | review_3 string | rebuttals_3 string | review_4 string | rebuttals_4 string | global_rebuttals string | dataset_source string | conference_year int64 | review_5 string | rebuttals_5 string | review_6 string | rebuttals_6 string | review_7 string | rebuttals_7 string | review_8 string | rebuttals_8 string |
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Revisit the Power of Vanilla Knowledge Distillation: from Small Scale to Large Scale | Accept (poster) | Summary: This paper explores the power of vanilla distillation on large datasets and strong training recipes. It shows the stronger data augmentation and using larger datasets can decrease the gap between vanilla KD and other meticulously designed methods. The extensive results show vanilla KD's power.
Strengths: 1. T... | Rebuttal 1:
Rebuttal: **Response to weakness 1:** Thanks for your valuable comments. We first answer for the method should be applied for different settings. Through our empirical sutdy, we observe a trend wherein a small training set prefers a knowledge distillation (KD) method with stronger regularization or priors, ... | Summary: The paper explores the effectiveness of knowledge distillation (KD) approaches for limited-capacity architectures based on small-scale datasets. The authors identify the "small data pitfall" in previous KD methods, which leads to underestimation of the power of the vanilla KD framework on large-scale datasets ... | Rebuttal 1:
Rebuttal: # Response to Reviewer bACv part (1/2)
**Response to weakness 1:** Thanks for your valuable comments. Our observation suggests that, in the context of knowledge distillation tasks, smaller training sets exhibit a preference for methods featuring stronger priors. These methods effectively impart m... | Summary: This paper revisits vanilla knowledge distillation and presents an empirical analysis of the impact of model size, dataset scale, and training strategy on student performance in knowledge distillation. It identifies: 1) the gap between vanilla KD and other carefully designed KD methods gradually diminishes whe... | Rebuttal 1:
Rebuttal: **Response to weakness 1:** Thanks for your valuable suggestions. The trend is that a small training set prefers a knowledge distillation (KD) method with stronger regularization or priors, as it can effectively bring in more informative knowledge from the teacher model to enhance the performance ... | Summary: This paper investigates the effectiveness of vanilla Knowledge Distillation (KD) in large-scale datasets. The authors identify a "small data pitfall" which underestimates the power of vanilla KD on large-scale datasets and demonstrate that stronger data augmentation techniques and larger datasets can decrease... | Rebuttal 1:
Rebuttal: > **Weakness 1:** Lightweight models, such as MobileNetv3, are deemed critical model architectures due to their efficiency and compactness, which make them ideal for deployment on devices with limited computational resources. However, it appears that there is a lack of dedicated experiments specif... | Rebuttal 1:
Rebuttal: # Response to all reviewers
We thank all four reviewers for their constructive feedbacks which greatly improved the quality of our paper.
### **Response to Reviewer bACv, part (2/2)**
**Response to question 2:** Thanks for the insightful question. First, we analyzed the impact of task difficu... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Competitive Algorithm for Agnostic Active Learning | Accept (poster) | Summary: This paper studies agnostic active learning with a finite hypothesis space. The goal is to achieve a competitive query complexity with an optimal algorithm.
Strengths: Interesting and important problem.
The approximation hardness based on Set-Cover is a nice addition.
--- rebuttal comment ---
After reviewer... | Rebuttal 1:
Rebuttal: **Related work.** We appreciate the additional references, which we will include in the
paper and discuss. But you are wrong about the relationship between our results and those of Hanneke-Yang '15. They actually give a much weaker
form of ``optimality''. See our general response for further
dis... | Summary: This paper applied a multiplicative weight update / generalized binary search style algorithm to solve agnostic active learning. It proposes a novel "capping" approach to the weight (over hypotheses) to ensure the potential function always grows by some amount, and it proves this amount is lower bounded by $\... | Rebuttal 1:
Rebuttal: **1. On the relevance of time complexity.** We respectfully disagree that our contribution is mainly on the
information theoretic side. We view it as a structural observation of
how one can adapt a Bayes-inspired/multiplicative weights algorithm to
get the frequentist agnostic learning guarantee.... | Summary: The paper studies agnostic active learning by proposing a competitive algorithm that achieves at most a $\log H$ multiplicative factor on top of the optimal query lower bound of $m^*$. While similar result was known for the realizable setting, this paper makes a step toward understanding the agnostic setting. ... | Rebuttal 1:
Rebuttal: **Runtime.** We should give the runtime more precisely, and appreciate the comment. The main cost is
from checking if any $\epsilon$-ball of hypotheses has 80\%
probability, after each label seen. Naively this takes about
$O(|H|^2(|\mathcal{X}| + m))$ time, because it takes $|H|^2|\mathcal{X}|$ ... | Summary: The authors provide an algorithm for learning in the presence of agnostic noise. The algorithm finds a hypothesis that gets error $O(\eta)$ where $\eta$ is the noise parameter. The algorithm requires querying specific points, so it uses a stronger oracle than the standard active learning. The algorithm is poss... | Rebuttal 1:
Rebuttal: Definition of Active Learning
-------
You appear to be concerned that we assume we know $D_X$ and can query
$(Y | X = x)$, while some previous work defines active learning as:
given all the $x_i$ in a dataset, pick a subset of them to see the
corresponding $y_i$. However, in the limit of infinit... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments. We would like to emphasize that we give the first algorithm for active agnostic learning that is competitive with the optimal algorithm for a given input (unlabeled data and hypothesis class). There are a couple general points we would like to clarify, ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy | Accept (poster) | Summary: The paper presents results on the nonexistence of efficient learning algorithms for $3$-depth ReLU networks with smoothed parameters and Gaussian input. It also proves that in general, there is no efficient learning algorithm for $2$-depth ReLU networks with smoothed parameters and smoothed inputs (the smoothn... | Rebuttal 1:
Rebuttal: We thank the reviewer for their efforts.
Regarding the weaknesses:
1. The paper studies PAC learning, as defined in Definitions 2.1, 2.2 and 2.3. Thus, we study whether there exists an efficient algorithm that returns w.h.p. a hypothesis with a small population loss. In this common notion of lear... | Summary: This paper studies the computational complexity of learning 3-layer neural networks under the standard Gaussian distribution. Specifically, under a standard cryptographic assumption on the existence of local pseudorandom generators, the authors show that there is no poly-time algorithm that can learn 3-layer n... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions.
Regarding the comparison to [11]:
In [11], the authors showed hardness of learning depth-3 networks under the Gaussian input distribution, but the neural network in their construction is degenerate. Hence, their result does not imply hard... | Summary: The submission studies the classical problem of constructing ReLU-activated neural networks, specifically from the learning point of view. Previous work has established the existence of an efficient learning learning algorithm for learning depth-2 ReLU networks under the Gaussian distribution assuming a non-de... | Rebuttal 1:
Rebuttal: We thank the reviewer for their efforts.
Our hardness results show that learning neural networks under the Gaussian distribution is hard already for non-degenerate instances. This is in contrast to all previously known hardness results for learning neural networks. Since for depth-2 networks the ... | Summary: This paper addresses the complexity of learning neural networks, a
very fundamental problem in learning theory. Previously, some
complexity and efficient solvability results were known for networks
of depth 2. The paper shows that when the depth is increased to
3, the learning problem becomes computationa... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review.
Regarding the questions:
1. Yes, the result can be easily extended to any $k \geq 3$ by appending to our construction additional layers. We will add a remark about it in the camera-ready version.
2. Indeed, we referred the reader to papers where thi... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
No-Regret Learning with Unbounded Losses: The Case of Logarithmic Pooling | Accept (poster) | Summary: This paper studies the logarithmic pooling method for prediction using expert advice. At each step, $m$ experts report distributions $p^1, p^2, \ldots, p^m$ over a size-$n$ domain. The goal is to make predictions with a vanishing regret in terms of the log loss.
The usual logarithmic pooling returns an aggreg... | Rebuttal 1:
Rebuttal: Thank you for these comments. In response to the questions:
- Question 1: Our interpretation of this question is: what would happen if we changed our setting so that the aggregation algorithm chooses weights at the same time as the adversary chooses the probability distribution, such that neither ... | Summary: Summary of the paper
====================
* The prediction setting explored in this work is harder than the usual "prediction with experts advice" (henceforth abbreviated PwE) setting (e.g. as in the Cesa-Bianchi and Lugosi book), since the learner is required to reveal the expert weights (w_t) *before* observ... | Rebuttal 1:
Rebuttal: Thank you for these comments. A few responses:
- Regarding the first weakness (and first question), see our response to “Question 3” in the global rebuttal. Briefly, we argue that our goal is to study logarithmic pooling, and by allowing the aggregator to pick weights after seeing forecasts (i.e. ... | Summary: This paper investigates the logarithmic pooling method for minimizing log loss and introduces the OMD algorithm utilizing Tsallis entropy as a regularizer to update weights for the logarithmic pooling method. By assuming calibrated forecasts, the paper demonstrates that the proposed algorithm ensures a sub-lin... | Rebuttal 1:
Rebuttal: Thank you for these comments. We address the comments in the “Weaknesses” and “Questions” sections.
Weaknesses:
1. Thanks for the suggestion -- we agree, and will take care to do so in the final version, should our paper be accepted. We already cite [Cover, 1991] (“Universal Portfolios”), but tha... | Summary: The paper studies no-regret learning in the setting of logarithmic pooling of experts with the logarithmic loss. In this setting, there is a set of experts each outputting a distribution $p_i^{t}$ over outcomes in some finite set $Y$. The task of the learner is to output a vector $w^{t}$. An outcome $y$ in $Y$... | Rebuttal 1:
Rebuttal: Thank you for these comments. A few points and clarifications:
* We disagree that our setting is an instance of *linear* optimization. The log loss of a forecast is not a linear function of the forecast, nor of the weights that the aggregator assigns to the various experts.
* We disagree that the... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their thoughtful comments. In this global rebuttal, we will address three recurring questions:
1. Some reviewers were interested in further justification of the calibration property.
2. Some reviewers were interested in further justification of using logar... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning | Accept (poster) | Summary: The paper addresses the problem of offline multi-agent reinforcement learning. A way to make conservative updates to the Q-function is proposed, extending, non-trivially, CQL to multi-agent settings. Theory and experiments clearly validate the approach.
Strengths: The method is both well motivated as well as ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comment!
●**Q1: Typos, sentence improvement, and intuition explanation.**
As per your suggestion, we have fixed the typos and reorganized the sentences. The intuition behind our theories is as follows: Theorems 4.1 and 4.2 in our paper illustrate the differences in th... | Summary: Offline Reinforcement Learning in the multi-agent setting suffers from the combined effects of distribution shift and increasing number of agents. An exponential blowup of the action space in addition to Out-Of Distribution (OOD) actions hinders performance of RL agents. The work tackles these phenomena by pro... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and detailed syntax error check!
●**Q1: Presentation problem. What is the formal problem definition of offline RL? What is a behavior policy? What is distribution shift?**
We apologize for the unclear statement, and we are committed to improving the present... | Summary: This paper proposes a novel offline multi-agent reinforcement learning algorithm called Counterfactual Conservative Q-Learning (CFCQL) to address the overestimation issue and achieve team coordination at the same time. The algorithm calculates conservative regularization for each agent separately in a counterf... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback!
●**Q1: No empirical evidence to support the superior of our method on large agent number $n$.**
We apologize for any unclear experimental instructions in our paper. The evidence supporting our claim can be found in Section 5.1, specifically in the demo lab... | Summary:
This paper addresses challenges in Offline Multi-Agent Reinforcement Learning (MARL), which suffers from severe distribution shift issues and high dimensionality. To overcome these problems, the authors propose a novel MARL algorithm, CounterFactual Conservative Q-Learning (CFCQL). Unlike conventiona... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback!
●**Q1: The motivation for the algorithm is unclear, e.g., why considering counterfactual is helpful for offline MARL?**
We apologize for any confusion resulting from the unclear statement in our paper.
The motivation for this study is rooted in the obs... | Rebuttal 1:
Rebuttal: ●Q1: Explanation on current baselines and more baseline results.
We acknowledge that the baselines used in our paper (OMAR, ICQ, and MADTKD) are designed for **offline** MARL, not off-policy MARL. We recognize that our paper lacks sufficient comparison with single agent offline RL methods except ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining | Accept (poster) | Summary: This work proposes RapidBERT to train BERT in a faster way. Different from the previous accelerated method, this work attempts to employ some recent popular transformer architecture efficient designs as the basic modification of RapidBERT architecture, such revisions including the introduction of FlashAttentio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments.
> The authors should also post the RapidBERT results pre-trained on the English Wikipedia and the Books Corpus
The primary focus of our work was to show that certain architectural modifications and training choices lead to both a speed up as wel... | Summary: The paper benchmarks several architectural changes to BERT that allows for more efficient pretraining. More specifically, the paper adds flash attention, ALiBi position representations, and GLU activations to the original BERT architectures. For fair comparison, they re-implement the baseline BERT-base using t... | Rebuttal 1:
Rebuttal: > The contribution of this paper is limited.
> I’d hope that the authors can provide more experiments showing why all these modifications are necessary.
We have added many additional ablations to demonstrate the individual contributions of each of the changes to the architecture in the Author Re... | Summary: The paper introduces RapidBERT, an architecture and training paradigm for pretraining BERT-style language models that is cost-effective. The proposed approach incorporates several modifications into the conventional transformer encoder block, including FlashAttention, Attention with Linear Biases, Gated Linear... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and suggestions.
> approach primarily combines existing techniques…the overall novelty of the architectural choices is limited
We strongly but respectfully disagree with the reviewer’s concerns about novelty. The literature is full of papers that... | Summary: This paper proposes a new efficient recipe for training BERT, matching the original performance of BERT on GLUE in ~1h on 8x A100. To do so, the authors leverage a number of architectural/implementation improvements: FlashAttention, ALiBi, GeGLU, `bf16` layer norm, unpadding, and tweaks to the masking ratio. T... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and suggestions, and we try to address them here.
**W1** One of the primary goals in this paper is to develop a high-performance BERT architecture and recipe for pretraining on commercially available high-end hardware (A100-80GB GPUs). We agree wi... | Rebuttal 1:
Rebuttal: We ran further ablations in response to reviewer comments and have plotted downstream GLUE accuracy as a function of measured pretraining wall clock time. The comments here describe the additional experiments in detail.
The patterns in Figures R1 and R2 shed light on the individual effects of var... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a training recipe that can train a BERT-style encoder model efficiently (1.13 hours on 8 GPUs). The recipe combines several techniques including a higher masking rate for MLM, bf16, optimized vocabulary size. The model is trained on the C4 dataset for 1.13 hours and achieves 79.6 on GLUE. T... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and questions.
> The experiments are only conducted on GLUE tasks; it is not clear how well the trained model transfers to other datasets.
We chose to focus exclusively on the GLUE benchmark for multiple reasons. Since GLUE is a classic benchmark, fine-tu... | null | null | null | null | null | null |
No Representation Rules Them All in Category Discovery | Accept (poster) | Summary: This paper works on generalized category discovery, a setting that requires classifying unlabelled samples according to the taxonomy defined by the labeled set. This work identifies that a shortcut exists in previous benchmarks, and methods overlooking the classification metric defined by the labeled set could... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review of our work!
We first clarify that we will publicly release all code and the Clevr-4 dataset upon paper acceptance. Secondly, regarding the phrasing of our method (**“one concern is whether $\mu$GCD could be listed as a major contribution”**), we pr... | Summary: This paper contributes one new dataset and one new method for category discovery. The dataset is designed in a way that each image can be clustered into 4 different clustering based on the attribute of the image (shape, count, texture, and color), this design can be used to reveal the difficulty of unsupervise... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback, and hope to address their concerns as follows:
**“How would applying semi-supervised $k$-means algorithm on pre-trained models in tab 2 perform”**:
We have re-evaluated a representative selection of the backbones from Tab 2 in the paper, in both... | Summary: 1. This paper tackles the problem of generalized category discovery (GCD) and identifies the drawbacks of existing methods that they are verified only with labels for a single clustering of the data. In such a case, the model may simply perform unsupervised clustering, not correctly using the available labels.... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, and respond to their concerns as follows:
**“Additional recent methods should be compared…[DCCL @ CVPR 2023]”**
We thank the reviewer for bringing this interesting paper to our attention, we were unaware of DCCL @ CVPR 2023 as CVPR 2023 was held after th... | Summary: This paper addresses generalized class discovery (GCD) from a unique perspective. The authors argues that current GCD benchmarks are unable to ascertain whether models are using the available labels to solve the GCD task, or simply solving an unsupervised clustering problem. In light of this, this paper introd... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's feedback on on our work. We hope to address their concerns as follows:
**“The introduced dataset is focused on synthetic data. It could be more convincing to evaluate on real-world data”**:
We found it difficult to find an appropriate real dataset which contain... | Rebuttal 1:
Rebuttal: **Global response**:
We sincerely thank all reviewers for the time they spent reviewing our manuscript, and for their thoughtful feedback. We are encouraged that the reviewers found: the ideas *‘unique’* and *'overlooked by previous methods'* (V8PZ, s1RT); our proposed dataset *‘interesting’*, *'... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Many-body Approximation for Non-negative Tensors | Accept (poster) | Summary: Authors propose a tensor decomposition approach based on the Legendre decomposition and convex optimization with natural gradient-based algorithm for non-negative tensors which is inspired by many-body interactions in physics. The tensor is interpreted as a probability measure over a multidimensional discrete ... | Rebuttal 1:
Rebuttal: I appreciate your comments. We provide our point-by-point response to each of your comments in the following.
### In the "Weaknesses" section
> 1. From the presented numerical experiments, the actual advantages of the proposed approach over baselines are not quite clear to me.
Many-body approxi... | Summary: The paper proposes a new type of tensor decomposition, based on considering an interaction network among the different modes of a tensor. Nonnegative tensors are considered and factorized based on a Legendre decomposition, which may be viewed as representing the tensor elements by an underlying probability dis... | Rebuttal 1:
Rebuttal: We appreciate your careful reading and constructive comments. As described below, our proposal is ensured to be convex.
> My main concern is that from a tensor point of view, it seems impossible that decomposing a tensor into a many-body approximation for an arbitrary interaction network leads ... | Summary: This work introduces a novel approach to non-negative tensor decomposition, termed "many-body approximation," which specifically addresses the relationship among modes of tensors. It is formulated as a variant of Legendre decomposition and realized through globally minimizes the Kullback-Leibler divergence. Ad... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback.
> Some theoretical analysis about the proposed "many-body approximation" method is missed. Is it possible to derive some theoretical results to support the merits of the proposed "many-body approximation" in dealing with tensor completion and approximation p... | Summary: The authors introduce a new, energy-model based approach to the decomposition of non-negative tensors. They compare their method to mainstay techniques.
Strengths: The technique seems solid and reasonable, but how solid and reasonable is hard to assess (see below).
Weaknesses: As presented, it's hard for me ... | Rebuttal 1:
Rebuttal: Thank you for your comments about our novelty and impact.
According to your suggestion, we have prepared a table comparing the proposed method with major tensor decomposition methods, the CP decomposition, Tucker decomposition, tensor train decomposition, and tensor ring decompositions. If there... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful feedback and constructive suggestions. We responded to individual reviewers and look forward to further discussions.
Also, to address the concern raised by Reviewer wHyr, we submit a PDF file of reconstructed images of the COIL dataset to compare pr... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a novel many-body decomposition, based on Legendre decomposition, for non-negative tensor. Instead of specifying a rank parameter, the proposed method use energy-based model to the interactions among modes.
Strengths: 1. The paper is overall well-written and easy to follow. Especially the ... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback. We provide our point-by-point response to each of your comments in the following.
### In the "Weaknesses" section
> 1. The novelty of the proposed method is limited given the existing Legendre decomposition.
Although our proposal is based on Legendre de... | null | null | null | null | null | null |
Amortized Reparametrization: Efficient and Scalable Variational Inference for Latent SDEs | Accept (poster) | Summary: The authors develop a reparameterization scheme for affine SDEs which is advantageous in memory cost and time. Specifically, the authors consider splitting the computation into a series of different chunks such that each can be effectively parallelized and integrated individually by taking the expectation with... | Rebuttal 1:
Rebuttal: **The method only applies to SDEs where the marginal descriptions are known,
as the authors make an assumption that the evolution of the latent space
is given by a Markov Gaussian process.**
Thank you for your comments. Before addressing specific comments, we would like to
make an important cl... | Summary:
This paper introduces a method for identifying latent stochastic models from discrete time observations.
The authors perform
unbiased approximations to gradients of the
evidence lower bound to identify parameters and estimate the state for latent stochastic differential equations.
Thy propose to combine a re... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. Our response to specific questions and comments are provided below. One important clarification is that we only make the assumption
that the approximate posterior (i.e. the latent state given the observations) is Gaussian.
We only use this approximate post... | Summary: This paper proposes a new inference method for latent SDE model. Due to the continuous nature of SDE, inference methods for latent SDE models are usually expensive and the cost would grow as the dataset goes larger or longer. Existing methods also would require differential equation solvers, which makes them u... | Rebuttal 1:
Rebuttal: Thank you for your comments. We appreciate that you found our approach to be well-motivated, sensible
and well-supported by empirical evaluations. Responses to your specific questions and
concerns are provided below.
**Some technical details seem to be discussed too briefly in the main text, for ... | Summary: The present work proposes a time and memory efficient method to perform inference in a directed probabilistic model in the presence of a latent stochastic process with intractable posterior (path) distribution. Learning is performed using a variational Auto-Encoding approach, in which an approximate latent pos... | Rebuttal 1:
Rebuttal: Thank you for your comments.
**“The authors refer to unpublished work without stating the authors, cf. [14], but they included the reference in the supplementary material submitted. Notwithstanding the fact that I cannot call this good practice, this result is based on known facts and the derivat... | Rebuttal 1:
Rebuttal: ## General Comment
We would like to open by thanking the reviewers for their careful consideration of our work.
We understand that providing quality reviews is time-consuming, so we are thankful for your efforts.
Here we summarize the main changes made to the paper.
We address comments and questi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problem | Reject | Summary: This paper studied the problem of learning a graph neural network based policy network as a construction heuristic for solving job shop scheduling problems. This paper proposed an idea called residual scheduling to remove irrelevant operations and machines from the graph based state representation. This has be... | Rebuttal 1:
Rebuttal: **Weaknesses**
>The idea of using graph neural networks or other forms of deep neural networks trained through reinforcement learning to solve job shop scheduling problems has been studied in many past research works. The main text of this paper lacks a comprehensive review of these research work... | Summary: The paper introduces a novel approach called residual scheduling for solving the Job-shop scheduling problem (JSP) and its variant, flexible JSP (FJSP), focusing on removing irrelevant machines and jobs from the consideration set. Despite these problems being NP-hard, the proposed method demonstrates state-of-... | Rebuttal 1:
Rebuttal: **Weaknesses**
>The zero gap is mentioned. Upon reading, readers find out that the gap refers to "makespan gap." Understandably, significant bulk of existing papers on job-shop focus on makespan rather than tardiness. Ignoring tardiness may lead to poorer on time delivery, which is a weakness in ... | Summary: This paper proposed DRL based method to learn dispatching polices for (flexible) job-shop scheduling problems (JSP/FJSP). The main idea is to remove the completed operations from the state embedding, which is called residual scheduling, so as to improve the representation accuracy. The DRL agent uses a graph r... | Rebuttal 1:
Rebuttal: **Weaknesses**
>The main weakness is that the technical contribution is incremental. While the redisual scheduling idea is interesting and novel, a large part of the proposed method is similar to existing works. Specifically, the graph representation and heterogeneous graph neural network in Sect... | Summary: This paper proposes a deep reinforcement learning-based constructive heuristic to solve the (Flexible) Job Shop Scheduling Problem. An instance of the problem is represented as a graph and fed into a Graph Neural Network-based model which outputs a score for each candidate (operation-machine) pair. The model i... | Rebuttal 1:
Rebuttal: **Weaknesses**
>Limited novelty: the main contribution is the definition of the residual state at each step of the construction process by removing irrelevant operations and resetting the time reference.
Please see the section of “Author Rebuttal by Authors” above.
>This seems to me an incremen... | Rebuttal 1:
Rebuttal: Dear all reviewers, we appreciate your valuable comments. We would like to address the common concerns raised by reviewers.
**Novelty and contribution.**
In RS, the model simply focuses on the remaining non-dispatched operations, so the problem size is getting smaller as the process goes. Th... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
StreamNet: Memory-Efficient Streaming Tiny Deep Learning Inference on the Microcontroller | Accept (poster) | Summary: This paper presents methods for speeding up patch-based inference on microcontrollers. The method creates a buffer to selectively save intermediate values that are traditionally discarded in patch-based computation. This allows StreamNet to balance latency and memory consumption. There are 1d and 2d variants o... | Rebuttal 1:
Rebuttal: Thank you all for the valuable comments! In our revised version, we addressed all of the reviewer comments. The details of our reply and the changes are presented in the following.
D.1 Is this method purely a runtime/compiler optimization, or are there any model architecture implications (e.g. ... | Summary: This paper introduces StreamNet, a novel approach designed to eliminate the performance bottleneck associated with patch-based inference, which incurs additional computational overheads due to overlapping patches. StreamNet comprises two techniques, StreamNet-1D and StreamNet-2D, each offering a different trad... | Rebuttal 1:
Rebuttal: Thank you all for the valuable comments! In our revised version, we addressed all of the reviewer comments. The details of our reply and the changes are presented in the following.
C.1 The re-computation overhead reported in MCUNetV2 is only 10% for MobileNetV2, but such overhead appears signif... | Summary: The patch-based inference is widely employed for TinyML models on resource-constrained microcontroller units (MCUs), which significantly reduces memory requirements compared to layer-based inference. However, path-based inference can lead to a substantial increase in Multiply-Accumulates (MACs), as it introduc... | Rebuttal 1:
Rebuttal: Thank you all for the valuable comments! In our revised version, we addressed all of the reviewer comments. The details of our reply and the changes are presented in the following.
B.1 What is the reuse distance of patches?
The reuse distance of the StreamNet means the distance of the data in ... | Summary: The processing of patch-based inference for MCUs induce a large number of redundant MACs against the layer-wise processing because of the overlapped processing. In order to address this problem, this work designs StreamNet that employs the stream buffer to eliminate the redundant computation of patch-based in... | Rebuttal 1:
Rebuttal: Thank you all for the valuable comments! In our revised version, we addressed all of the reviewer comments. The details of our reply and the changes are presented in the following.
A.1 Could you provide more details about the models such as the number of layers, model sizes, and accuracy?
Our ... | Rebuttal 1:
Rebuttal: Thank you all for the valuable comments! In our revised version, we addressed all of the reviewer comments. The details of our reply and the changes are presented in the following.
C.1 The re-computation overhead reported in MCUNetV2 is only 10% for MobileNetV2, but such overhead appears signif... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Module-wise Training of Neural Networks via the Minimizing Movement Scheme | Accept (poster) | Summary: This paper proposes a new training method for greedy layer-wise or module-wise training of neural networks, which is compelling in constrained and on-device settings where memory is limited and suffers from a stagnation problem. Experimental results show that their method improves the accuracy of module-wise t... | Rebuttal 1:
Rebuttal: Dear Reviewer N1H7,
Thank you for your valuable review. We answer your remarks below.
**Weaknesses**
**1. Evidence for early overfitting**
This is indeed an interesting question. Our experiments in Figures 2 and 3 demonstrate that vanilla module-wise training performs very well in the early la... | Summary: The paper explores the module-wise training of neural networks via the Minimizing Movement Scheme. This approach aims to overcome the stagnation problem often encountered in layer-wise training, leading to improved accuracy and reduced memory usage. The authors compare their results with those of other methods... | Rebuttal 1:
Rebuttal: Dear Reviewer X5WU,
Thank you for your valuable review. We answer your remarks below.
**Weaknesses**
**1. Convergence**
Indeed this is an important question and this will be further discussed in the final version. In Section 2.2, we have assumed that the training of the individual modules conv... | Summary: The paper proposed a new regularization for module-wise training via the distance of the input and output of the module.
Strengths: 1. The proposed regularization is quite straightforward and easy to apply.
2. The paper connects the proposed regularization with optimal transport via theoretical analysis.
3. S... | Rebuttal 1:
Rebuttal: Dear Reviewer U1e3,
Thank you for your valuable review, we answer your remarks below.
**Weaknesses**
**1. Convergence**
Indeed this is an important question and this will be further discussed in the final version. In Section 2.2, we have assumed that the training of the individual modules conv... | Summary: TRGL offers a promising approach to module-wise training that addresses the stagnation problem, improves accuracy, and reduces memory usage in constrained and on-device settings. The method introduces a module-wise regularization inspired by the minimizing movement scheme for gradient flows in distribution spa... | Rebuttal 1:
Rebuttal: Dear Reviewer Ad5i,
Thank you for your valuable review. We answer your remarks below.
**Weaknesses**
**(1) Training time**
Of course like any other method that adds a term to the local loss (so most other methods), training time is slightly increased in favor of saving memory, which is the rea... | Rebuttal 1:
Rebuttal: Thanks to all the reviewers for their valuable insights. We have addressed in the individual answers the points raised by the reviewers. Since some comments are shared by different reviewers, we summarise here the main answers:
**1. Experiments on ImageNet (Reviewers U1e3 and N1H7)**
The reviewe... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Composing Parameter-Efficient Modules with Arithmetic Operation | Accept (poster) | Summary: This paper studies how to combine parameter-efficiently tuned models in the parameter space. The authors define two kinds of Arithmetic Operation for parameter ensemble: addition and negation. They evaluate their methods for distribution generalization, multi-tasking, detoxifying, and domain transfer. Extensiv... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and comments. Nevertheless, we kindly disagree with the reviewer, especially on the “novelty” assessment. It seems that there might be some misconceptions regarding our work's motivations and its differentiation from prior studies. We aim to elucidate our motivat... | Summary: The authors propose composing parameter-efficient modules (primarily LoRA and IA3 modules) directly in weight space, and show that simple linear combinations are able to achieve good performance in distributional generalization, multitasking, unlearning/de-toxifying, and domain transfer. The authors also show ... | Rebuttal 1:
Rebuttal: ### Q1: How $\lambda$ is chosen for each setting
As mentioned in Line 137 of the submission, $\lambda$ is generally tuned on the validation set. Specifically, for classification tasks, we vary $\lambda$ from 0 to 1 in increments of 0.02 on a validation set with a limited amount of data. Similar $\... | Summary: This paper proposes an efficient way to adapt pre-trained language models using parameter-efficient fine-tuning (PEFT). Instead of fully fine-tuning these models, the authors develop lightweight modules for each dataset, resulting in compact modules with varied skills. These modules are combined using linear a... | Rebuttal 1:
Rebuttal: ### Q1: Deviations and statistical tests for close results
Thanks for the advice! In our submission, we only conducted statistical tests for the domain transfer experiment in Table 4. We understand that some results in Table 1 are close and statistical tests are necessary there as well. We plan t... | Summary: This paper proposes an approach to compose parameter-efficient finetuning modules without requiring additional training. The modules can be added to combine capabilities, or negated to remove some abilities from the model. The paper shows how different combinations of modules may be used in multiple scenarios ... | Rebuttal 1:
Rebuttal: ### Q1: Comparison to other PEM combining work
This is a good point. There have indeed been some works that combine multiple PEMs in the past, such as Pfeiffer et al. 2021 [1] and Wang et al. 2022 [2] mentioned by the reviewer. However, these approaches are not comparable to ours because (1) they... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and comments, and we reply to the comments of each reviewer separately in the respective thread. Due to time limitations we could only address major points, but we’ll make sure to reflect all advice in future revisions. | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors proposed to perform an arithmetic combination of PEFT Modules. Suggested combinations were evaluated on distribution generalization, multitasking, detoxifying, and domain transfer tasks. Authors showed that combining PEFT Modules produces new modules with desired attributes.
Strengths: - The propo... | Rebuttal 1:
Rebuttal: ### Q1: Fine-tuning results on the full dataset for Table 1
Thanks for your advice! We run LoRA-tuning on the combination of the two subsets s0 and s1 for the eight GLUE tasks described in Table 1, and show the results in the following table (denoted as “full dataset”). Not surprisingly, we observ... | null | null | null | null | null | null |
Pairwise Causality Guided Transformers for Event Sequences | Accept (poster) | Summary: The paper addresses the limited exploration of incorporating causal knowledge into deep learning models for temporal event sequences. The authors propose a novel approach to enhance the performance of transformer-based models in multivariate event sequences by injecting pairwise qualitative causal knowledge. T... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and respond to specific questions below.
**Training Details.** We have included more training details in Section 3 of the Appendix (see "Model Implementation and Training"). Due to space limitations, we could not include the details around impleme... | Summary: The paper considers incorporating qualitative pairwise-casual relations into transformer based models for capturing temporal event sequences. The unbiassed estimation of the proposed measure, is ensured with theoretical justification. The experiments are conducted on both synthetic data, and several real even... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and address the key questions below. Importantly, we wish to clarify some misunderstandings that will hopefully clarify matters.
**Clarification about "Without" or "With" in L19.** It should indeed be "without" , as we have written. Our approach foc... | Summary: The paper proposes a method to incorporate additional background information into transformers that is pairwise causal i.e. event Z affects event Y. They do this for temporal event sequence data where the data is non-stationary and this casual relationship can be confounded by additional events.
Strengths: ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and respond to specific questions below.
**RNN vs. Transformer.**
Our setting is general to neural network architectures for (event) sequences. To this end, RNN style architectures fit our framework. We choose transformer-based models for practical ... | Summary: This paper focuses on the multivariate event sequences, where different types of events occur sequentially. The authors present an approach that leverages pairwise qualitative causal knowledge to enhance the performance of transformer-based models in handling multivariate event sequences. Specifically, the aut... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and respond to specific questions and comments below.
**Novelty of Proposed Approach.**
We argue that our theory is not a straightforward adaptation; rather, it is based on a careful formulation and design of mainstream transformer networks under... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes to use pairwise event causality pairs to improve the performance of transformer-based models, based on the intuition that causal knowledge encodes useful information like “event Z amplifies future occurrences of event Y”. Experiments demonstrate the performance of the proposed method.
Stren... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments. We will work towards further improving the general clarity of the paper.
We address the reviewer's specific questions below.
**Time Confounding and Assumption 1.**
Time confounding in our context is analogous to time-varying confounding in treatm... | null | null | null | null | null | null |
Compression with Bayesian Implicit Neural Representations | Accept (spotlight) | Summary: ## Summary
The authors propose an approximated correlation communication approach to compress Bayesian INF. Two practical considerations, such as prior fitting and posterior refinement are proposed. It achieves an R-D performance comparable to the SOTA INF method on image compression. Moreover, the audio compr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review and the valuable questions. We respond to their concerns below.
**Concerns regarding channel simulation:** While the reviewer states important questions about the quality of our coding scheme's posterior approximation quality (which we address below... | Summary: This paper proposes overfitting variational Bayesian neural networks to the data and compressing an approximate posterior weight sample using relative entropy coding, which enables direct optimization of the rate-distortion performance by minimizing the $\beta$-ELBO. Moreover, an iterative algorithm for learni... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and detailed review. We will carefully proofread the paper again and correct the typos and references in our final version. Moreover, we address the reviewer's questions below.
> It would be nice to compare the proposed method with the concurrent work... | Summary: The paper improves INR-based (image) compression by introducing majorly two techniques: 1) a relative entropy coding based model compressing framework as an alternative to commonly used quantization - entropy coding pipeline; 2) a semi-amortized approach to train the model prior which is similar to beta-VAE an... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and feedback on our work; we address the reviewer's concerns below.
> Seems that to train a model prior, we should first train many model posteriors. As also discussed by the authors, this training makes the entire training (encoding) time extreme... | Summary: This paper addresses the problem of lossy data compression (evaluation is on images and audio) using implicit neural representations (INRs). In this approach, a neural network is designed that maps coordinates (e.g., x,y locations in an image or time for audio) to samples (RGB values or audio amplitude). Then ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and constructive feedback and respond to the reviewer's concerns below.
> Obviously, the paper would be stronger if the empirical results were better. COMBINER is SOTA for INR-based methods (as far as I know), which is an important result, and I think... | Rebuttal 1:
Rebuttal: We extend our gratitude to all the reviewers for their comprehensive feedback and time spent reviewing our manuscript. It is heartening that all the reviewers agree that the idea proposed in this paper is novel and valuable. We have addressed their concerns in our respective responses.
In additio... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a new method for compressing general signals, by using Variational Bayesian implicit neural representations. It proposes an algorithm for learning a prior distribution over the implicit representation weights, as well as a pipeline for inferring the posterior distribution corresponding to ev... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and effort to review our paper and address your concerns below.
## Performance
In our paper, we focus on beating the previous INR-based compression methods. The performance of VAE-based methods is shown in Figure 2 using dotted lines for reference, similar to al... | null | null | null | null | null | null |
Cross-Scale MAE: A Tale of Multiscale Exploitation in Remote Sensing | Accept (poster) | Summary: The authors proposed a self-supervised method based on ViT masked auto encoders (MAE), namely Cross-Scale MAE, to improve the representations learnt in remote sensing based models. It includes a scale augmentation of each input image to learn features at different scales in the encoder while overcoming the nee... | Rebuttal 1:
Rebuttal: Thanks for your meaningful comments. Regarding to your question and comments, our answers are listed as following.
W1. On moving figures from Supplement to the main text. Thanks for the suggestion. We will move one of these figures to the main text and highlight the following: The graphs in the s... | Summary: This paper proposed a flexible self-supervised learning (SSL) framework that yields robust representations named Cross-Scale MAE by enforcing cross-scale information consistency at structural and semantic levels without needing aligned multiscale remote sensing imagery. And this paper deploys xFormers to reali... | Rebuttal 1:
Rebuttal: Thanks for your meaningful comments. Regarding to your question and comments, our answers are listed as following.
To the questions:
Q1. On semantic understanding: We would like to explain our viewpoint by addressing them at two levels of representations used in the loss. In the paper, we used t... | Summary: This paper proposes a Cross-Scale MAE to tackle the multiscale problem in remote sensing images. The triplet loss is designed including cross-scale consistency loss at the encoder, cross-scale prediction loss at the decode, and reconstruction loss. Comparative experiments show competitive performances.
Streng... | Rebuttal 1:
Rebuttal: Thanks for your meaningful comments. Regarding to your question and comments, our answers are listed as following.
W1. On “semantic” vs. “structural”: We agree with the reviewer about the meaning of low-level features. We would like to explain our viewpoint by addressing them at two levels of rep... | Summary: This paper presents Cross-Scale MAE, a self-supervised model built upon the Masked Auto-Encoder (MAE), which tackles the challenges in remote sensing image understanding (such as extensive coverage, hardware limitations, and misaligned multiscale images) by learning relationships between data at different scal... | Rebuttal 1:
Rebuttal: Q1&2)
On simplifying the Introduction and clarifying contribution and novelty: As suggested, we will remove Fig. 1 and integrate the information there to Fig. 2. We have also redrawn Fig. 2 with more detailed captions to clarify the contribution. Please refer to Fig. 3 of the rebuttal.
Essenti... | Rebuttal 1:
Rebuttal: We are appreciate all reviewers' significant comments.
Initially, we wish to elucidate that our supplementary material, accompanying the main paper, encompasses a wealth of substantial experimental outcomes. This includes the visualization of multiscale representation benefits, downstream task a... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors propose a Masked-Auto-Encoder approach for training remote sensing data at different scales. The model is pretrained on the FMoW-RGB and then the representations assessed through KNN classification performance on four other remote sensing datasets. Comparisons are made to Sat-MAE and Scale-MAE. ... | Rebuttal 1:
Rebuttal: Thanks for your meaningful comments. Regarding to your question and comments, our answers are listed as following.
To the weaknesses:
W1. On scale being fixed: We completely agree that remote sensing images usually come with known scale which is fixed for a specific sensing modality. This is why... | null | null | null | null | null | null |
LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference | Accept (poster) | Summary: The paper propose a novel framework called LinGCN, which reduces the multiplication depth and optimize the performance of Homomorphic Encryption based GCN inference. According to the evaluation results, LinGCN shows promising results in both latency speedup and inference accuracy over existing approaches.
St... | Rebuttal 1:
Rebuttal: We are very grateful for your valuable insight.
### Response to weakness 1:
With a reduced multiplication depth,computational cost and latency will also reduce. Analytically, the Q parameter is reduced with shallower depth, the coefficient of ciphertext polynomial also has a lower bit size, wh... | Summary: To improve the efficiency of HE-based PPML for GCN, in this paper, the authors propose LinGCN, an end-to-end framework for non-linear reduction and polynomial replacement. LinGCN features 3 key elements, including 1) a differentiable structural linearization algorithm, 2) a compact node-wise polynomial replace... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful review.
### Response to question 1 (Combined response with weakness 3):
Please refer to **Global Response (ii)** for further detail.
### Response to question 2 (Combined response with weakness 2):
Thanks for the valuable insight. To the best of our kn... | Summary: LinGCN optimizes HE-based GCN inference by reducing multiplication levels through a differentiable structural linearization algorithm and a compact node-wise polynomial replacement policy, both guided by a two-level distillation from an all-ReLU teacher model. LinGCN also improves HE solutions for GCN private ... | Rebuttal 1:
Rebuttal: We are very grateful for your valuable comments.
### Response to weakness:
Thanks for the important feedback. Our main contribution **structural linearization** for multiplication depth reduction is intrinsically different from existing linearization methods (unstructured linearization, layer... | Summary: This study presents an approach for enhancing the efficiency of private inference ( using homomorphic encryption (HE)) in Spatiotemporal graph convolutional networks through fine-grained and structured pruning/dropping of non-linearity. The proposed method consists of two main steps. Firstly, ReLUs are elimina... | Rebuttal 1:
Rebuttal: We are very grateful for your constructive comments.
### Response to limitation 1, novelty:
CryptoGCN's approach to employing layer-wise nonlinear pruning and polynomial replacement results in a substantial degradation in accuracy and an insufficient reduction in multiplication depth. Our pro... | Rebuttal 1:
Rebuttal: ## Global Response:
We truly appreciate your valuable and constructive comments. We have made a substantial effort to clarify your doubts and enrich our experiments in the rebuttal phase. Below are the responses to two common doubts:
### (i). New dataset evaluation:
Without loss of general... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Evolving Standardization for Continual Domain Generalization over Temporal Drift | Accept (poster) | Summary: Mitigating the temporal distribution shift problem is meaningful in realistic. This paper introduces the problem of continual domain generalization over temporal drift (CDGTD), aiming to address the issue of potential temporal distribution shifts in unseen test domains. The authors propose an Evolving Standard... | Rebuttal 1:
Rebuttal: Sincerely thanks for your efforts in reviewing the paper. Below, we respond to your questions in detail.
> **Q1:** The used datasets are only a subset of datasets from the Wilds-Time benchmark. It would be valuable to include MIMIC-Readmission and MIMIC-Mortality datasets.
**A1:** Thanks for you... | Summary: This paper introduces a problem formulation for Continual Domain Generalization over Temporal Drift (CDGTD) and proposes the Evolving Standardization (EvoS) method to address the challenge of gradually shifting data distributions over time, aiming to generalize to unseen domains that are not too far into the f... | Rebuttal 1:
Rebuttal: Thanks for your efforts in reviewing the paper. Below, we address your concerns in detail.
> **Q1:** This setting contradicts the idea of Domain Generalization.
**A1:** Thanks. Firstly, we want to clarify that our CDGTD is a challenging and practical variant of conventional DG. Hence, its settin... | Summary: This paper introduces the problem of continual domain Generalization over Temporal Drift (CDGTD), where the domain distribution gradually changes over time and the model needs to generalize to new domains in the near future with training domains sequentially arriving. And this paper also proposes an Evolving S... | Rebuttal 1:
Rebuttal: We are grateful for your efforts in reviewing the paper as well as your constructive comments. Below, we do our utmost to address your concerns.
> **Q1:** I am wondering whether the number of heads ($n\_h$) and the feature dimension of heads ($d\_h$) are the same in each attention module $\mathca... | Summary: This paper considers domain generalization over temporal-drift data where the model is trained online and required to generalize to the unseen future domain. The proposed method, Evolving Standardization (EvoS), assumes each domain follows a Gaussian distribution and utilizes transformers to capture the tempor... | Rebuttal 1:
Rebuttal: Thanks a lot for your efforts in reviewing the paper. Below, we respond to your questions in detail.
> **Q1:** The technical contribution is incremental.
**A1:** Thanks. Firstly, although transformers can model sequential relationships, they require inputs themselves to be of sequentiality, like... | Rebuttal 1:
Rebuttal: Sincerely thank all reviewers for the efforts in reviewing our paper and the constructive suggestions. We are more than encouraged that reviewers find
* our proposed problem of Continual Domain Generalization over Temporal Drift (CDGTD) to be **interesting** and **novel** (*Reviewer Nwuj*), **inn... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Cross-Moment Approach for Causal Effect Estimation | Accept (spotlight) | Summary: This work proposes a cross-moment approach to estimating the average causal effect with latent confounders in linear SCM. One proxy variable of the latent confounder can be observed. In contrast to prior research (e.g., difference-in-difference) that requires stringent assumptions, this work shows that the cau... | Rebuttal 1:
Rebuttal: **Regarding comments in weaknesses**
1. [Concerns about evaluations] Regarding the method in [SGKZ20], as mentioned in lines 70-71, it is based on solving an over-complete independent component analysis (OICA) which, in practice, can get stuck in bad local minima and return wrong results. In our ... | Summary: This work focuses on estimating the causal effect in the presence of an unmeasured confounder within a linear causal model. The authors demonstrate that the desired causal effect can be identified by employing a single proxy variable, leveraging its non-Gaussian characteristics. Additionally, they propose a Cr... | Rebuttal 1:
Rebuttal: **Regarding comments in weaknesses**
1. [Linearity assumption] Please refer to the global response.
2. [Single latent confounder] Please refer to the global response.
**Questions**
1. As shown in Theorem 1, the causal effect is identifiable if condition in (5) is satisfied for the latent confound... | Summary: This paper introduces an innovative technique for estimating the causal effect of a treatment on an outcome within linear structural causal models. This method utilizes cross moments, which are statistical moments derived from the joint distribution of the treatment and outcome variables, to quantify the causa... | Rebuttal 1:
Rebuttal: **Regarding comments in weaknesses**
As we mentioned in the global response, please note that most of these methods require at least two proxy variables and also further "Completeness" assumptions. Nevertheless, in the attachment of global response, we did additional experiments for comparing wit... | Summary: The authors consider the estimation of causal effect in linear SCM with independent errors when there is a latent confounder U and one proxy variable of U (negative control outcome).
They generalize the DiD literature by relaxing the assumption of common trends and propose a general identification formula un... | Rebuttal 1:
Rebuttal: **Regarding comments in weaknesses**
1. [About linearity and independent exogenous noises assumptions] Independence of exogenous errors is the main and standard assumption in structural causal models (SCM) which is based on the principle of independent mechanism (For more discussion on why this is... | Rebuttal 1:
Rebuttal: # Global Response
We thank the reviewers for their time and valuable feedback. In the following, we provide a global response to some of the concerns/questions raised in the review.
**About linearity assumption:** The linearity assumptions present in a large body of the research in causal discove... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Quantification of Uncertainty with Adversarial Models | Accept (poster) | Summary: The authors propose a new method for estimating epistemic uncertainty. They propose to adversarially search for modes of the posterior distribution. Empirically, they demonstrate that their uncertainty performs well on OOD detection.
Strengths: The empirical results of this approach on OOD detection are very ... | Rebuttal 1:
Rebuttal: We thank the reviewer for this assessment of our work. Regarding the stated weaknesses:
- **Calibration:** Thank you for proposing this interesting direction. Your intuition was right, we found that our method indeed improves upon the other considered baseline methods, although it was not directly... | Summary: The paper introduces Quantification of Uncertainty with Adversarial Models (QUAM), a novel approach for epistemic uncertainty estimation in deep learning. Well-known uncertainty quantification approaches, such as Deep Ensembles or variational inference, underestimate the epistemic uncertainty by sampling from ... | Rebuttal 1:
Rebuttal: We thank the reviewer for this very positive assessment of our work.
Indeed we missed out on formally introducing the symbols for the cross-entropy, the KL-divergence and the mutual information in equation (1) in the main paper. Thank you for pointing out we will correct this in the final versio... | Summary: This paper introduces Quantification of Uncertainty with Adversarial Models (QUAM). Building on the claim that previous epistemic uncertainty estimation methods (e.g. MC dropout, Deep Ensembles) underestimate the epistemic uncertainty by only considering the posterior distribution when sampling models, the aut... | Rebuttal 1:
Rebuttal: We thank the reviewer for this thoughtful feedback and critical assessment of our work, as well as the concrete suggestions to improve clarity. We will change the manuscript according to the reviewer’s suggestions for the final version as follows:
- **Clarity:**
1) *Definition of adversarial mo... | Summary:
This paper is about uncertainty estimation using adversarial models (not examples!). The authors propose a new uncertainty estimation method, called QUAM, which performs a search of an adversarial model, which is one that fits the training set but has predictions far away from the predefined model, with the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful assessment of our work. Regarding the explicitly stated remarks and questions:
- **Aleatoric uncertainty:** Your remark is correct, aleatoric uncertainty is indeed a property of the data, stemming from the stochasticity / noise in the measurement process as... | Rebuttal 1:
Rebuttal: We thank all reviewers again for the time and effort they have invested in order to provide their high quality feedback.
All reviewers found our approach novel and relevant, and acclaimed the general applicability of the method as well as the empirical performance. Nevertheless, reviewers were ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Replicability in Reinforcement Learning | Accept (poster) | Summary: This work studies the question of reproducibility in reinforcement learning (RL). They define reproducibility as an algorithm returning the same policy on two different random draws from the environment, with probability at least $\rho$. In the generative model setting, they show that there exists an algorithm... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their constructive comments.
> The paper does not clearly motivated why we should care about reproducibility in RL.. but I think this is necessary given the novelty of it.
The reviewer raises an important point. In many applications, like mean estimation... | Summary: The paper studies replicable reinforcement learning algorithms in the tabular MDP setting with an oracle generative model. The paper gives the first lower-bound for the sample complexity of a $\rho$-replicable $(\varepsilon, \delta)$-optimal algorithm. To obtain this lower-bound, the paper first builds an info... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their constructive comments.
> In my opinion, the structure of the paper can be slightly improved. The reductions between the optimal Q-function estimation and the optimal policy, the multiple coin estimation problem and RL on tabular MDPs are at a high lev... | Summary: The paper makes a significant contribution by introducing a theoretical study of replicability in reinforcement learning. It focuses on discounted tabular Markov Decision Processes (MDPs) with generative models and explores two definitions of replicability: exact and approximate versions. For the exact version... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their constructive comments.
> While the topic of studying replicability in RL (Reinforcement Learning) is intriguing, I have reservations about its significance. For instance, if there exists a single optimal policy, it logically follows that all RL algori... | Summary: Reproducibility is a big problem in RL. This paper builds on Impagliazzo (2022) on replicability in learning, and develops a replicability framework for RL. It focuses on a discounted tabular MDP setting with a generative model. The replicability problem is given shared internal randomness, how many samples ar... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their constructive comments.
> As we all well know... So the question is what more have we learnt from the results in this paper? I think the results in this paper are essentially confidence interval type calculations just dressed up nicely as $\rho$-replic... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs | Accept (spotlight) | Summary: The paper introduces a task to evaluate the ability of large language models (LLMs) to resolve conversational implicatures and go beyond the literal interpretation of the meaning of the language. The evaluation is based on a dataset of naturally occurring implicatures that are converted into a binary classific... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to thoroughly review, rating the soundness and presentation as good. We are happy the reviewer believes our evaluation protocol is original and all the material is provided to reproduce it. We justify below why _our contribution is especially important in ... | Summary: This paper evaluates several popular LLMs on a benchmark for evaluating pragmatic reasoning via resolving question-answer conversation snippets into the implied binary answer. The study explores the influence of different factors in LLM design, including training method (next-token prediction vs. instruction t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the supportive review, saying the paper offers a _“very comprehensive evaluation”_ and that _“the finding that few-shot examples are useful mostly for conveying format [..] is very interesting”_. Below, we address questions. To summarise, we:
- present new results showing... | Summary: The authors analyze the behavior of LLM pragmatic understanding (capability to add and omit imformation to efficiently communicate in context) using a new evaluation protocol. They compare LLM performance to human performance and argue that instruction fine-tuning with examples may improve pragmatic understand... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time, and for the supportive and positive review. We are happy to see the reviewer believes the soundness and presentation are excellent and the contribution is good, stating the paper *“has clear value to the community”*. Below, we address the questions raised... | Summary: This paper investigates how well recent LLMs preform on resolving conversational implicatures. It presents a task on conversational implicature resolution built on top of the crowdsourced and human-annotated dataset of George and Mamidi (2020). The work presents an evaluation protocol that lays out how LLMs ar... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review and for the in-depth suggestions. We are glad to read that the reviewer thinks our work _“addresses an important problem”_ and contains _“extensive experiments on a wide range of recent LLMs”_, rating the soundness, presentation, and contribution... | Rebuttal 1:
Rebuttal: This response is for **reviewer VB9K** and **reviewer SeJL**, who ask about our contribution in light of the BIG bench results. With the below we aim to motivate in further detail why we believe the BIG bench result cannot be built upon in a scientific way. Hence, our work is an important contribu... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Approximately Equivariant Graph Networks | Accept (poster) | Summary: This work discusses a non-trivial case of equivariant graph networks. In this scenario, the input graph $G$ remains fixed, and therefore, the relevant permutations that act on the graph signals are the automorphisms of $G$. Under this assumption, the authors describe a bias-variance tradeoff with respect to th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and appreciation of our work. We provide detailed responses to the each section point by point:
### Responses to Weaknesses:
1. (Graph coarsening details) We thank the reviewer for raising this point and will add more discussion of graph coarse... | Summary: This paper formalizes the notion of active symmetries and approximate symmetries of GNNs on a fixed graph domain. Furthermore, it theoretically characterizes the statistical risk of linear regression with symmetries and show a bias-variance tradeoff. For graph tasks, it utilize
coarsed graph for approximate s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and appreciation of our theoretical contribution. We provide detailed responses to the each section point by point:
### Responses to Weaknesses:
1. (Result in Section 3.1 seems trivial and useless) We want to remark that considering simplistic models to ana... | Summary: The authors discuss the generalization of learning a map that is equivariant to one group, while the ground truth is (approximately) equivariant to another group. Their theoretical analysis first considers the case where the ground truth is exactly equivariant and finds a bias-variance trade-off: making the hy... | Rebuttal 1:
Rebuttal: We thank the reviewer for the critical assessment and constructive feedback of our work. We provide detailed responses to the each section point by point:
### Responses to Weaknesses:
1. (why risk gap) We will add more motivation in Section 3 for the risk gap. To summarize: The risk quantifies h... | Summary: The authors observe that – while graphs considered as a class have global permutation symmetry – for specific problems, e.g. when learning on a fixed graph, the graph has a much smaller symmetry. Consequently, they attempt to answer the question of how symmetric the model should be in comparison with global pe... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully examining our work and appreciating the novelty and the impactfulness of our paper. We provide detailed responses to the Weakness and Questions section.
### Responses to Weaknesses:
1. (spell check): We thank the reviewer for pointing this out and will fix all... | Rebuttal 1:
Rebuttal: ## Common Response
We thank the reviewers for their detailed assessment of our work, and their appreciation for the novelty and the impactfulness of our paper. We are encouraged that all reviewers found that our theoretical results are novel and interesting, particularly on the symmetry bias-vari... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Self-Adaptive Motion Tracking against On-body Displacement of Flexible Sensors | Accept (poster) | Summary: In the context of ubiquitous sensing, the paper addresses the issue that on-body devices cannot be firmly worn at a fixed position across different sessions by adapting to unknown displacements. The authors propose three main contributions: (1) A transformation layer adapts to unknown displacements, (2) an LST... | Rebuttal 1:
Title: Re: Rebuttal
Comment: Supporting what reviewer ncj6 mentioned: please address the rebuttal to each review - this makes it easier for the reviewer to enter the discussion based on the own review.
Now to the point: thank you for the rebuttal and the provided PDF. My main criticism about additional par... | Summary: This paper shows an approach for adaptive learning to track motion trajectories from elbow pad sensor, especially concentrating on modelling of sensor displacements in unsupervised manner during the operation. Tracking method is based on multi-layer neural network architecture with learnable affine layer, Four... | Rebuttal 1:
Title: Response to rebuttal
Comment: I have read rebuttal and other reviews. I would like to thank authors for additional benchmarking (i.e., with more subjects, additional dataset, and against more related SOTA methods), which definitely improves the original manuscript and results. However, in overall I s... | Summary: Flexible sensors are useful for tracking human status as wearable systems, but they can become displaced when worn, causing challenges for machine learning algorithms. The proposed solution of this paper is a self-adaptive motion tracking network that includes a learnable Affine Transformation layer, a Fourier... | null | Summary: This paper presents an approach to self-adaptive motion tracking using on-body, flexible sensors that in real world applications may be subject to displacements. The authors present a network that contains a component that automatically learns an affine transformation between training data (with annotations in... | null | Rebuttal 1:
Rebuttal: # Reviewer #ncj6
## Q1. Limited participants / recording length / physique of participants / task.
To further demonstrate the generalization ability of our method, we conducted additional experiments with five new participants of varying body types (see Table 1 in the uploaded pdf file).
Each part... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Understanding Contrastive Learning via Distributionally Robust Optimization | Accept (poster) | Summary: This paper tackles the sampling bias problem in contrastive learning, where negative samples are usually sampled randomly from the marginal distribution and may contain false positive samples.
The authors provide a connection between the contrastive learning objective and distributionally robust optimization a... | Rebuttal 1:
Rebuttal: # Response to Reviewer C48x:
Dear Reviewer,
Much thanks for your detailed comments. In the revised version, we will meticulously polish the paper in accordance with your feedback, correcting typos and providing clear explanations of notations. Also, we find there may exist some misunderstandings... | Summary: This paper starts from the question why the naive form of CL is robust to sampling bias issue, resulting in empirical success in various areas. T this end, the authors first present the relationship between CL and DRO theoretically, where the DRO-constrained CL objective is conceptually equivalent to the objec... | Rebuttal 1:
Rebuttal: # Response to Reviewer rLbF:
Dear Reviewer,
We appreciate your recognition of our contribution on the connection between CL and DRO. We also express our gratitude for your insightful inquiries regarding ADNCE. Below, we present responses to your comments:
**Q1: Why such Gaussian-like weights ar... | Summary: The paper proposes a novel theoretical framework for understanding contrastive learning (CL) via distributionally robust optimization (DRO).
Under this framework, the paper derives that the InfoNCE loss can be interpreted as DRO with KL ball around the negative sampling distribution, and the paper leverages D... | Rebuttal 1:
Rebuttal: # Response to Reviewer sD47:
Dear Reviewer,
We greatly appreciate your acknowledgement of our contributions and your insightful comments. In what follows, we provide responses to the questions you have raised:
**Q1: Concerning on the burden of hyperparameter tuning in ADNCE**
A1: Considering... | Summary: In this work the authors demonstrate a connection between contrastive learning, in particular InfoNCE, and distributionally robust optimization. In contrastive learning algorithms it is typical during training that samples which are similar are treated as being different, ie a negative pair, since contrastive ... | Rebuttal 1:
Rebuttal: # Response to Reviewer CyLz:
Dear Reviewer,
We sincerely appreciate your recognition of our work and deeply regret any confusion caused by typographical errors or unclear notations within our paper. Your detailed comments are highly valued. In the revised version, we commit to meticulously refini... | Rebuttal 1:
Rebuttal: # Overall Rebuttal:
We thank all reviewers for taking the time to review our paper and for providing valuable and insightful feedback. We are delighted to see that our work has been recognized for its contributions and inspiration to the contrastive learning community, as mentioned by Reviewers $\... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Tree of Thoughts: Deliberate Problem Solving with Large Language Models | Accept (oral) | Summary: The paper introduces an innovative concept of problem-solving that utilizes a tree-like structure of thoughts, constructed and evaluated by the LLM, specifically GPT-4. To illustrate the efficacy of this technique, the authors have incorporated three distinct tasks: the Game of 24, creative writing, and crossw... | Rebuttal 1:
Rebuttal:
Thank you for your detailed and constructive feedback!
### 1. Cost and efficiency
This is a great point. Please see **General Response (3)**.
### 2. Running IO/CoT baselines many times
We showed Game of 24 IO/CoT best-of-k results in Table 2 and Figure 3, where CoT best-of-100 has a game succe... | Summary: This paper proposes Tree of Thoughts to promote deliberate problem solving with LLMs. By using a tree-based structure and a four-step process towards problem solving, tree of thoughts successfully address many of the challenges with left-to-right decoding such as looking ahead, backtracking, considering multip... | Rebuttal 1:
Rebuttal:
Thank you for finding our work "well-motivated", "novel", "clearly described", and "convincing".
### 1. Adapt ToT to other tasks.
This is a great question. Please see **General Response (1)**, where we show a simple scheme that adapts ToT to StrategyQA and GSM8K with near-zero task-specific han... | Summary: The paper presents a new framework for language model inference called Tree of Thoughts, which aims to improve the ability of language models to solve complex, multi-step problem-solving tasks.
The framework involves generating a tree of possible plans for solving a given task, with each node in the tree rep... | Rebuttal 1:
Rebuttal:
Thank you for your thoughtful comments, all of which are very helpful for improving our work!
### 1. Related work and BFS/DFS vs. MCTS
Thank you for pointing out these recent or concurrent papers related to ToT. We will discuss them in our related work section.
We used BFS and DFS as they are t... | Summary: This paper introduces a new method for prompting large language models (LLMs) for multi-step reasoning tasks. Existing prompting methods are confined to the autoregressive generation scheme, making it difficult for LLMs to finish tasks that require exploration and planning. To alleviate this problem, the autho... | Rebuttal 1:
Rebuttal: Thank you for endorsing our work!
### 1. More application scenarios
This is a great point. Please check **General Response (1)**, where we show a very simple scheme to apply ToT in common NLP tasks (StrategyQA, GSM8K). However, such tasks might not need GPT-4 + ToT as GPT-4 + COT suffices --- we... | Rebuttal 1:
Rebuttal: We appreciate all reviewer's great feedback, which will significantly strengthen our draft!
The motivation of ToT is simple: **to explore and extend the capability frontier of autoregressive LLMs**. More specifically, given the SoTA LLM (GPT-4) can already solve many existing NLP tasks, what new ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Characteristic Circuits | Accept (oral) | Summary: I am not qualified to review this paper.
Strengths: I am not qualified to review this paper.
Weaknesses: I am not qualified to review this paper.
Technical Quality: 4 excellent
Clarity: 4 excellent
Questions for Authors: I am not qualified to review this paper.
Confidence: 1: Your assessment is an educat... | null | Summary: This work proposes a new tractable probabilistic model called characteristic circuits or CC. The CC is defined in a similar way to probabilistic circuits (PCs) but with leave nodes defined as characteristic functions instead of the distributions as in PCs. The authors further show the computation of marginals ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback and questions.
> Key difference between CCs and PCs:
- PCs do not naturally lend themselves to a unified view over heterogeneous data domains, while CCs more naturally provide a framework to model high-dimensional mixed data distributions. To ... | Summary: The paper introduces characteristic circuits (CCs), a new family of tractable probabilistic models (TPMs) that leverages univariate characteristic functions as leaves of probabilistic circuits (PCs) for modelling a tractable joint of heterogeneous data distributions (i.e. with both continuous and discrete vari... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for pointing out both the strength and possible weakness of our work.
> Inappropriate structure can limit the modelling power:
- Similar to related modelling families (e.g., PCs, PGCs), the structure can have a high impact on the performance of the CC. To mi... | Summary: This manuscript proposes a framework for directly representing characteristic function of random variables by a probabilistic circuit-like structure. Unlike the ordinal probabilistic networks, the proposed framework, characteristic circuits, can treat distributions that do not have closed-form expressions for ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and the suggested related work.
> Comparison to PGCs
- Indeed, PGCs are related to CCs as both can be considered to represent the probability distribution using its generating function rather than its density function. We added a discussion and further de... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the use of characteristic functions as probabilistic models for heterogeneous data and proposes characteristic circuits (CC) for their representations. The authors propose efficient algorithms for computing (marginal) densities with CCs and show that parameters of CCs can be learned by minim... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback and questions.
> How is the probability measure encoded as a CF?
- The characteristic function of a probability measure is its Fourier transform and, hence, can be obtained through the application of the Fourier transform. However, in our wo... | null | null | null | null | null | null |
Replicable Clustering | Accept (poster) | Summary: This paper studies the design of clustering approximation algorithms in the context of statistical clustering under the notion of replicability. In replicable clustering problem, it requires that with high probability, the output of the algorithms should have the exact same partition of the sample space after ... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the extensibility of our work and their time and effort in reading our paper. We address their comments as follows.
> The sample complexity has exponential dependence on the dimension
, which could be the main weakness for this paper when
is large. Althoug... | Summary: This manuscript focuses on the concept of replicability in statistical clustering algorithms. It introduces the notion of replicability, which refers to the ability of an algorithm to produce consistent results when executed multiple times on different inputs from the same distribution. Replicability is seen a... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the importance of replicability in clustering as well as their time and effort in reading our paper. We address their comments as follows.
> 1] How does the proposed replicable algorithm compare to existing clustering algorithms in terms of performance and ac... | Summary: This paper studies the concept of replicability in clustering. This topic is important because replicability is what allows for other researchers to reproduce the results of a study to verify their correctness. This is a big problem these days, 50% of scientists saying that there is a replicability crisis. In ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort in reading our paper and address their comments as follows.
> I wasn’t sure by reading the paper how their replicability results compare to previous literature.
> How do your algorithms compare to existing algorithms? Is there any baseline implied b... | Summary: The topic of this paper is replicable clustering which in high-level asks for design of an algorithm that given two runs of the algorithm on different samples from the “same” input distribution. Replicability is a notion introduced recently in a work by Impagliazzo et al. [2022].
The algorithm for statistica... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort in reading our paper and address their comments as follows.
> The paper is following the notion of [Impagliazzo et al., 20]; however, I still not quite convinced why the shared randomness assumption is meaningful. A related question is whether an as... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper initiates the study of formal replicability for clustering algorithms. Replicability is defined to be a property of an algorithm for a statistical clustering problem, requiring that fixing the internal randomness of the algorithm while resampling the input data will yield exactly the same (represent... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the importance of replicability in clustering algorithms. We appreciate the reviewer for their time and effort in reading our paper and address their constructive comments as follows.
> What is the cost of replicable clustering compared to non-replicable algo... | null | null | null | null | null | null |
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance | Accept (poster) | Summary: The paper focuses on the robustness of explanations. It begins by defining explanation invariance and equivariance concepts using geometric deep learning formalism and demonstrates that certain popular interpretability methods inherently possess theoretical robustness guarantees. Two metrics, invariance and eq... | Rebuttal 1:
Rebuttal: # Rebuttal Reviewer wVxe
We would like to thank the reviewer for taking the time to make encouraging comments and constructive criticisms. By following the reviewer's suggestions, we were able to:
* Emphasize the theoretical robustness guarantees that we derived in the appendix.
* Clarify the va... | Summary: This paper study the robustness of several post-hoc interpretability methods against the transformation of input data. The robustness is measured by invariance and equivariance metrics. Theoretical robustness guarantees and a systematic approach to increase the invariance are derived. Finally, the authors cond... | Rebuttal 1:
Rebuttal: # Rebuttal Reviewer LQbZ
We would like to thank the reviewer for taking the time to make encouraging comments and constructive criticisms. By following the reviewer's suggestions, we were able to:
* Discuss how to choose between equivariance and equivariance when measuring robustness.
* Emphasiz... | Summary: This paper proposes the definition of the robustness of explanations with respect to the model symmetry group. For models invariant to some symmetry group, the explanation should also be invariant or equivariant to it. The paper derives two metrics to measure the invariance and equivalence of explanations and ... | Rebuttal 1:
Rebuttal: # Rebuttal Reviewer u2n8
We would like to thank the reviewer for taking the time to make encouraging comments and constructive criticisms. By following the reviewer's suggestions, we were able to:
* Stretch the applicability of our framework with models that are not perfectly invariant and NLP a... | Summary: The core contribution of this work is the direction of measuring explanation robustness through a more broader set of data perturbations/transformations for different data modalities which have not been discussed in literature thus far. For instance shift transformations in images, cyclic translations in time ... | Rebuttal 1:
Rebuttal: # Rebuttal Reviewer NZiw
We would like to thank the reviewer for taking the time to make encouraging comments and constructive criticisms. By following the reviewer's suggestions, we were able to:
* Show that our metrics can also be used to characterize explanations of models that are not invari... | Rebuttal 1:
Rebuttal: # Global Rebuttal
The below contains a rebuttal for remarks that are common to most reviewers.
## Clear explanations for symmetry groups
In *Table 3* from *Appendix F*, we have included all the details necessary to understand the action of each symmetry group. Following the reviewer's suggestio... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the authors propose a set of desiderata for explanation methods for neural networks ranging from CNNs to GNNs. They postulate that any explanation that is able to faithfully explain the model should be in agreement with the invariance properties exhibited by the underlying model. They formalize ... | Rebuttal 1:
Rebuttal: # Rebuttal Reviewer 7REw
We would like to thank the reviewer for taking the time to make encouraging comments and constructive criticisms. By following the reviewer's suggestions, we were able to:
* Clarify the various symmetry groups appearing in the experiments.
* Better contextualize the raw ... | null | null | null | null | null | null |
Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing | Accept (poster) | Summary: This paper proposes a dynamic prompt learning approach for image editing that modifies self-attentions to more accurately attend to the correct nouns given a text prompt. The proposed approach is used along with null text inversion where the dynamic tokens corresponding with the noun words are updated with a b... | Rebuttal 1:
Rebuttal: We appreciate your feedback and will use the discussion to improve. Below we use the references in the main paper.
$\textbf{W1}:$ Actually DPL also works for Word-Swap with less related concepts. As we have shown in Fig.7, Fig.13, Fig.17, we successfully edit the bird into airplane/helicopter, th... | Summary: The paper proposes a new method to improve the attention masking for attention-based local editing.
Strengths: The proposed loss functions are novel, intuitive and effectively improves the inversion process of text-to-image diffusion model.
Weaknesses: 1. Please show the quantitative evaluation of the object... | Rebuttal 1:
Rebuttal: We appreciate your feedback and will incorporate the discussions mentioned below to enhance the quality of our paper. Note that we utilize the numerical references to cite sources within the main paper.
$\textbf{W1}:$ As requested, we performed an evaluation of the binary masks obtained from the ... | Summary: The authors first point out that inferior cross-attention maps regarding noun text tokens are the main causes of failures cases in prompt-based editing methods, such as prompt-to-prompt (P2P).
To tackle this, they propose to optimize noun text features with three objectives at each denoising timestep;
i) minim... | Rebuttal 1:
Rebuttal: We appreciate your feedback and will incorporate the discussions mentioned below to enhance the quality of our paper. Note that we utilize the numerical references to cite sources within the main paper and any references not included in the main paper are provided in a list at the end of this resp... | Summary: The paper propose DPL to solve the cross-attention background and distractor object leakage problem in
image editing using text-to-image diffusion models. The presentation is well written and easy to follow. The discussion and analysis is extensive and interesting. But the experiment dataset is too small to cl... | Rebuttal 1:
Rebuttal: We appreciate your feedback and will incorporate the discussions mentioned below to enhance the quality of our paper. Note that we utilize the numerical references to cite sources within the main paper.
$\textbf{W1}:$ For the creation of our multi-object real-image dataset, we faced the challenge... | Rebuttal 1:
Rebuttal: In the author rebuttal PDF file, we include three additional figures for the reviewer's reference:
$\textbf{(1)}$ Fig.17: extended comparison with DiffEdit and Imagic for image editing;
$\textbf{(2)}$ Fig.18: ablation study of the Gradual Optimization for Token Updates;
$\textbf{(3)}$ Fig.19... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes Dynamic Prompt Learning (DPL) to address the cross-attention leakage issue for text-based image editing. Based on the observation that inaccurate cross-attention maps cause unintended modifications of regions outside of the targeted area for text-based image editing, the authors propose Dyn... | Rebuttal 1:
Rebuttal: We appreciate your feedback and will incorporate the discussions mentioned below to enhance the quality of our paper. Note that we utilize the numerical references to cite sources within the main paper.
$\textbf{W1}:$ This study primarily addresses cross-attention leakage issues in the realm of r... | Summary: This paper works on the fidelity problem (i.e., 'unintended changes of background and distractor objects') in text-based image editing of text-to-image diffusion models. The authors attribute the fidelity problem to cross-attention leakage and propose dynamic prompt learning to force cross-attention maps to fo... | Rebuttal 1:
Rebuttal: We appreciate your feedback and will integrate the discussions to improve. We use citations for references in the main paper and any omitted references are listed below.
$\textbf{W1.1}:$ Imposing strict DPL indeed limits the editing region, which aligns with practical scenarios. Our goal is to po... | null | null | null | null |
Uni3DETR: Unified 3D Detection Transformer | Accept (poster) | Summary: The authors explore and analyze the existing LiDAR-based 3D object detection framework, and propose to adopt transformer-based method to perform detection from differnt LiDAR inputs. The experimental results on a number of datasets are better compared to the existing methods.
Strengths: 1. The task of 3D obje... | Rebuttal 1:
Rebuttal: **Q1: It would be much convincing if the authors can compare with this SOTA (PV-RCNN++)**
A1: To compare with PV-RCNN++, we follow the same setting to train our model on the training and validation set of KITTI, and evaluate on the KITTI test set. The comparison is listed in the below Tab. 5-1. O... | Summary: The authors propose a unified detr-style detector for both the indoor and outdoor 3D object detection. Besides common learnable query, they also adopt unlearnanle query sampled from raw point cloud.
Strengths: The proposed method is simple and easy to follow.
Weaknesses: The novelty is limted, and the exper... | Rebuttal 1:
Rebuttal: **Q1: Why not use a dense detector like pointpillar or centerpoints instead?**
A1:
* The main reason is that these dense detectors are usually based on the 3D convolution structure and 3D anchors. They perform 3D box generation directly on the extracted features, thus are sensitive to the distinc... | Summary: Uni3DETR is a unified 3D object detector that is capable of handling both indoor and outdoor scenes within the same framework. This is significant as many existing detectors are specialized for either indoor or outdoor environments, but not both.
The method employs a detection transformer with a point-voxel i... | Rebuttal 1:
Rebuttal: **Q1: It essentially resembles a simple combination of two model architectures.**
A1:
* Our main contribution is that we propose a unified architecture to address both indoor and outdoor 3D detection within the same framework. Unlike previous methods, which consider indoor and outdoor 3D detectio... | Summary: The paper proposes Uni3DETR, a unified architecture suitable for indoor and outdoor scenes. Uni3DETR employs the DETR with point-voxel interaction for object prediction. It uses a mixture of query points to exploit global and local information. Finally, Uni3DETR uses a decoupled IoU loss by disentangling the d... | Rebuttal 1:
Rebuttal: **Q1. The claim that the architecture is uniform is partially correct.**
A1:
* The word “unified” in our paper specifically refers to the architecture aspect. The voxel size is a data-related parameter, not architecture-related. Since point clouds are collected with different sensors, their range... | Rebuttal 1:
Rebuttal: Dear all reviewers
We sincerely thanks for valuable comments and suggestions. We first address the common concerns, followed by detailed responses to each reviewer separately. We hope our responses clarify existing concerns and make these points clear.
**Q: Comparison of computational complexit... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes Uni3DETR, a unified 3D detection transformer that addresses indoor and outdoor 3D detection within the same framework. The paper provides a specific analysis of the inconsistency in the structure of current indoor and outdoor scene detection models. Due to the differences in data distributi... | Rebuttal 1:
Rebuttal: **Q1: The experiments of model architectures are a bit insufficient.**
A1:
* We conduct the experiments on the SUN RGB-D dataset in the below Tab. 1-1. Dense 3D convolutions contribute to the 6.3% AP25 improvement and 10.1% AP50 improvement. Its effectiveness to the performance can be demonstrate... | null | null | null | null | null | null |
Variational Inference with Gaussian Score Matching | Accept (poster) | Summary: The submission proposes a method for black-box variational inference based that is based on score matching. The method follows an iterative procedure, where at every iteration the variational approximation is updated by first obtaining a single sample and then updating the approximation such that the gradient ... | Rebuttal 1:
Rebuttal: >It would be important to better analyze, if there are any mild conditions for convergence, and which criteria the learned approximation fulfills.
We agree with the referee that its important, but challenging to analyze given that GSM does not explicitly minimize any divergence. Empirically, we ... | Summary: A new variational inference (VI) framework is presented by matching the score of the variational distribution, q, with the target posterior p. Specifically, the closed form score matching equations for the Gaussian variational family are derived and the resulting method is named Gaussian Score Matching VI (GSM... | Rebuttal 1:
Rebuttal: > I don't concur with the use of the term "closed-form updates" of line 229 as GSM-VI
By closed-form updates, we refer only to the fact that (4) has a closed form solution for the Gaussian variational family for a generated sample. That is, the explicit update equations are given in equations (5)... | Summary: This paper proposes a novel alternative optimization strategy for approximate Bayesian inference in statistical modeling.
The starting point of the proposed score-based VI approach is the realization that two distributions are the same if their derivative is the same almost everywhere.
This principled is used... | Rebuttal 1:
Rebuttal: > The paper is very well written. However, I think that a bit more intuition on the algorithm with Eq 4 as objective would be ideal. At the moment, it seems a bit disconnected from Eq 3 - why would you want to minimally adjust q under that score-mathing constraint?
We can certainly add some more... | Summary: The paper proposes score matching as a new approach to (black box) variational inference (BBVI) where the variational family is Gaussian.
The usual way is to minimize the KL divergence (or equivalently to maximize the ELBO) using stochastic gradient descent (SGD) to update the variational parameters.
Instead, ... | Rebuttal 1:
Rebuttal: > I will update my rating if the author's answers address my concerns.
We thank the referee for their thoughtful reviews and kind consideration. Below, we address the weakness and questions raised above and will include this discussion in the revised manuscript.
> Why do you think GSM-VI is s... | Rebuttal 1:
Rebuttal: We thank all the referee for their thoughtful reviews. Based on comments from different reviewers, we have now compiled three new figures from some old, and one entirely new experiment. Please see the attached pdf. These serve to answer some of the questions raised below and so we begin by discuss... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training | Accept (poster) | Summary: This manuscript propose new notions of inconsistency, instability, and information-theoretifc instability based on the output confidence score to estimate the generalization gap of deep neural networks. Theoretical and empirical results are presented and show that the proposed notions, especially inconsistency... | Rebuttal 1:
Rebuttal: > The Inconsistency and Instability are very similar to the definition of disagreement, while the former notions replace the outout from one-hot predictions to softmax confidence score.
We find it interesting that in spite of the similarity in the definitions, they behave quite differently, as sh... | Summary: The paper presents two measures for a stochastic training algorithm: inconsistency and instability. The former measures the inconsistency (or "disagreement") within the random ensemble of models trained from the same training set. The latter measures the inconsistency of two ensembled predictors, each obtained... | Rebuttal 1:
Rebuttal: > Are the notions of inconsistency and instability related to the notion of functional-CMI (or functional MI) in the work of Harutyunyan et al, "Information-theoretic generalization bounds for black-box learning algorithms", NeurIPS 2021? Exploring this connection might enhance this work.
It seem... | Summary: This paper investigates the generalization gap in deep neural networks, and propose that this gap is influenced by the inconsistency and instability of model outputs, two quantities which are defined by the authors, and justified theoretically via a new information theoretic generalization bound. The authors c... | Rebuttal 1:
Rebuttal: Regarding theoretical novelty: in our view, our analysis is quite different from Xu & Raginsky (2017) as their bound is a sub-Gaussian bound which cannot lead to a faster rate than $\sqrt{1/n}$ as long as the noise variance is nonzero. In comparison we have a Bernstein-style bound, which is needed... | Summary: In this work, the authors introduce the ideas of “instability” and “inconsistency” of model outputs, and investigate the relationship between these quantities and the generalization gap. In particular, they empirically find a positive correlation between instability + inconsistency and the generalization gap. ... | Rebuttal 1:
Rebuttal: > In practice, practitioners may use a large learning rate and small batch size to obtain well-generalizing models (and so the final randomness would be high in this situation). Thus, ${\mathcal D_P}$ may not be useful here.
Even when the initial learning rate is large, the final randomness can b... | Rebuttal 1:
Rebuttal: Thank you very much for the valuable feedback.
In response to the suggestions, we conducted additional correlation analyses using the metrics from [Jiang et al. 2020], with generalization gap (loss difference), test error, and test error minus training error. One of the metrics from [Jiang et ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Smoothed Analysis of Sequential Probability Assignment | Accept (spotlight) | Summary: This paper focuses on the contextual sequential probability assignments, and specifically examines cases where the contexts $x_{1:T}$ are generated by $\sigma$-smooth adversaries as introduced in [Haghtalab et al. 2021], and where the labels $y_{1:T}$ are adversarially generated. The primary findings pertain t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review and the careful reading of our paper. We will fix the typographical errors and omissions.
**Choice of parameters**: Thanks a lot for noticing this. Upon further inspection our proof gives the desired bound with the choice $\epsilon = \sigma / T \lo... | Summary: The paper studies the sequential probability assignment problem with a smoothed adversary. The learner sequentially assigns a probability given contexts, which the adversary generates from a distribution that can be far from a known base distribution by a factor of $1/\sigma$. For the problem with the i.i.d. s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. We will incorporate corrections to the typographical errors in a revision.
**Differences between Haghtalab et al 21,22 and our work**: The main focus of the work by Haghtalab et al 21, 22 was on the case of the binary loss. Haghtalab et al 21 foc... | Summary: The paper considers the sequential probability assignment problem, which is the following:
The algorithm (forecaster), based on past context and outcomes, must assign probabilities to 0-1 values for the next outcome given the latest context. The forecaster competes against a reference class of predictor funct... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review.
**Regarding the concurrent work by Wu et al**: As the reviewer pointed out that both papers are concurrent and independent works. We agree that at first sight the ideas in the statistical aspect of our paper are related to the ones in Wu et al 23 and are b... | Summary: The paper discusses smoothed analysis of probability assignments in an online setting. The paper shows how the problem can be reduced to a transductive setting and obtains an upper bound on the regret using covering numbers which is further bounded by the scale sensitive VC dimension. The results are instantia... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments.
**Regarding the concurrent work by Wu et al**: As the reviewer pointed out that both the papers were concurrent and independent works. We agree that the ideas in the statistical aspect of our paper are related to the ones in Wu et al 2023 and are... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the problem of sequential probability assignment in the smoothed setting. In particular, a learner receives labelled examples sequentially, where the contexts are drawn from some smooth distribution which can otherwise be chosen adversarially (in each step) and the labels can be chosen adver... | Rebuttal 1:
Rebuttal: We thank the reviewer for a thoughtful review. We will incorporate the suggested typographical and expository corrections.
**Applicability of oracle-efficiency**: The oracle-efficient framework is important because it allows us to directly tap into existing deployed algorithms, without having t... | null | null | null | null | null | null |
Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models | Accept (spotlight) | Summary: This paper proposed a new two-stage method for statistical downscaling by combining a coarse de-biasing step based on optimal transport and a conditional up-sampling step based on a diffusion model.
Strengths: Overall, the paper is very well written and clearly explains the proposed method. The proposed meth... | Rebuttal 1:
Rebuttal: Thank you for your positive review. Please find our response below.
> Because the diffusion model is very dependent on the debiased result, this debiasing step is crucial for the success of accurate downscaling in my opinion.
Thank you for your sharp observation that the debiased results from OT... | Summary: The authors proposed a two-stage probabilistic framework for unpaired data. The problem is factorized into two steps, an optimal transport (OT) based mapping for debiasing and a diffusion-based model for up-sampling. The problem is demonstrated on fluid mechanics datasets representing difficult fluid and weath... | Rebuttal 1:
Rebuttal: We greatly appreciate your positive feedback. Please find our response below.
> What is the Reynolds number of the NS equations?
The Reynolds number is 1000. The (high-fidelity) simulation setup is identical to [1].
> Are there any difficulties applying it to real-world turbulence dataset?
Con... | Summary: The authors suggest a simple approach for the problem of statistical downsampling, which is the super-resolution of low-resolution weather grids. The approach involves first "debias-ing" the low-resolution grid via solving an optimal transport problem, then obtaining a high resolution image by solving an image... | Rebuttal 1:
Rebuttal: Thank you for your detailed review, especially your comments on places we could have explained better.
**Problem Setting, Motivation and Validity**
The problem we study in this paper is analogous to image or video super-resolution on a high level, but it has several important distinctions. We ap... | Summary: This work introduces a new framework to tackle statistical downscaling, a climate science equivalent of super-resolution, in two steps: The first step removes the bias while staying at low resolution with an optimal transport method and the second step increases the spatial resolution with a diffusion-based mo... | Rebuttal 1:
Rebuttal: We greatly appreciate your detailed review. Please see below the responses to the issues raised.
> Use common metrics in statistical downscaling such as CRPS, Motivate the use of the metrics listed in the paper
Thank you for the comment. We will provide a more thorough explanation for the evalua... | Rebuttal 1:
Rebuttal: **General Rebuttal Response**
We thank all reviewers for providing such detailed reviews. We are encouraged by the comments that the current idea has novelty, presented in a clear way, and that the results have potentially high (societal) impact and relevance.
To address the weaknesses and quest... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Global Optimality in Bivariate Gradient-based DAG Learning | Accept (poster) | Summary: The authors give a simple optimization algorithm for DAG-learning-inspired optimization problems that avoids the limitations of known techniques.
Strengths: Originality:
Work is original.
I particularly liked the reduction from a combinatorial problem to a non-convex optimization one.
Quality:
Simple and str... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to the reviewer for acknowledging the value of our contributions and our clear presentation.
> Line 49: Can you please explain Equation (2) a bit more?
>
Equation (2) is a penalized version of Equation (1), where $h(W(\Theta))$ acts as a penalty. This chan... | Summary: This paper studies the problem of learning the correct Directed Acyclic Graph (DAG) that describes the data using continuous optimization. The connection of this problem with continuous methods comes from a prior work of Zheng et al., where they introduce a differentiable function $h$ whose level set at 0 exac... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort into carefully reviewing our paper and provide such valuable feedback.
> The result is only proven for $d = 2$ nodes in the DAG. This makes most calculations tractable, since there is a closed form for the loss (a quadratic polynomial in two variabl... | Summary: This paper presents a novel approach to the non-convex optimization problems associated with learning the structure of a structural equation model (SEM) or Bayesian network. Considering the equivalent penalty form, the authors propose a homotopy-based optimization scheme that finds global minimizers of the pro... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful critiques and comprehensive understanding of our work, and for providing such useful feedback. We will try our best to address the reviewer’s concern.
> The authors' approach is primarily focused on the bivariate case, which may limit its applica... | Summary: the paper provides a theoretical study on the loss landscape and convergence in gradient-based DAG learning framework. By focusing on linear functions with the number of variable d = 2, They provide a homotopy-based optimization scheme to guarantee the global optimality. Some numerical validations are provided... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to the reviewer for their time, effort, and valuable suggestions.
> No discussions on how nonlinear or a large number of variables would affect the loss landscape, and/or how the homotopy algorithm would be affected.
>
Thanks for this insightful question! ... | Rebuttal 1:
Rebuttal: To all reviewers,
We thank all reviewers for their time put into reading our work and their valuable comments. We appreciate the consensus that our paper is well-written and theoretical contributions are delivered clearly. Finally, we appreciate Reviewer Y11K acknowledging our analysis elegant an... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Fast Bellman Updates for Wasserstein Distributionally Robust MDPs | Accept (poster) | Summary: * tailored algorithms for solving Wasserstein distributionally robust MDPs and fast implementations with $L_1$, $L_2$, $L_\infty$-based Wasserstein distance.
Strengths: * Fastest algorithms (in terms of dependency on N, S, and A - the number of kernels/states/actions) for solving Wasserstein distributionally ... | Rebuttal 1:
Rebuttal: Thank you very much for your positive comments and for taking the time to read our manuscript!
**Weaknesses**
1. This is a rather niche application of principles that have been developed in several other papers for robust MDPs.
In the same spirit of using first order methods for solving both th... | Summary: - The paper proposes a computationally efficient solution framework to solve the distributionally robust Bellman operator induced by Wasserstein ambiguity sets, which is critical in performing distributionally robust value iteration algorithms.
- The proposed framework features a novel decomposition of the op... | Rebuttal 1:
Rebuttal: Thank you very much for your positive comments and your time to review our paper!
**Weaknesses**
1. The framework seems to rely on the specific structure of the Wasserstein ambiguity set with the specific reference distribution of the Wasserstein ball, which makes the application of the framewor... | Summary: The paper studies Wasserstein Distributionally robust MDPs (WDRMDP) problem when the ambiguity set is defined based on Wasserstein distance and rectangular. It is then well known that the optimal policy can be computed by solving Bellman equations, which have the form of distributionally robust linear programs... | Rebuttal 1:
Rebuttal: Thanks a lot for your encouraging comments and your time to review our paper!
**Weaknesses**
1. The WDRMDP problem itself is not novel...well-studied distributionally robust linear program extensively explored in the fields of optimization and mathematical programming...Propositions 4.1 and 4.3,... | Summary: The paper focuses on the computational complexity of Wasserstein Distributionally Robust MDPs with Lp norm. By decomposing the calculation of Bellman updates to smaller subproblems, the algorithm can achieve linear complexity in the number of actions and kernels, and quasi-linear complexity in the number of st... | Rebuttal 1:
Rebuttal: Thank you very much for your comments and your time to review our paper!
**Weaknesses**
1. The experiments are carried out on very simple, randomly generated distributionally robust MDP. While this approach provides a controlled setting for their work, it risks oversimplifying the problem and li... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
GeoTMI: Predicting Quantum Chemical Property with Easy-to-Obtain Geometry via Positional Denoising | Accept (poster) | Summary: Edit: Updating score from 5 to 6 based on the discussions.
This work presents a variation of denoising autoencoder type model that uses an easy-to-obtain (corrupted) input geometry to predict properties of molecules. The assumption is that the corrupted geometry can be denoised to the correct geometry, and th... | Rebuttal 1:
Rebuttal: **[Weakness 1 and 3] “Relation between $X$, $\tilde{X}$, and $Y$” and “Selecting $\tilde{X}$”**
Please refer to the second response in “Response to all reviewers”.
---
**[Weakness 1 and 2] Distinction of GeoTMI from "the supervised task of predicting $Y$ from $\tilde{X}$ with an auxiliary task... | Summary: This paper proposes a novel training framework called GeoTMI. This framework uses a denoising process to accurately predict quantum chemical properties for molecules using MMFF geometries that are much easier to obtain than DFT-optimized geometries.
Strengths: 1. The proposed method is interesting, and the d... | Rebuttal 1:
Rebuttal: **[Weakness 1] Although Table 3 has shown that “Equiformer + Noisy Nodes + GeoTMI” achieves better performance than “Equiformer + Noisy Nodes”, the direct comparison between Noisy Nodes and GeoTMI is missing. It would be better if a direct comparison with other denoising-based methods is included.... | Summary: The authors propose a novel method to help solve the problem of 3D positional noise in quantum chemical properties. The proposed method is like a plug-in for other 3D GNN methods to improve their performance on defective 3D positional data. The numerical results show that the model can help the GNN models to p... | Rebuttal 1:
Rebuttal: **[Weakness 1] From QM9 results, I wonder whether the GeoTMI will be still useful in more powerful molecular property prediction models.**
We appreciate your concern about its effectiveness on more powerful models. It is important to assess GeoTMI's performance on state-of-the-art models to bette... | Summary: The paper proposes an effective framework, GeoTMI, to train 3D GNNs for quantum property prediction. Specifically, GeoTMI involves the denoising process during the learning of property prediction tasks by maximizing a three-term mutual information among the noisy representation, original representation, and pr... | Rebuttal 1:
Rebuttal: **[Weakness 1] Statement that denoising works focusing on prediction from X is not very true.**
We acknowledge the point that not all denoising approaches may exclusively focus on predicting $X$. However, in the context of predicting quantum chemical properties, we intended to highlight the preva... | Rebuttal 1:
Rebuttal: $\Large{\text{Response to all reviewers}}$
We extend our sincere appreciation to the reviewers for your invaluable insights and constructive feedback, which have significantly enhanced the quality and rigor of our manuscript. Your feedback will undoubtedly contribute to the refinement of our res... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Computational Guarantees for Doubly Entropic Wasserstein Barycenters | Accept (poster) | Summary: The paper presents an algorithm (damped Sinkhorn) and theoretical convergence guarantees for computing doubly regularized Wasserstein barycenters. The concept of doubly entropic Wasserstein barycenters extends the single entropic regularized barycenters by introducing an additional level of regularization. Thi... | Rebuttal 1:
Rebuttal: Thank you for your review and for spotting some typos.
- The LSI constant is indeed large, which renders the computational speed exponential in $R/\tau$, where $R$ is the radius of the domain. This is, however, unavoidable because Wasserstein barycenters are NP-hard to compute for discrete point ... | Summary: -----
EDIT : 6 --> 8
-----
This paper builds on [15] and considers the recently introduced model of _doubly regularized entropic Wasserstein Barycenters_ which, given a set of measures $\nu^1,\dots, \nu^k$, weights $(w_j)_j$ (non-negative and sum to $1$), a reference measure $\pi$, and two smoothing paramete... | Rebuttal 1:
Rebuttal: Thank you for such a thorough review and for your very helpful suggestions.
## Answers to Main Questions
### Question 1: Computational Price of Damping
*Is there a computational price to pay for the damped Sinkhorn?*
**Answer:** Actually, in the numerical experiments attached to the main respo... | Summary: The paper has proposed a computational algorithm for computing the newly developed regularized Wasserstein barycenters in [Chizat,2023] via optimizing the duality of the primal problem. The paper also characterised the convergence of algorithms on both exact and approximated algorithms which are the main contr... | Rebuttal 1:
Rebuttal: Thank you for the review.
The theoretical aspects of doubly entropic barycenters have been thoroughly investigated in https://arxiv.org/pdf/2303.11844.pdf. Our primary goal was, instead, to provide new numerical schemes for their computation and to establish their convergence, particularly coveri... | Summary: This paper proposes an algorithm for solving the doubly regularized Wasserstein barycenter problem for probability measures that corresponds to adding an inner regularization based on the entropy penalty appearing in the Wasserstein distance term, and an outer regularization appearing at the level of the Wasse... | Rebuttal 1:
Rebuttal: Thank you for your comments and questions.
- Regarding the compactness assumption, it is really only needed in our case because the prior work that introduced doubly entropic barycenters derived many theoretical results in the compact case. In particular, compactness was used in that context to j... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their feedback. We will answer minor questions raised by the reviewers individually; in this shared response to all reviewers, we will focus on the numerical simulations aspect.
Most of the reviewers pointed out the absence of numerical simulations as a significant ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a study on the computation of doubly regularized Wasserstein barycenters, a recently introduced family of entropic barycenters with inner and outer regularization strengths. The authors build upon previous research, which has shown that different choices of regularization parameters unify v... | Rebuttal 1:
Rebuttal: Thank you for your comments and questions. We respond to your questions below:
1. Let us explain why the Approximate Sinkhorn Oracle (Definition 1) is defined the way it is. First, why do we need an approximate algorithm at all? We need it because, for continuous measures, we typically cannot imp... | null | null | null | null | null | null |
PAC Learning Linear Thresholds from Label Proportions | Accept (spotlight) | Summary: Learning from label proportions allow training data to aggregate into sets of feature vectors with sum or average of their labels for each set as a label. As supervised learning, the goal is to classify test set of instances and minimize error of the classifier. This work focuses on learnability of LLP over Ga... | Rebuttal 1:
Rebuttal: Q. *For Theorem 1.3 and Theorem 1.5, it is better to provide one or two sentences for each about proofs instead of putting everything in the appendix. You dont have to provide a separate section like Theorem 1.4. Just few sentences please.*
*Authors*: While we have included in Section 1.4 an ove... | Summary: This theoretical paper investigates the learnability of linear threshold functions (LTFs) in the learning from label proportions (LLP) setting. When the feature-vectors are distributed according to a Gaussian distribution and conditioned on their underlying labels, LTFs can be efficiently properly learnt. For ... | Rebuttal 1:
Rebuttal: Q. *From Theorem 1.3 to 1.5, as the distribution becomes more general, the sample complexity required to efficiently properly learn LTFs also increases, and the difference between these sample complexities seems to be obvious. Numerical results on the difference between these sample complexities a... | Summary: This work studies the problem of learning linear threshold functions (LTFs, aka linear classifiers) under the setting of learning from label proportions (LLP), where the training data are "bags" (aka sets) of instances, and the training labels are classifier proportions of the instances in the bag. Under the a... | Rebuttal 1:
Rebuttal: Q. *After Section 1.1, it will be useful for the reader if there is an overview of the paper. Something along the lines of "Sec 1.3 are the main results. Sec 1.4 gives proof sketch of the main results and high level description of the algorithms. Section 3 state these algorithms precisely..."*
*... | Summary: This paper studies PAC learning when the training data is aggregated into sets or bags of feature vectors. For each bag, we observe the feature vectors of these bags and only the average of the labels in the bag. They focus on the case when the feature vectors are distributed according to a Gaussian distributi... | Rebuttal 1:
Rebuttal: Q. *For example, in the proof of Theorem 1.4 or Lemma 4.2, too much emphasis is given in the calculation of the optimal setting of parameters and sample complexity. I would prefer a more intuitive explanation at times..*
*Authors*: We state our results formally in Section 1.3 and therefore inclu... | Rebuttal 1:
Rebuttal: We thank the Reviewers for their encouraging and helpful feedback. We have addressed their questions and comments in the respective author rebuttals to the reviews. | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Strong and Precise Modulation of Human Percepts via Robustified ANNs | Accept (poster) | Summary: The paper presents a novel approach to find categorical perceptual changes in humans using artificial neural networks (ANNs). Notably, the paper presents compelling evidence that an adversarially trained resnet50 is better at generating these adversarial attacks on humans on a low budget pixel regime. To test... | Rebuttal 1:
Rebuttal: **Errors/typos & paper organization**
We thank the reviewer for spotting those important errors/typos, now all corrected. As far as organization is concerned, and in accord with the reviewer’s suggestion, we designed the “Overview of approach and experiments” section to provide a succinct and hig... | Summary: I have read the rebuttal and will keep my (relatively high!) score as is.
It is a folk-theorem that human categorization behavior is robust to adversarial perturbations that are under an L2 norm of 30 or less. This paper shows that networks that have received adversarial training can be used to generate relat... | Rebuttal 1:
Rebuttal: We thank the reviewer for supporting our work.
**Terminology complaint**
We agree that “adversarial image” is not well defined in the field and that the work we presented exposes the need for a clear definition of this phrase. Our working definition – which is inline with the original methodolo... | Summary: The paper systematically challenges the common assumption that human categorization of images remains highly robust to small-scale image perturbations (low pixel budget). The authors find that small-scale image perturbations, guided by adversarially trained artificial neural networks (robustified ANNs), can si... | Rebuttal 1:
Rebuttal: **The paper revolves around the premise that images generated by robustified ANNs (under a low pixel budget) can interfere with human classification judgments. This conclusion doesn't seem surprising. Humans often misjudge many images; it's just that robustified ANNs can generate highly deceptive ... | Summary: The paper under review brings to light a crucial and fascinating issue in deep learning: adversarial attacks. The authors contend that a neural network trained adversarially, which is logically more robust against attacks, produces perturbed images that humans perceive as differing from the original when it is... | Rebuttal 1:
Rebuttal: We thank the reviewer for reviewing our work, for their positive comments about our behavioral experimental methods, and for their critical analysis of our empirical findings. We agree that the novelty and significance of our findings are dependent on 1) the prior beliefs in the field on perceptua... | Rebuttal 1:
Rebuttal: We thank the reviewers for finding our work interesting and for your support. We appreciate your constructive feedback and we agree that those suggestions would clarify the contributions of this work.
**Interpreting the low-pixel budget regime (< 30)**
All reviewers asked for clarification about... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the problem of adversarial images in artificial and biological visual systems. On standard ("vanilla") image models (e.g. ResNet-50), adversarial perturbations that successfully disrupt a vanilla model are quite small and do not significantly alter human perception. This paper generates adve... | Rebuttal 1:
Rebuttal: **Interpretation of the low budget regime**
We thank the reviewer for this feedback. Please refer to our global response on this point.
**I am curious if the authors looked at similarities or differences in the representations across the four trained networks. If one expects the robustified netw... | null | null | null | null | null | null |
Diffusion Model for Graph Inverse Problems: Towards Effective Source Localization on Complex Networks | Accept (poster) | Summary: This work discusses the challenges associated with tracing the origin and path of information diffusion in complex networks, such as those involved in epidemics or rumors. To address these, the authors propose a probabilistic model, DDMSL (Discrete Diffusion Model for Source Localization), which utilizes Marko... | Rebuttal 1:
Rebuttal:
Thank you for your valuable suggestion. We have incorporated the time complexity analysis of the DDMSL algorithm and will provide comprehensive answers to all your inquiries.
(1)"The motivation for recovering the diffusion path"
Thank you for your question. In our perspective, reconstructing co... | Summary: This paper proposes a discrete denoising diffusion model for source localization called DDMSL. DDMSL can simultaneously locate information sources and restore information propagation paths. Experiments results on real-world datasets demonstrate DDMSL’s effectiveness.
Strengths: 1. This is the first study to s... | Rebuttal 1:
Rebuttal: Thank you sincerely for your valuable suggestion. Taking into account your feedback, we have incorporated a comprehensive time complexity evaluation for DDMSL and will ensure that all your questions are answered thoroughly.
(1) "What are the motivations and advantages of using diffusion models to... | Summary: Information diffusion is common in various domains, such as social networks, the internet, and disease propagation. Obtaining the diffusion paths and localizing the source based on the final diffusion node states is beneficial for researchers to identify key transmission pathways during information disseminati... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. In response to your input, we have conducted experiments utilizing real-world diffusion datasets and DDMSL generalization experiments. The forthcoming section will provide comprehensive responses to each of your inquiries.
(1)"Why the proposed method utilizes... | Summary: This paper addresses the problem of source identification in a stochastic
network diffusion process, having observed the set of nodes that are
infected at time T. It considers SIR and SI models. The authors propose a
neural network based solution called DDMSL. The authors approach is based
on a reaction-diffus... | Rebuttal 1:
Rebuttal: Thank you very much for your comments and suggestions. We have provided some explanations and clarification regarding your concerns as follows:
(1) “Many notation and crucial concepts are missing”.
Response: Thank you very much for your comments and suggestions. The neural network model u... | Rebuttal 1:
Rebuttal: I would like to extend my sincere appreciation to the esteemed reviewers for their invaluable suggestions on our paper. It has come to our attention that several reviewers have expressed interest in understanding the rationale behind our adoption of the denoising diffusion model. We will address t... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective | Accept (spotlight) | Summary: The paper addresses the dataset condensation task and proposes a new framework termed Squeeze, Recover and Relabel. In this three step approach, the authors first train a model from scratch to accommodate most of the crucial information from the original dataset. In the second stage, target data is synthesized... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive comments. We are encouraged that you find our work clear and easy to understand, and provide an exhaustive in-depth empirical comparison with state-of-the-art methods. We would like to address the comments and questions below.
>Q1. Clarification on Cro... | Summary: This paper proposes a 3-step dataset condensation approach. Instead of applying bilevel optimization based approach in the previous work, the proposed method break down 3 decoupled steps: squeeze, recover and relabel. The key idea is decoupling the modeling training on real data and the generation of the synth... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive comments. We are appreciated that you find our work achieving superior performance with more visually appealing images. We would like to address the comments and questions below.
>W1. Strong technical novelty. The proposed method combines a few prior ... | Summary: This paper proposes a new dataset condensation termed Squeeze, Recover, and Relabel that decouples the bilevel optimization of model and synthetic data during training. Extensive experiments show the effectiveness and efficiency of the proposed method in several IPC settings.
Strengths: 1, The paper is well-w... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive comments. We are encouraged that you find our work well-written and easy to understand, the proposed method is efficient with extensive experiments. We would like to address the comments and questions below.
>W1. Albeit the computation and memory effi... | Summary: This paper proposes a dataset distillation or dataset condensation method that can support ImageNet-scale compression. The main idea is inspired by some data-free knowledge distillation techniques to optimize the cross-entropy error, BN statistic distance, and some other prior terms for the distilled data. The... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive comments. We are encouraged that you find our work simple yet effective, enjoying satisfactory scalability, and helping the community to know dataset distillation could achieve promising results on large-scale datasets. We would like to address the com... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We would like to express our gratitude for your insightful feedback and comments, which have been helpful in updating and enhancing our submission. We kindly invite you to review our author rebuttal so that we may address any further questions you may have or clarify any points th... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper introduces a new dataset condensation framework, that is Squeeze, Recover, and Relabel (SRe2L).
SRe2L decouples the optimization of model and synthetic data during training, enabling effective condensation across varying dataset scales, model architectures, and image resolutions.
The authors mentio... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive comments. We are encouraged that you find our work novel with surprising performance on scalability. We would like to address the comments and questions below.
>W1. Limited Theoretical Analysis: While the paper presents impressive empirical results and... | null | null | null | null | null | null |
Provable Guarantees for Neural Networks via Gradient Feature Learning | Accept (poster) | Summary: This paper proposes a general framework for analyzing feature learning in two-layer ReLU neural networks. The idea is to consider the class of two-layer ReLU networks with “gradient features”, i.e. features aligned with the gradients of the loss induced by the distributions of the data and initial model parame... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing thorough suggestions! For feature learning happening in the early steps of gradient descent, please refer to the global response above. Below we address the other comments. In short, we provide some failure cases that our framework cannot cover.
### More discu... | Summary: This paper proposes a general framework of feature learning for two-layer neural networks trained with gradient descent. This framework covers a variety of classification losses and data distributions. Specifically, the authors establish that the loss of neural networks trained with gradient descent is compara... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing thorough suggestions! For the theorem still operating in the regime of a few gradient steps for the first layer, please refer to the global response above. Below we address the other comments. In short, we can **improve** our results from $\tilde{O}(d^{1.5})$ to... | Summary: This paper introduces a general framework for studying feature learning in two-layer NNs. This framework covers feature learning in different examples such as linear classification, mixture of Gaussians and parity functions (it also gives some intuition about the learned features). The neural network under stu... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing thorough suggestions! For the limitation of the first gradient descent learning and Q2 General insights, please refer to the global response above. Below we address the other comments.
### Q1 Continous setting and square loss
It is possible to extend to a conti... | Summary: The paper defines the concept of "gradient features" which capture the features that the network can learn after one step of gradient descent. The paper then instantiates this framework to prove optimization and generalization guarantees for various statistical learning problems.
Strengths: - The paper develo... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing thorough suggestions! For the "one-step trick" question, please refer to the global response above. Below we address the other comments.
### More about multi-step feature learning
We would like to mention that the early-stage analysis (“one-step trick”) is an i... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive and valuable feedback.
We are glad that all reviewers unanimously agree that our theoretical analysis is novel, exciting, powerful, and significant. Reviewers find that our paper provides an easy-to-use, general, and unified framework (zxbj, H9VP, Sj2... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective | Accept (poster) | Summary: The paper proposes a normalization technique that works on sliced time-series with the main goal to remove non-stationary behavior of the inputs (and outputs). The paper computes mean and standard deviation of the slides inputs and then normalizes the inputs by them. Additionally, the paper proposes to estimat... | Rebuttal 1:
Rebuttal: Thank you for your acknowledgement and valuable feedbacks on our work, we would like to address your concerns as follows.
- In practice, **it is commonly assumed that time series data within a slice have the same distribution**. Existing normalization methods (such as DAIN and RevIN) also assume ... | Summary: The paper introduces a novel approach called Slicing Adaptive Normalization (SAN) for non-stationary time series forecasting. The proposed method addresses the challenge of accurate predictions in the presence of non-stationarity in real-world data. It overcomes limitations in existing normalization techniques... | Rebuttal 1:
Rebuttal: We greatly appreciate your recognition of our proposal's novelty and effectiveness. We would like to address your concerns as follows:
- Firstly, we conducted additional experiments using PatchTST and CrossFormer on 5 datasets. We built forecasting models using their official codes and hyper-para... | Summary: Non-stationary time series forecasting is a challenging problem, and recent research has focused on using normalization techniques to address non-stationarity. However, these methods have limitations when it comes to handling the distribution discrepancy between the input and the forecasted horizon. This dis... | Rebuttal 1:
Rebuttal: We greatly appreciate your acknowledgement of our proposal. We would like to address your concerns as follows:
- Weakness A: Thank you for your valuable advice on defining the problem. We have found it helpful to reference papers on CPD or OOD to better illustrate the limitations of existing norm... | Summary: The paper introduces a normalization approach for predicting non-stationary timeseries. While previous work on this topic assumes that output timeseries roughly share the same statistics (mean and variance) as the input timeseries and adjusts predictions accordingly, the authors propose two adjustments. Firstl... | Rebuttal 1:
Rebuttal: Thank you for your acknowledgement and valuable feedbacks on our work, we would like to address your concerns as follows.
- Firstly, the suggestion of conducting ablation study on setting slice number to 1 is of great value. Considering both the training efficiency and rebuttal space limitation, ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Spectral Theory of Neural Prediction and Alignment | Accept (spotlight) | Summary: This submission uses spectral theory and simulation experiments to try to compare and assess the representations of DNNs vs those of biological neural networks.
Strengths: The paper comes with a fairly extensive review of the literature.
The paper uses both theoretical ideas and simulations.
Weaknesses: The... | Rebuttal 1:
Rebuttal: We thank you for raising these issues and your comments. Please see below for the responses.
1. **Clarity of the results**
We have made several changes to the paper which we believe substantially address these issues. While we refer the reviewer to the global response for details, we note that ... | Summary: The paper uses (but doesn't introduce) a theoretical framework that relates generalization error to spectral bias of network activations to geometrically/spectrally analyze what aspects of pretrained deep networks contribute to the predictive performance of neural activity in layers V1, V2, V4 and IT. The auth... | Rebuttal 1:
Rebuttal: We thank you for your thorough review. Please see our responses below.
**Q1:**
Thank you for the suggestions regarding the clarity and presentation of the manuscript. We have made a number of changes that we hope address these concerns. These are detailed in the global response. We have also upd... | Summary: This study asks a crucial question: What is it about representations in ImageNet-trained neural networks that allow the successful regression of responses in mammalian visual cortex? The authors answer this question (in part) via an ingenious method combining learning theory and empirical analyses.
Even afte... | Rebuttal 1:
Rebuttal: We deeply thank you for reading our paper thoroughly, your positive thoughts and detailed comments/suggestions!
## Weaknesses:
1. **Clarity, density of results and writing:**
We thank the reviewer for the suggestion. To make it easier for the reader to get a high level summary of our resul... | Summary: Previous works have demonstrated that many different state-of-the-art deep neural networks (DNNs) perform similarly at neural responses prediction. But a complete understanding of which aspects of these DNNs lead to the similarity in predicting neural responses remains unknown. The authors proposes a spectral ... | Rebuttal 1:
Rebuttal: We thank you for your review and suggestions. Please see our responses below.
1. **Clarity issues and summary of results:**
We thank you for these suggestions and agree that the clarity should be improved. In response to this comment and others, we added a concise list outlining the principle co... | Rebuttal 1:
Rebuttal: # Global Response
We thank the reviewers for their helpful comments and for highlighting the significance of our work. As noted by one reviewer, “Even after a decade of study...it is unclear why the responses of neurons in the visual cortex can be so well predicted...by linearly regressing them f... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors studied linear regression-based DNN encoding models of macaque visual cortical areas by extending a recent generalization error theory. They provided a theoretical link between the predictivity and geometry of representations and showed that models with similar generalization errors may have quite ... | Rebuttal 1:
Rebuttal: We greatly appreciate your careful, extensive and constructive assessment of our work which led us to improve the clarity of our paper! Please also see global response and Rebuttal Figures (RFig).
**Tense**: Thank you for pointing this out. We edited our draft so that the tense is consistent in e... | Summary: In recent years, DNNs trained on image recognition tasks have emerged as strong predictive models of neural activity in the visual cortex. Typically, a regression model is trained to map DNN responses onto biological neural activity in response to the same inputs. The standard method for evaluating the predict... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review of our work. Please see the responses below.
## Weaknesses:
1. **Motivation for methodology:**
We are thankful for the suggestions and made changes to improve the clarity of our work.
We found that the radius and dimensionality o... | null | null | null | null |
Common Ground in Cooperative Communication | Accept (spotlight) | Summary: The authors identify the problem of common ground as the core challenge in cooperative communication, where common ground means having enough shared knowledge and understanding to successfully communicate. They argue that prior models of cooperative communication uniformly assume the strongest form of common g... | Rebuttal 1:
Rebuttal: Thank you for your detailed and comprehensive review. We have addressed questions and considerations common to all the reviewers in our global response. Here we will address the remainder of your review and considerations unique to it. If you feel something in your review has not been attended eit... | Summary: This paper models cooperative communication, particularly under imperfect knowledge sharing. The authors generalize the model of cooperative communication giving it a principled mathematical footing and elucidating the dynamics of communication by introducing insightful concepts like conditional teaching and l... | Rebuttal 1:
Rebuttal: Thank you for your detailed and comprehensive review. We have addressed questions and considerations common to all the reviewers in our global response. Here we will address the remainder of your review and considerations unique to it. If you feel something in your review has not been attended eit... | Summary: > This is an emergency review for the paper. Due to the limited time, the content is shorter than a normal review, and the mathematical details are not fully checked.
This paper considers the theory of two-party cooperative communication. Compared to prior models of cooperative communication, the proposed mod... | Rebuttal 1:
Rebuttal: Thank you for your detailed and comprehensive review. We have addressed questions and considerations common to all the reviewers in our global response. Here we will address the remainder of your review and considerations unique to it. If you feel something in your review has not been attended eit... | Summary: The paper highlights the core challenge of cooperative communication is establishing a common ground, which refers to the shared knowledge and understanding necessary for successful communication. Existing models of cooperative communication assume perfect and complete knowledge sharing, thereby overlooking th... | Rebuttal 1:
Rebuttal: Thank you for your detailed and comprehensive review. We have addressed questions and considerations common to all the reviewers in our global response. Here we will address the remainder of your review and considerations unique to it. If you feel something in your review has not been attended eit... | Rebuttal 1:
Rebuttal: We are grateful for the feedback and the effort invested in reviewing our work. We address specific considerations individually.
## Strengths (`quoted text`)
Foremost, `common ground is a very important concept`. Our work `offers a significant contribution`. Our paper's `strength lies in its abili... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Analyzing and Improving Greedy 2-Coordinate Updates For Equality-Constrained Optimization via Steepest Descent in the 1-Norm | Reject | Summary: The paper presents new update rules for block-coordinate descent (BCD) methods to minimize a smooth function subject to one linear equality constraint (precisely, all variables must sum to 1) and possibly a box constraint. A popular method to solve large-scale problems of this type is BCD with blocks of size 2... | Rebuttal 1:
Rebuttal: Thank you for pointing out these minor issues. We will fix them.
> "How does the GS-1 update behave in SVM training?"
Please see the discussion of experiments in the general response.
> "How would Figure 1 look like if the iterations continued to a very small (10^-6) error f(x)-f^*?"
Good ques... | Summary: The first goal of the paper is to minimize a smooth function subject to a summation constraint. The authors demonstrate that the greedy 2-coordinate descent (CD) method, when applied to the problem with equality constraints, achieves a linear rate of convergence under the proximal PL inequality under the L1-no... | Rebuttal 1:
Rebuttal: Thank you for carefully reading our paper; your comments will help us improve the manuscript. We respond to the highlighted weakness and your questions below.
> "The experimental part is limited"
Please see the discussion of experiments in the general reply.
> 1. "More comments are needed when ... | Summary: The paper studies new coordinate descent-type methods for equality-constrained problems, where 2 coordinates are updated on each iteration and proves new convergence guarantees under suitable proximal-PL conditions that allow to obtain linear convergence rates for the proposed methods.
The first main result c... | Rebuttal 1:
Rebuttal: Thank you for highlighting these strengths. We put in a lot of work to ultimately find what we believe is a simple and elegant analysis of this issue; our older drafts of the paper had much more complicated analyses while achieving slower rates. We comment on the highlighted weaknesses below.
> 1... | Summary: This work studies minimizing a smooth function with a summation equality constraint over its variables. The authors show a connection between the greedy 2-coordinate update and steepest descent w.r.t. 1-norm, and introduce a new proximal PL assumption w.r.t. 1-norm. They show improved convergence rates under s... | Rebuttal 1:
Rebuttal: Thank you for the suggestions on improving the plots.
> "It is unclear to me what function classes can satisfy the proximal-PL condition w.r.t. 1-norm where the worst case dependence on $n$ for $\mu_1$ can be avoided."
It is a good point that the paper currently does not give an example where $\... | Rebuttal 1:
Rebuttal: We thank the reviewers for taking the time to read the paper and provide feedback on our work. We believe that the paper will be strengthened by incorporating this input. Below we comment on two issues that were brought up in multiple reviews.
**Experiments on real data**
We view our primary con... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization | Accept (poster) | Summary: This article presents an efficient neural heuristic method based on meta-learning, referred to as EMNH (Efficient Meta-learning Neural Heuristic), for solving Multi-Objective Combinatorial Optimization Problems (MOCOPs). The authors employ a shared multi-task model to expedite the meta-learning process and int... | Rebuttal 1:
Title: Posted review for the wrong paper?
Comment: Hi Reviewer EY2k,
It looks like this review is for a different paper? Did you accidentally paste the wrong one in?
Thanks for checking!
---
Rebuttal 2:
Rebuttal: We appreciate the reviewer for the valuable comments, and finding our method novel, experim... | Summary: The paper proposes efficient meta neural heuristic (EMNH) for solving multi-objective combinatorial optimization problems (MOCOP). The paper provides novel scaled sampling method for stability and a hierarchical fine-tune method for sub-task specific performance improvement over MDRL. The idea is sound and the... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the valuable comments, and finding our experiments detailed and convincing. Our EMNH method, when provided with a sufficiently large value of $K$ (the number of fine-tuning steps), may not outperform traditional strong solvers like WS-LKH. We conducted a study to exa... | Summary: This work proposes EMNH, an efficient meta neural heuristic, for solving multi-objective combinatorial optimization problems. It builds a single meta model to tackle different trade-offs among multiple objectives during training, which can be efficiently fine-tuned into specialized submodels to solve different... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the valuable comments, and considering our paper well written with timely contribution and promising performance. We hope the point-to-point response below would address the remaining concerns.
**To Weakness 1: Runtime and Efficiency of Fine-tune.** The fine-tuning ... | Summary: The paper introduces a meta neural heuristic in which a meta model is first trained and then fine-tuned with a few steps
to solve corresponding single-objective subproblems. For the training process, a partial architecture-shared multi-task model is leveraged to achieve parallel learning for the meta model, so... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the valuable comments, and considering our method advanced and our paper well written. Regarding the source code, on the one hand, we have stated clearly in our original submission - ' Our codes for all the methods will be made available'. On the other hand, we will ... | Rebuttal 1:
Rebuttal: Many thanks for all reviewers' constructive and valuable comments. Following their suggestions, we have made the following main revisions:
1. **Motivation:** We have revised some descriptions to make the connection between our motivation and the proposed method clearer according to the comments o... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In order to achieve higher learning efficiency and better solution quality, this paper proposed an efficient meta neural heuristic (EMNH), in which a meta model is first trained and then fine-tuned with a few steps to solve corresponding single-objective subproblems. For the training process, an architecture-s... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the valuable comments, and considering our idea clear and logical. We hope the point-to-point response below would address the remaining concerns.
**To Weakness 1:** We acknowledge and appreciate the reviewer's concern. It is indeed crucial to establish a clear con... | null | null | null | null | null | null |
Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval | Accept (poster) | Summary: This paper presents a Laplace approximation-based probabilistic retrieval approach (aka. Bayesian metric learning for image retrieval). The author provides a probabilistic view of the contrastive loss based on the von-Mises Fisher distribution and corrections for the Hessian positive definiteness. Extensive ex... | Rebuttal 1:
Rebuttal: > It appears that this paper builds upon prior work, including the Laplacian autoencoder (Miani, M. et al., 2022), as well as several works on uncertainty in metric learning. Consequently, I am more concerned about the unique technical contributions of this work. In this regard, the necessity of t... | Summary:
The authors show that contrastive loss can be viewed as a likelihood after projection onto the spherical space. This consequently allows them to use the Laplace approximation to estimate the posterior over the parameters. To make the construction further amenable to estimation, the authors propose approaches ... | Rebuttal 1:
Rebuttal: > The overall method involves a fair number of moving parts, and it would be good to reconcile them as a single algorithm or a list of bullet points for easy digestion for the reader.
We thank the reviewer for the suggestions and will include the following snippets of pseudo-code in the paper.
``... | Summary: They propose a Bayesian encoder for metric learning. They learn a distribution over the network weights with the Laplace approximation. They first prove that the contrastive loss is a negative log-likelihood on the spherical space. They propose three methods that ensure a positive definitive covariance matrix.... | Rebuttal 1:
Rebuttal: > First the experimental results only achieved limited improvement.
**Strong UQ performance:** The reviewer is correct that the predictive performance does only improve slightly upon the baselines. However, we highlight that the OOD performance (the focus of the paper) across all 4 datasets impr... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their positive and constructive feedback. The reviewers found the problem considered “interesting” [R1] and stated that it is an “important topic for improving the robustness and mitigating the silent failure of deep neural network systems.” [R3] The paper is “well organ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement | Accept (poster) | Summary: The paper addresses conditional generation with reward-conditioned diffusion models. They propose to learn a reward function from a small subset of labeled data. The paper aims to answer an intriguing research question: "How can we reliably estimate the reward-conditioned distribution through diffusions and ba... | Rebuttal 1:
Rebuttal: >**Q1**. The paper lacks a comparison to other similar models, such as classifier-guided diffusion models.
**A1**. Alg 1 is not an alternative to classifier-guided diffusion. Instead, it is a simplification of it and also generalized to continuous reward and semi-supervised learning. Please refer... | Summary: In this work, authors explore the problem of reward-directed generation using conditional diffusion models in a semi-supervised learning setup. More specifically, they consider a dataset which has a small subset of it labeled with rewards and the majority of it unlabeled. Using the small labeled subset, they f... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments!
>**Q1**. “How to balance the reward signal and distribution-shift effect to have high-reward and high-quality samples?” Experimental setup is limited
**A1**. Our paper provides the first theory for conditional diffusion and use of reward-conditioned diffusio... | Summary: This paper presents an approach to generation using diffusion models augmented with a reward function. It does so by setting up a semi-supervised learning setup, where the reward function is learned from a small set of data. The reward is then used to learn a reward conditioned score function, which is subsequ... | Rebuttal 1:
Rebuttal: >**Q1**. The paper does not discuss a practical manner in identifying when the generative model starts deviating from the training distribution.
**A1**. Our focus is theory and implications are listed in “impact and novelty” in [our rebuttal](https://openreview.net/forum?id=58HwnnEdtF¬eId=npWK... | Summary: The paper addresses the problem of conditional generation with diffusion models in a self-supervised setting, where the conditional generation is guided by a learned regressor on the small labeled subset. This is referred to as reward-directed conditional diffusion. Assuming the inputs have a latent linear rep... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful and insightful review. We’ve revised our paper to clarify the notations and added more explanations around the score network according to your suggestions.
>**Q1**. What are the practical implications. I could have (qualitatively) predicted the results for text-to-ima... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for valuable comments!
**Q1 Impact and novelty of theory**
**A1:** Conditional diffusion models (CDM) have emerged as a powerful generative model with diverse applications from image generation to control and RL[1, 2, 3]. In sharp contrast to abundant em... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Language Models are Weak Learners | Accept (poster) | Summary: This paper explored an interesting problem that how to apply and extend LLMs over tabular supervised learning tasks. The paper first described each tabular sample as text, and then resorted to LLM to generate the summary for a set of selected representative samples as the template, which can be viewed as a wea... | Rebuttal 1:
Rebuttal: We are thankful for your positive review of our work! We are happy that you quote our experiments as extensive and as demonstrating the important aspects of our method. Please find our responses to your comments as follows.
> 1. The current method seems not easy to apply on high-dimensional tabul... | Summary: This paper explores the concept of weak learners, which are classifiers that achieve slightly better than random performance on any given data distribution. The paper demonstrates the effective utilization of large language models (LLMs) as weak learners. The study focuses on applying a large language model to... | Rebuttal 1:
Rebuttal: Thank you for your positive review of our manuscript! We appreciate your recognition of our work as novel and paving a way to utilize LLMs in boosting.
**Weaknesses:**
> 1. The paper focuses specifically on tabular data classification, which may restrict the generalizability of the proposed appr... | Summary: The paper investigates the use of large language models (LLMs) as weak learners in a boosting algorithm applied to tabular data. By providing text descriptions of tabular data samples, LLMs can generate a summary that acts as a template for classification, effectively serving as a weak learner. The authors inc... | Rebuttal 1:
Rebuttal: We are glad that you found our paper enjoyable to read and our writing lucid to follow. Thank you for your positive feedback of our work! Please find our responses to your review as follows.
**Weaknesses**
> 1. The integration of multiple weak learners using ensemble learning methods, each requi... | Summary: This paper demonstrates that prompt-based LLMs can be used as weak learners, with applications on boosting algorithms for tabular data. By providing text descriptions of tabular data samples, the authors show that LLMs can produce a summary of the samples and use it as a template for classification that can be... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions for our manuscript.
> 1. The paper can be significantly improved by providing a more intuitive description of the proposed approachs and by discussing the practical impact of the results in Section 4.
Thank you for your comments! We have brought out the in... | Rebuttal 1:
Rebuttal: A common point shared by many reviewers was to make fonts bold in the tables 1 & 2 for the best numbers in each row. We have added bolding to highlight the best-peforming results which can be viewed in the attached file.
We note that our method doesn't always show improvements over the state of ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a novel way to use large language models (LLMs) as weak learners in boosting frameworks for tabular data. The core idea is called LLM Summary Boosting, a novel method that prompts large language models (LLMs) to create weak learners for usages within a boosting framework to make predictions... | Rebuttal 1:
Rebuttal: Thank you for your positive views of our paper! We are glad that you found it well-written and easy to follow along with. Please find our responses to your review as follows.
**Weaknesses**
> 1. While I find the arguments and experiments quite comprehensive, I am personally on the fence about th... | null | null | null | null | null | null |
GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies | Accept (poster) | Summary: This work presents a new robust and scalable method for inferring phylogenetic trees based on (variational) Bayesian inference.
Strengths: Originality
- the originality of this work is in providing a robust and rigorous solution to an important application problem where the application of Bayesian methodology... | Rebuttal 1:
Rebuttal: > 1. Treatment of the benchmark data sets is limited compared to the potential of the method for real applications. This could be expanded to highlight the relevance of the work.
Thank you for your constructive comments.
Regarding the improved treatment of the current experiments, we introduced a... | Summary: The authors propose a method for learning phylogenetic trees from sequence data.
The key idea is borrowed from [16], which is to represent the tree topology in terms of an embedding of the leafs of the trees in a continuous space, $z \in \mathcal{Z}$ from which the topology is extracted via a mapping $\tau \... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and comments.
> My main question is what are the benefits compared to, e.g., MrBayes, VBPI-GNN, and the method proposed in [33], the first two of which are referred to as gold standard. I'm guessing the proposed method is computationally more efficient and s... | Summary: The authors present a novel variational distribution for tree topologies, $Q(\tau)$, in the context of variational inference (VI) in phylogenetics. They construct their $Q(\tau)$ by introducing a continuous distribution $Q(z)$ in hyperbolic space and define $Q(\tau)$ by an expectation over the support of $Q(z)... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful suggestion and feedback.
> W1. The standard deviations of the proposed method reported in Table 2 are low for multiple datasets; this may be due to the consistency of the optimization across seeds, but may also indicate that the support of $Q(z)$ collapses to regions... | Summary: Authors proposed GeoPhy as a fully differentiable approach for phylogenetic inference, addressing a fundamental problem in phylogenetic inference. In experiments with real benchmark datasets, GeoPhy demonstrated its superior performance compared to other methods when considering all topological candidates. Thi... | Rebuttal 1:
Rebuttal: > W1. It is highly recommended to include an algorithm block summarizing the GeoPhy method from input to output for clarity. Additionally, there are some details in the experimental section that need clarification.
Thank you for your suggestion. We will include the algorithm block that summarizes... | Rebuttal 1:
Rebuttal: We thank all the reviewers for taking the time to provide thorough and insightful feedback on our manuscript.
Your constructive comments have greatly enhanced the quality of our work.
In response to the points raised, we have engaged in a detailed discussion on the expressivity, performance, and l... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposed a family of implicit distribution over tree topologies which allows support free variational Bayesian phylogenetic inference. The distribution is constructed based on the neighbor joining algorithm which maps a distribution over the tip node vectors to the tree topology space. Both Euclidea... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and feedback.
> W1. As admitted by the authors, the proposed variational approximation $Q(z)$ and the reverse distribution $R(z | τ)$ is simple, which may damage the overall approximation quality of the method.
We believe that enhancing the expressiveness of ... | null | null | null | null | null | null |
Quantifying the Cost of Learning in Queueing Systems | Accept (poster) | Summary: In this paper, the authors introduce a new regret metric, called Cost of Learning in Queueing (CLQ), to quantify the rate at which an optimal scheduling policy can be learned to minimize the time average queue lengths. The authors derive a lower bound to CLQ and show that an UCB-based policy comes close to ach... | Rebuttal 1:
Rebuttal: **Comparison to [34] (Question 1 and Weakness 1).**
The reviewer is right that the analysis in~[34] gives a stronger asymptotic queue length bound. However, the setting in [34] that resembles our single-queue setting ([34, Theorem 4]) gives a bound with worse dependence on $\epsilon$. In fact, w... | Summary: In this paper, the authors propose a new metric to quantify the cost of learning in queueing networks. This notion is required to capture the differences between holding costs in queues and, say costs accumulated in a bandit setting; the latter having a monotonicity property (in expectation). The authors then ... | Rebuttal 1:
Rebuttal: **Non-instance dependent guarantees (Weakness 1).**
We appreciate and will adopt the reviewer's suggestion to discuss this further around Theorem 1, and not just in the conclusion (as we currently do).
**Confusing sentence on page 7 (Weakness 2).**
We agree that this sentence was confusing; we i... | Summary: This paper studies a problem that involves both learning with queueing.
In the simple setting, there is a single queue served by multiple
servers. However, the service rate at each server is unknown and needs
to be learned. Intuitively, the combination of the learning policy and
the scheduling policy will impa... | Rebuttal 1:
Rebuttal: **Comparison to [39] (Question 1).**
The reviewer is right that the guarantee in [39, Theorem 1] can be translated into a CLQ bound. In particular, it implies a cost of learning of $O(\frac{N^4M^4}{\varepsilon^3})$ for a stationary multi-server system with $N$ agents and $M$ workers. This is subop... | Summary: The authors consider online queuing systems in a discrete time setting. They study settings with single class queue and multi-class queues, and they propose to consider a metric CLQ that serves as a conservative measure on how the queue length(s) could grow across every time point in a horizon. The authors pro... | Rebuttal 1:
Rebuttal: **Long-term optimality (Question 2/Weakness 3).**
We thank the reviewer for pointing out that we may get a better bound for a larger $\tau$. Indeed, our analysis directly extends to show that UCB converges to the optimal $O(1/\varepsilon)$ scaling (for the single-queue setting). By combining Lemma... | Rebuttal 1:
Rebuttal: We conducted a new simulation with the algorithm from [34, Figure 7] under the same setting of Figure 1 in our paper. The figure in the attached PDF shows that their algorithm has a significantly worse transient behavior despite its optimal asymptotic regret scaling.
Pdf: /pdf/a8e12f787a89256cabe... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Towards a fuller understanding of neurons with Clustered Compositional Explanations | Accept (poster) | Summary: This paper is a niche extension of seminal work on network dissection. The authors present a generalization, called Clustered Compositional Explanations, that combines Compositional Explanations with clustering and a novel search heuristic to approximate a broader spectrum of the neuron behavior.
Strengths: E... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We understand that our work did not present well to the reviewer, but perhaps that is due to a misunderstanding of the paper. First of all, the reviewer mentions the research of a “set of clusters which group neurons.” However, we never grouped n... | Summary: The authors propose a novel XAI method called Clustered Compositional Explanations (CCE), that aims to descibe the function that a group of neurons in a neural network perform. The method is built on top of CoEx (Mu and Andreas 2020) and NetDissect (Bau et al 2017) with the novelty being its generalization to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the appreciation of the important research questions addressed by our paper and our presentation. We hereby clarify and answer point by point both the questions **(Q)** and highlighted weaknesses **(W)**.
**W1)** We can assure the reviewer that, as written in the checkli... | Summary: This paper extends the ideas of Mu (2020) to examine a more powerful class of compositional explanations of neurons, by adding the goal of explaining other ranges of neuron activations, unlike previous work that had restricted analysis to the top ranges only. Like the previous work by Mu, the paper searches f... | Rebuttal 1:
Rebuttal: We thank the reviewer for the meaningful suggestions. Following the recommendation, we ran an experiment to better consider middle activations, and we feel that the addition of this experiment to the appendix makes the paper stronger.
In particular, we tested how many times the network changes i... | Summary: This paper focuses on a problem with Network Dissection and Compositional Explanation methods: these two methods explain the concept encoded by a neuron (or, more precisely, a convolutional filter) by only considering highly activated regions in the feature map. To address the problem, this paper proposes to d... | Rebuttal 1:
Rebuttal: We thank the reviewer for the appreciation of the important research questions addressed by our paper. We hereby clarify and answer point by point both the questions **(Q)** and highlighted weaknesses **(W)**.
**W1)** While we do agree that our paper is an extension of previous work, we stress th... | Rebuttal 1:
Rebuttal: Dear reviewers,
we report the additional experiments requested by “Reviewer 2vbF” and "Reviewer CMC6” in the file attached to this global comment. We validated the importance of the middle activation (**Table 1**) and tested our algorithm on models (VGG16 and ResNet18) trained on a different da... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper represents a generalization of compositional explanation called clustered compositional explanations which combines compositional explanations with clustering and a search heuristic to approximate a broader spectrum of the neuron behavior, by proposing the Min-Max Extension per Sample Heuristic (MMES... | Rebuttal 1:
Rebuttal: We thank the reviewer for the appreciation of our paper and our contribution.
Regarding the difference between Mu and Andreas [24], given the additional page available in the camera-ready version, we plan to move Appendix A into the main paper to address the reviewer's concern and to better highl... | null | null | null | null | null | null |
Recovering Simultaneously Structured Data via Non-Convex Iteratively Reweighted Least Squares | Accept (poster) | Summary: This paper proposes an IRLS method for recovering data with multiple, heterogeneous low-dimensional structures from linear observations. It combines non-convex surrogates for row-sparsity and rank, to identify simultaneously row-sparse and low-rank matrices from limited measurements. Theoretical results are pr... | Rebuttal 1:
Rebuttal: We appreciate your constructive and very detailed feedback to our submission. A point-by-point response to your comments follows below:
> Although it is also true of other results in this area, only local convergence is guaranteed and practically it may be challenging to guarantee an initializati... | Summary: This article introduces an algorithm for recovering jointly row-sparse and low-rank matrices from (underdetermined) linear measurements. The algorithm is based on iteratively reweighted least squares for a non-convex objective. The method is theoretically analysed, establishing local quadratic convergence rate... | Rebuttal 1:
Rebuttal: We appreciate your constructive and very detailed feedback to our submission. A point-by-point
response to your comments follows below:
> The numerical results are somewhat limited, it would have been nice to have a discussion on the applications of the proposed method and more realistic numerica... | Summary: Paper proposes to solve inverse problems on matrices subject to multiple types of sparsity (e.g. low-rank and element-wise sparsity) using an algorithm designed for the non-convex objective functions involved. The algorithm is a re-weighted least squares, which despite not solving a convex problem, authors pro... | Rebuttal 1:
Rebuttal: We appreciate your constructive and very detailed feedback to our submission. A point-by-point
response to your comments follows below:
> The paper contains statistical results of the type ”if at least m measurements are available
then the algorithm achieves good performance”, but is missing a mo... | Summary: This work studies the problem of recovering a low-rank and row-sparse matrix from its compressed linear observations. A method based on iteratively re-weighted least squares is proposed, in which the sparsity inducing function is a non-convex log function. For theoretical contributions, this work provides a lo... | Rebuttal 1:
Rebuttal: We appreciate your constructive and very detailed feedback to our submission. A point-by-point
response and clarification with regards to your comments follows below:
> (1) The algorithm requires estimates of the rank and row sparsity, and the theoretical conclusions require that these estimates... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Deep Evidence Regression for Weibull targets | Reject | Summary: This paper aims to explore the application of a scalable UQ-aware deep learning technique, Deep Evidence Regression, and applies it to predict Loss Given Default. It extends the Deep Evidence Regression methodology to learn target variables generated by a Weibull process and provides the relevant learning fram... | null | Summary: Authors tackle the problem of uncertainty quantification for predicting credit risks. Concretely, they have applied a scalable UQ-aware deep learning technique, Deep Evidence Regression to predicting Loss Given Default with uncertainty. Authors argue that the conventional methods use for uncertainty quantifica... | null | Summary: This paper introduces the utilization and extension of deep evidential regression for uncertainty estimation in credit risk prediction. The approach assumes a Weibull distribution for the target variable (e.g., LGD or a synthetic target). The authors modify the evidential regression mechanism to accommodate ta... | null | null | null | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Unified Detection Framework for Inference-Stage Backdoor Defenses | Accept (poster) | Summary: This work formulates the inference-stage backdoor detection problem. The authors then propose a framework to establish provable guarantees w.r.t. the detection FPR, given some validation data on hand. Finally, they derive the optimal detection rule (in the Neyman-Pearson paradigm) in a simplified scenario, and... | Rebuttal 1:
Rebuttal: > Q1: Inaccurate terms in the title
R: We appreciate your suggestion to improve the current title to better align with specific content, such as focusing on inference-stage backdoor input detection. Your input is valuable, and we will carefully incorporate your suggestions in our revision pro... | Summary: This paper proposes a backdoor sample detection method. It utilizes Mahalanobis distance as the score function to compute the probability of a given sample being poisoned. It also leverages an existing statistical tool, the conformal prediction framework, to determine a statistical threshold for the computed s... | Rebuttal 1:
Rebuttal: We sincerely thank reviewers for investing their time and energy into reviewing our manuscript and offering valuable feedback. We're pleased that they recognized the quality of our writing, acknowledged the novelty of our proposed framework (Reviewer vAAp), and found our approach effective across ... | Summary: This paper formulates the inference-stage backdoor detection in terms of backdoor-sample identification and proposes a unified defense framework. It derives a theoretically optimal detection rule and validates its effectiveness in both CV and NLP domains.
Strengths: 1. The paper is well-organized with a compr... | Rebuttal 1:
Rebuttal: We sincerely thank reviewers for investing their time and energy into reviewing our manuscript and offering valuable feedback. We're pleased that they recognized the quality of our writing, acknowledged the novelty of our proposed framework (Reviewer vAAp), and found our approach effective across ... | Summary: This paper proposes a unified inference-stage detection framework to defend against backdoor attacks. The authors first formulate the inference-stage backdoor detection problem, discuss its challenges and limitations, and then suggest a framework with provable guarantees on the false positive rate or the proba... | Rebuttal 1:
Rebuttal: We sincerely thank reviewers for investing their time and energy into reviewing our manuscript and offering valuable feedback. We're pleased that they recognized the quality of our writing, acknowledged the novelty of our proposed framework (Reviewer vAAp), and found our approach effective across ... | Rebuttal 1:
Rebuttal: We sincerely thank reviewers for investing their time and energy into reviewing our manuscript and offering valuable feedback. We're pleased that they recognized the quality of our writing, acknowledged the novelty of our proposed framework (Reviewer vAAp), and found our approach effective across ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
On Proper Learnability between Average- and Worst-case Robustness | Accept (poster) | Summary: This paper initiates the study of a new kind of PAC learning: probabilistically robust PAC learning. The authors show that the finiteness of the VC dimension of the function class is not sufficient to obtain a proper learning rule in this new PAC learning setup. However, they show that for Lipschitz losses t... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding the results to be a surprising/interesting contribution, this setting to be interesting to others, and the array of tools used to be of broader interest to the community.
**Q1**: *"Unevenness of the presentation."*
A1: We agree with the reviewer and will make ... | Summary: This paper investigates the relaxations of the worst-case robust loss to make VC classes properly PAC learnable. Firstly, this paper shows that an exsiting and natural relaxation does not work. Then, the paper gives a family of robust loss relaxations that interpolate between average- and worst-case robustness... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding the results in this work to be interesting.
**Q1**: *"Minor issues."*
A1: We thank the reviewer for pointing out these issues. We will fix them in the camera-ready version.
**Q2**: *"Would you please show some ideas about the proof of Lemma 3.2? When conside... | Summary: This paper studies the proper robust learnability under relaxation of the (usual) worst-case/all powerful adversary assumption.
- The authors first show that finite VC dimension is not sufficient to enable proper learnability under the relaxation proposed by Robey et al. (2022).
- For another generalization ... | Rebuttal 1:
Rebuttal: We thank the reviewer for noting that the results in this work are of interest to the learning theory community and that relaxing the worst-case analysis is well-motivated.
**Q**: *"Is it a limitation / too big of a relaxation to have the adversary pick a perturbation independently of the unpert... | Summary: This paper studies the setting of robust PAC learning to test time attacks, using a relaxed notion of robustness on average instead of robustness to the worst-case attack.
The contributions are as follows.
-Negative result: even when using the relaxed notion of robustness, improper learning is impossible.... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding that this paper provides a nice contribution to the literature on robust learning.
**Q1**: *"In this model, is the set G and measure μ being chosen at training time and known to the learner?"*
A1: Yes, the set G and the measure \mu are chosen at training time ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Attention as Implicit Structural Inference | Accept (poster) | Summary: The paper shows how attention mechanisms can be interpreted as expectation values over learnable graph connectivity structures given a structural prior; that is from a perspective of (structural) variational inference. The authors first demonstrate this link for cross- and self-attention heads, then proceed wi... | Rebuttal 1:
Rebuttal: Thank you for your review,
- *Since cross attention and self attention only differ by setting $x=x'$ for self attention, sections 3.2 and 3.3 could easily be merged*
In hindsight we agree with this, and would reduce self-attention and cross-attention to a single section.
- *Would introducing t... | Summary: This work presents a theoretical framework of how the attention mechanism often used in transformers can be recast as inference over possible adjacency structures in graphical models. In particular, there is an implicit inference on the distribution of edges within a graphical model defined over nodes in the q... | Rebuttal 1:
Rebuttal: Thank you for your review,
- *For the first toy problem, there could be more description as to why having a two-hop neighborhood would be advantageous, or what kind of data would have this property. The second toy problem's motivation was much more clear to me.*
Since this concern was brought up... | Summary: The paper proposes a framework for interpreting standard formulations of attention mechanisms through the lens of graphical models. The authors illustrate that their formulation unifies architectures and offers a way to easily generalize and improve the existing formulations.
Strengths: - The paper offers an ... | Rebuttal 1:
Rebuttal: - *How scalable are the proposed changes to the attention mechanisms? How do these changes interplay with popular architectures?*
While we see no reason, in principle, for scaling issues, since both modifications were designed with computational complexity in mind. (Multihop requires a single ext... | Summary: This paper proposes a probabilistic interpretation of attention mechanism, where the computation of attention can be expressed as the expectation of a value function defined on the nodes of a graph consisting of the query nodes and key nodes, and the expectation is taken with respect to the posterior distribut... | Rebuttal 1:
Rebuttal: Thank you for your review,
since both your questions were raised by more than one reviewer we have included the answer in the global response. | Rebuttal 1:
Rebuttal: Thank you to the reviewers for their insightful comments. A couple of concerns were repeated across reviewers which we will address here.
We appreciate reviewers concern that the experiments were on toy data, however, we would like to stress that we view the main contribution of the paper as theo... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This submission describes how many different transformer architecture variants can be see as implicit structural inference. The inference is understood as taking an expectation over possible connectivity structures constrained by a prior over structures. Several variants of attention are shown to be describa... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review.
- *The paper is largely limited to describing various existing models within the attention as expectation over structures framework.*
We see our key contribution as a unifying theoretical framework helping to understand the fundamental computation underlyin... | null | null | null | null | null | null |
Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias | Accept (poster) | Summary: This paper proposes a vision language pretraining method that focusing on tackling the bias caused by different languages. The Text Alignment Regularization (CTR) is proposed to unify cross-lingual semantic representations of medical reports. The experiments show that the proposed CTR can effectively eliminate... | Rebuttal 1:
Rebuttal: ## Response to Reviewer uyBA
### 1. Response for Weakness 1:
> The design of the proposed visual language model is closed to existing MLM and CLIP based VLP methods with a incremental improvement of CTR. More analysis is needed for the difference between existing methods and existing VLP methods.... | Summary: This paper presents a unified framework for Cross-Lingual Medical Vision-Language Pre-Training (Med-UniC), integrating multimodal medical data from different languages (e.g., English and Spanish). A Cross-lingual Text Alignment Regularization (CTR) is proposed to explicitly unify cross-lingual semantic represe... | Rebuttal 1:
Rebuttal: ## Response to Reviewer NLjC:
### 1. Response for Weakness 1:
We sincerely appreciate your insightful feedback about our research. Concerning the results from other SOTA methods, these $\textbf{were all pre-trained on MIMIC-CXR}$, the English dataset, with the exception of GLoRIA, which was pre-... | Summary: One common challenge in performing medical vision-language pre-training (VLP) is data scarcity, especially in languages other than English. This challenge can be addressed by combining datasets from various languages to train language-agnostic models, but the authors empirically show that each language communi... | Rebuttal 1:
Rebuttal: ## Response to Reviewer ZNkV
### 1. Response for Weaknesses:
> The bias analysis section seems brief given how much attention it was given in the abstract/intro. The authors state that more analysis is in the appendix, but I would have wanted to see more in the main paper .
Thanks for your commen... | Summary: The paper aims to address community bias caused by having data in multiple languages in medical vision-language pre-training (VLP). Specifically, it introduces the Unifying Cross-Lingual Medical Vision-Language Pre-Training framework to integrate multi-model data from English and Spanish and proposes a Criss-l... | Rebuttal 1:
Rebuttal: ## Response to Reviewer YFc1
### 1. Response for Weakness 1 and 2:
> - Looking at Fig 4, it is clear that the pre-training methodology proposed by the authors has significant improvements in bringing the learned representations from the two languages closer in the latent space. However, the two re... | Rebuttal 1:
Rebuttal: - We add the graphical explaination of the bias in $\textbf{Fig. A}$ and $\textbf{Fig. B}$ for reviewer `ZNkV`.
- We select the results from Tab 5 in the appendix D.2 and Tab 4 in the main paper to construct $\textbf{Tab. A}$ for reviewer `NLjC`. The $\textbf{Tab. A}$ shows the ablation experimen... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Unsupervised Behavior Extraction via Random Intent Priors | Accept (poster) | Summary: The authors propose UBER, a method for learning a collection of behavior policies from offline experience data lacking reward labels and ultimately adapting these behaviors in an online setting. UBER generates a collection of randomly-initialized reward models and trains a policy on the offline data using each... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We appreciate the reviewer's valuable feedback.
**W1: Concerns about the generosity of the theorems.**
- Theorem 4.2
Recent advanced analysis [1] allows us to refine the suboptimality bound of Theorem 4.2 to be $\tilde{O}(\sqrt{d^2H^3/N})$ without algorithmic adjustments. Then... | Summary: The paper studies a setting where there is an offline trajectory dataset with no reward information and the goal is to extract effective behaviors from the offline data such that they can be re-used during a separate online phase to accelerate online learning. To extract effective behaviors from the offline da... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We appreciate the Reviewer for finding our work novel, effective and well-written. We provide clarification to the points the Reviewer raised as follows.
**S1: Discussion and comparison with previous behavior extraction methods.**
**A for S1:** Previous behavior extraction method... | Summary: This paper tackles the problem of unsupervised behavior extraction from reward-free offline data. The main idea is to pre-train multiple policies with random rewards. UBER consists of two phases. It first trains $N$ ($100$ or $256$) policies with random rewards with an offline RL algorithm (TD3+BC), and in the... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your constructive feedback. We have provided additional experimental results and explanations to address your concerns, and we hope the following clarifications shed light on the raised points.
**W1: The theoretical results do not seem to justify the use of random re... | Summary: The authors propose unsupervised behavior extraction via random intent priors (UBER), an unsupervised method for extracting and learning behaviors from an offline dataset for downstream tasks. Assuming the situations where there are no reward labels for the transitions in the offline dataset, they suggest usin... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your constructive feedback. We've provided additional experimental results and explanations to address your concerns, and we hope the following clarifications shed light on the raised points.
**Q1 \& W1.1: The motivation and justification of using random intentions a... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank all the reviewers for their constructive feedback and valuable insights. We are encouraged to learn that many found our work "novel," "interesting," "fairly promising," and "well-written." We genuinely appreciate these positive remarks.
We acknowledge the concerns raise... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper studies the usage of unsupervised (reward-free) data to help RL, which could extract useful behaviors from offline reward-free datasets. The proposed method is called UBER, which generates random intent priors and trains the agents based on them. The procedure generates diverse behaviors which in tur... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for taking the time to review our manuscript and for providing insightful feedback. We appreciate the opportunity to clarify our contributions and address your concerns.
**W1 \& Q1: Justify the novelty part when it compares to the unsupervised RL literature.**
**A for W... | null | null | null | null | null | null |
HiBug: On Human-Interpretable Model Debug | Accept (poster) | Summary: This paper proposes a novel model-debugging method for deep learning-based classifiers. Technically, the proposed method first integrates a method for assigning attributes to training data. The method leverages a pre-trained large language model, such as chatGPT, to generate visual attributes based on task des... | Rebuttal 1:
Rebuttal: Thank you for pointing out our paper's problems. In the following parts, we address your concerns:
1. **First, the paper could provide a clearer distinction between the contributions and novelties of their work compared to the existing work, Domino [1].**
- Thank you for pointing out this pro... | Summary: HiBug seeks to identify (useful) NL descriptions of the "slices" of inputs on which some model (say an image classifier) has higher error rate, and perhaps even explain why the error rate is high (e.g., not enough training data in that space, or some data bias towards an unrelated correlation).
Prior work (sa... | Rebuttal 1:
Rebuttal: We sincerely thank your recognition of our paper. We address your concerns as follows:
1. **Some assertions, especially in the design section, are provided without explanation or justification, making it hard to assess their veracity.**
- We refer to the common response CQ1 to this question.
2... | Summary: The authors introduce HiBug, an approach to identify bugs in trained models in an interpretable fashion. At its core, HiBug annotates inputs (images) with a set of attributes and values obtained using an LLM followed by a VQA step, and then identifies those combinations of attributes corresponding to subsets ... | Rebuttal 1:
Rebuttal: We sincerely thanks your recognition of our paper. We address your concerns as following:
1. **While HiBug might be novel on paper, how different is it from what developers debugging ML models already do on a day-to-day basis?**
- Before designing HiBug, we investigated the common debugging fl... | Summary: In this paper the authors describe a system that introduces interpretability into the LLM model debugging. They use an LLM like chatGPT to reveal interpretable features from data for which the ML models don't perform well, With these features they find poorly performing data slices and provide identifiable... | Rebuttal 1:
Rebuttal: Thank you for pointing out our paper's problems. We sincerely hope you can read our clarification of HiBug’s novelty in the author rebuttal section above. In the following parts, we address your concerns:
1. It will be great if the authors can address the questions in the weakness section.
1.... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their constructive comments and recognition of our work's strength.
Before addressing common questions, we clarify the novelty of our paper as follows:
### Novelty
- The problem HiBug focuses on is critical yet under-explored. Before designing HiBug, we invest... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback | Accept (poster) | Summary: The work aims to solve censored diffusion sampling problem, which prevent diffusion model generating malign / bad images. The core approach is to train a classifier and apply classifier-guided diffusion generation.
Strengths: 1. Authors present an interesting finding that the classifier-guided diffusion can ... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive comments. As suggested by the reviewer, we added an experiment using the **Stable Diffusion** model. Please refer to Section 2 of the common rebuttal and the attached pdf document for details.
We plan to add this new experiment, with some polishing, to the la... | Summary: The authors examine the problem of preventing the generation of certain types of images generated by a diffusion model. To achieve this, the authors propose using a reward model trained on human feedback. The authors demonstrate their approach from examples that require minimal human feedback to achieve suffic... | Rebuttal 1:
Rebuttal: We highly appreciate the constructive comments and the positive evaluation from the reviewer. We made our best efforts to reflect the comments to improve the paper. In the following, we address each of the reviewer's concerns in detail.
### 1. On time-dependent vs. time-independent guidance
Guida... | Summary: This paper presents censored diffusion model training by using a reward model trained using human feedback. Towards this, the paper utilizes reward model ensembles (for benign dominant settings) and tools from imitation learning (for malign dominant settings).
Strengths: The human feedback part is pretty inte... | Rebuttal 1:
Rebuttal: We appreciate the constructive comments and the positive evaluation from the reviewer.
In the following, we make our best efforts to address each of the concerns and questions.
For the concern regarding the comparison against other methods, please refer to Section 4 within our common response.
##... | Summary: This paper studies the problem of preventing the generation of unwanted images in diffusion models. It formulates the task of 'censoring' and proposes using reward model trained from human labelling to guide the diffusion model. The method requires no fine-tuning and a few minutes of human feedback, while disp... | Rebuttal 1:
Rebuttal: We highly appreciate the constructive comments and the positive evaluation from the reviewer.
We made our best efforts to reflect the comments to improve the paper.
In the following, we address each of the reviewer's comments in detail.
### 1. Regarding the principles for selecting the techniques... | Rebuttal 1:
Rebuttal: # Common Response (pdf attached)
We thank all reviewers for the extremely detailed and constructive feedbacks. We are delighted that most reviewers found our ideas convincing and the contributions solid. Below, we provide our response to some common concerns.
### 1. List of additional experiments... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors combine pre-trained diffusions with classifiers trained on human feedback about which type of images to omit, and use the classifiers to guide diffusion sampling using the Universal Guidance technique. They observe that they are able to filter out several types of malign images on a variety of data... | Rebuttal 1:
Rebuttal: We are delighted to see that the reviewer empathizes with our problem statement and solutions. We also greatly appreciate the inspiring comments with the positive evaluation. We have devoted our best efforts to provide satisfactory resposnes to each of the reviewer's concerns below.
### 0. Regard... | Summary: This paper proposes an approach to censor the sample generation of pre-trained diffusion probabilistic models to better align with human preferences. The authors use minimal human feedback (<3min spent in providing the feedback for basic tasks, and <15min for more complicated tasks that they consider) to train... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive comments. We made our best efforts to address the concerns and reflect the comments to improve the paper. For the concerns regarding the complexity of the tasks we cover, comparison against other methods, and the longer human time for the LSUN Bedroom task, p... | null | null | null | null |
Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach | Accept (poster) | Summary: The authors introduce a novel algorithm, Manifold Projection-Diffusion Recovery (MPDR), for training energy-based models (EBMs) that improve the performance of anomaly detection tasks. These tasks are highly relevant in real-world applications like industrial surface inspection, machine fault detection, and pa... | Rebuttal 1:
Rebuttal: Dear Reviewer Tcts,
We would like to express our sincere gratitude for taking the time and effort to review our paper. We greatly appreciate your highly detailed and constructive comment and are happy to answer your questions.
**Theoretical Analysis**
> How does the choice of manifold affect th... | Summary: Paper proposes MPDR, a novel method of using auto-encoders for training EBM.
Some practical techniques are introduced.
Extensive numerical experiments are done.
Strengths: Numerical experiments cover a large scope of benchmarks. And it shows superiority on most benchmarks.
Weaknesses: No theoretical guaran... | Rebuttal 1:
Rebuttal: Dear Reviewer Pi2D
Thank you so much for your comment. We would like to address your concerns in detail.
> No theoretical guarantee is provided.
We are concerned that this statement does not accurately reflect what is presented in the paper. Please refer to the end of Section 3.2 and Appendix ... | Summary: This paper introduces an energy-based model based on the manifold of low-dimensional data. To train the EBMs, this paper takes the idea of maximum recovery likelihood and adds a layer of autoencoder to approximate the low-dimensional manifold representing the data. This introduces perturbation along the low-di... | Rebuttal 1:
Rebuttal: Dear Reviewer gSqM,
Thank you for dedicating your time to reviewing our work. We sincerely appreciate your detailed feedback. Here, we would love to answer your questions in depth.
**The novelty of the paper**
We would like to highlight that simplicity does not always equate triviality. MPDR (o... | Summary: This paper introduces an EBM-based model for anomaly detection in the latent manifold space. The proposed model first trains an autoencoder that maps a data point $x$ into the low-dimensional $z$, and then two-stage sampling strategy is developed to generate the original data via the LMC algorithm. Several not... | Rebuttal 1:
Rebuttal: Dear Reviewer dsqA,
Thank you for taking the time to review our paper. We deeply value your feedback. Below, we will address your concerns and questions.
> MVTec-AD dataset and comparison with UniAD and DRAEM
Thank you for your suggestion. MPDR also demonstrates promising performance on MVTec-A... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a novel anomaly detection algorithm utlizing energy-based models (EBMs). The proposed method, Manifold Projection-Diffusion Recovery (MPDR), is based on recovery likelihood, a framework for learning energy functions by denoising data from artifically injected Gaussian noise. MPDR uses deter... | Rebuttal 1:
Rebuttal: Dear Reviewer ekma,
We're truly grateful for your insightful feedback. Your time and effort in evaluating our work is deeply appreciated. Please allow us to address any questions you have.
> Related works are not covered in sufficient detail.
We apologize that we couldn't discuss all related w... | null | null | null | null | null | null |
Stable Vectorization of Multiparameter Persistent Homology using Signed Barcodes as Measures | Accept (poster) | Summary: In this paper, the authors are addressing a very critical need in topological data analysis (TDA), vectorization of multiparameter persistence (MPH). Persistent homology (PH) is the key method in TDA, but in its current form, it allows only a single function to use in its key process, filtration. By enabling m... | Rebuttal 1:
Rebuttal: Thank you for your time and feedback.
- *Performance on graph data.*
We agree that the results in section 4.3 (graph data) are not as good as those in section 4.2 (point cloud data).
We note, however, that the performance on the virtual screening task of our method is very good; ToDD is a supervi... | Summary: The paper vectorizes data descriptors coming from multiparameter persistent homology for classifying point clouds and measuring similarity between graphs extracted from databases of times series and molecules.
Strengths: The paper includes rigorous definitions and proves (in the appendices) three theorems fr... | Rebuttal 1:
Rebuttal: Thank you for your time and feedback.
We start with two clarifications: Our applications go well beyond classification of point clouds up to isometry, and even in the point cloud application, we do not seek a strong isometry invariant since such invariants are necessarily highly sensitive to, e.g... | Summary: The authors introduce first vectorizations of multiparameter persistent homology (MPH) via signed barcodes, that are easy to compute and shown to be stable. The two proposed vectorizations often outperform the state of the art MPH methods on a variety of data sets.
Strengths: (S1) The paper is clearly organiz... | Rebuttal 1:
Rebuttal: Thank you for your time and feedback.
- *Comment on/discuss the fact that the proposed approach is theoretically weaker than other approaches (Appendix A, Proposition 1), but it outperforms them.*
We believe this is due to the fact that, despite being weaker than the rank invariant, using the Hil... | Summary: This work promote the use of signed barcodes for feature generation, and proposed the feature generation pipeline based on the signed bar codes,
a. the work introduces two general vectorization techniques for signed barcodes;
b. the authors prove Lipschitz-continuity results that ensure the robustness of the ... | Rebuttal 1:
Rebuttal: Thank you for your time and feedback.
- *Motivation for multiparameter persistence and extra info compared to one-parameter.*
Thank you very much for pointing out the lack of references motivating multiparameter persistence and its relationship to one-parameter persistence.
To address this, we wi... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their time and feedback.
We have responded to their questions, and we will be happy to provide further clarifications, if required.
Only a couple of short paragraphs (included in the responses to specific reviewers, below) are required in the main body of ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Online Pricing for Multi-User Multi-Item Markets | Accept (poster) | Summary: The paper proposes online pricing and allocation strategies for multi-user multi-markets model under three different valuation models.
The main contribution is to extend dynamic pricing strategies from the literature to the case of more than one item and more than one user.
The setting goes as follows:
- At ... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and thoughtful questions.
In this paper, our main contribution is the analysis of revenue-maximizing algorithms that allocate and price multiple items to multiple users. As stated in our related work section, Kleinberg and Leighton [3] developed a widely-a... | Summary: The paper considers the online pricing problem where there are several users and items. Each user has a (private) valuation for each item and a (public) upper bound for the number of items desired, both of which could vary at different rounds. The provider has an available item set at each round, and the goal ... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and questions. Please find our responses to the questions below:
* Based on your comments, we agree to include a discussion on the computational complexity and resource requirements of the algorithms. As you noted, the integer linear program in (7) can be ... | Summary: The paper studies the problem of dynamic pricing in which multiple items are offered to multiple users at each round. Authors propose a novel algorithm for maximizing revenue under three user valuation models: fixed valuations, random experiences and random valuations. Authors provide theoretical guarantees a... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and questions. Please find our responses to the questions below:
* As discussed in our related works section, dynamic pricing literature has only considered settings where a single user interacts with the provider per time step. Therefore, even if their va... | Summary: The paper proposes algorithms for optimizing the sale of multiple goods to multiple users, taking into account their time-varying valuations throughout repeated rounds. These algorithms efficiently learn from users' accept or reject feedback and utilize this information to make optimal offers and prices based ... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and questions. Please find our responses to the questions below:
* As discussed in our related works section, dynamic pricing literature has only considered settings where a single user interacts with the provider per time step. Therefore, even if their va... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their constructive feedback and thoughtful questions. Please see our separate responses below each review.
In response to a question from reviewer Hkdu regarding the experiments, we provide the new results in the PDF file. In our initial submission, Figure 4 was ge... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper addresses the problem of online pricing in multi-user multi-item markets. The main objective is to maximize revenue by selling multiple items to multiple users in each round. The paper proposes algorithms that efficiently offer and price items while learning user valuations from accept/reject feedba... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and questions. Please find our responses to the questions below:
* Our framework can be readily extended to capture settings in which multiple users are offered the same item. We could achieve this by replicating each item according to its number of availa... | null | null | null | null | null | null |
Multi-Modal Inverse Constrained Reinforcement Learning from a Mixture of Demonstrations | Accept (poster) | Summary: The paper proposes the algorithm Multi-Modal Inverse Constrained Reinforcement Learning (MMICRL) for imitation learning mixture of expert demonstrations with various constraints. The algorithm includes agent identification, agent-specific constraint inference and multi-modal policy optimization. The problem is... | Rebuttal 1:
Rebuttal: Dear Reviewer, we sincerely value your time and effort in evaluating our work. Your insights have been valuable to our work. We have prepared comprehensive responses and clarifications to address each point you raised. We hope these responses can resolve your concerns.
1. *"For proposition 4.2, p... | Summary: The paper introduces a new algorithm called Multi-Modal Inverse Constrained Reinforcement Learning (MMICRL) to address the challenge of recovering multiple underlying constraints from a mixture of trajectories demonstrated by different types of expert agents. The algorithm utilizes a flow-based density estimat... | Rebuttal 1:
Rebuttal: Dear Reviewer, we sincerely value the time and effort you have devoted to evaluating our work. To address each point you raised, we have prepared comprehensive responses and clarifications. We hope these responses can resolve your concerns.
1. *"I am not an expert in IRL, but I know some RL algor... | Summary: This paper considers the problem of estimating constraints from a dataset consisting of a mixture of expert trajectories, and proposes an algorithm (MMICRL) for solving that problem.
MMICRL proceeds iteratively, first estimating which trajectories belong to which agent class using a density estimation approach... | Rebuttal 1:
Rebuttal: Dear Reviewer, we sincerely value the time and effort you have devoted to evaluating our work. To address each point you raised, we have prepared comprehensive responses. We hope these responses can resolve your concerns.
1. *"How is evaluation performed given that the agent class is identified i... | Summary: # Problem Statement
The paper addresses a significant problem in Inverse Constraint Reinforcement Learning (ICRL), which is the assumption that all expert demonstrations follow the same constraints. This assumption is problematic because in real-world scenarios, demonstration data may come from various agents ... | Rebuttal 1:
Rebuttal: Dear Reviewer, we sincerely value your time and effort in evaluating our work. Your insights have been valuable to our work. We have prepared comprehensive responses and clarifications to address each point you raised. We hope these responses can resolve your concerns.
1. *"The number of agent ty... | Rebuttal 1:
Rebuttal: Dear Reviewers, Area Chairs, and Program Chairs,
We sincerely appreciate the valuable comments and suggestions provided, as they have been instrumental in enhancing our work. In light of your feedback, we can improve our work with detailed clarifications, comprehensive explanations, and supplemen... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Operator Learning with Neural Fields: Tackling PDEs on General Geometries | Accept (poster) | Summary: This paper creatively employs Implicit Neural Representations (INR) for operator learning on irregular domains. This novel method benefits from INR's capability to adaptively handle irregular grid distributions or irregular geometric areas, making learning the mapping from input to output in the INR's latent s... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful feedback and have addressed the raised concerns below.
## Transformer-based methods
> [...] Besides Geo-FNO and FFNO, transformer-based methods can also naturally handle irregular geometric areas. The authors should at least mention these works.
We thank ... | Summary: This paper proposes a method for solving PDEs with neural networks in continuous space (and optionally time as well). The authors leverage the success of coordinate-based neural networks (or implicit neural representations – INRs) and formulate the problem as an operator learning one, akin to the Neural Operat... | Rebuttal 1:
Rebuttal: We value the reviewer's extensive and detailed feedback and have effectively addressed the raised concerns as follows.
>Connections with Dupont et al., ICML’22.
The work by Dupont indeed inspired the INR part of our contribution, and will be better acknowledged in the final version.
> Efficienc... | Summary: This work tried to introduce an implicit neural field-based framework for solving PDEs for different applications on general geometries. The method represents the input and output function spaces as an implicit neural representation. The method consists of a two-stage pipeline: first, the authors train two aut... | Rebuttal 1:
Rebuttal: We're thankful for the reviewer's helpful feedback and have addressed the raised concerns below.
## Application to inverse design
> Can this method be used for Inverse design?
We agree, this will be clarified in the final version. As you mention, the geometric design section in the core paper... | Summary: In the paper, the author proposed to use neural field in operator learning. In the CORAL model, it first encodes the input into some codes, apply an MLP on the codes, and then decode into the output function. Since the neural field can be continuous evaluated, the CORAL model can be applied to general geometri... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful feedback and have addressed the raised concerns below.
## CORAL framework
> The overall framework is quite simple.
CORAL adopts a classical encode-process-decode paradigm for learning operators. However, training an auto-decoder presents challenges, nec... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their comments and suggestions. We carefully answered all the questions raised by each reviewer. We have also added new experimental results following the suggestions of the reviewers concerning:
* Consolidated results with error bars on *Navier-Stokes* (Reviewer K5... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Train 'n Trade: Foundations of Parameter Markets | Accept (poster) | Summary: This paper introduces a novel concept called *parameter markets*, which serves as a platform for exchanging parameters learned by machine learning models. In this framework, agents have the option to engage in parameter trading, to achieve (1) mutual benefits through collaboration or (2) monetary gains by simp... | Rebuttal 1:
Rebuttal: ### Response to Reviewer Mu3U
We are grateful for the review, the kind words, and the positive assessment. We address your questions and concerns below.
* **On assurance of model privacy and alignment.**
* This is true! We do not view this as a limitation, however. Vast research resources are ... | Summary: * This paper proposes an economic framework for trading parameters of prediction models.
* Interaction is modeled as a brokered marketplace - Each agent $u$ trains a model characterized by parameters $\theta_u$ using gradient descent. At each time-step $t$:
* Each agent performs a gradient descent step on t... | Rebuttal 1:
Rebuttal: ### Response to Reviewer Tv8J
Thank you for your clear summary and thoughtful review! We appreciate the kind words. We answer your questions below and include two new experimental results: multi-agent market and asynchronous parameter trading.
* **On valuation function.**
* Thanks for pointing... | Summary: The paper investigates how to design a marketplace for model parameters. The marketplace consists of agents training models for potentially different objectives and a trusted third-party broker. The broker receives the model parameters from each agent, assesses (and informs each agent of) the loss achieved by ... | Rebuttal 1:
Rebuttal: ### Response to Reviewer iN1r
We are grateful for your review and for describing our framework as novel and interesting. We address your questions in the response below and have updated our paper!
* **On trusting brokers.**
* Indeed, having a reliable broker is **essential for any trading mark... | Summary: The paper proposes a framework for collaborative and competitive parameter trading among deep learning agents. The authors conduct experiments to validate the effectiveness of the proposed framework in improving the performance of the agents. The experiments show that even when the agents are training on diffe... | Rebuttal 1:
Rebuttal: ### Response to Reviewer 91tn
Thank you for finding our framework novel and the results to be encouraging, particularly in terms of trading parameters with different purposes. We appreciate your thoughtful review!
* **On limitations.**
* There are two primary limitations toward building viabl... | Rebuttal 1:
Rebuttal: ### General Response
We are grateful for all the comments and constructive feedback on our work. Reviewers consistently commented that our proposed trading framework is **novel and well-motivated**. Reviewers 91tn, Tv8J, and Mu3U note that our experimental results as **promising and substantial**... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Data-Dependent Bounds for Online Portfolio Selection Without Lipschitzness and Smoothness | Accept (poster) | Summary: The paper considers the online portfolio selection problem. In this problem, one must allocate funds between d possible investment choices, with the goal of maximizing the total amount. In each round, the "success" of each choice is revealed, in the form of a ratio between new and old price, called price relat... | Rebuttal 1:
Rebuttal: Thanks for your careful review.
1. **Typos:** We will correct them. Thank you.
2. **Concern on the small-loss bound:** If the price relatives are not upper-bounded by 1, then the cumulative loss in the small loss bound is defined with respect to the normalized price relatives. Hence, the assumpt... | Summary: This work studies online portfolio selection (OPS) problem and establishes regret bounds that is square-root dependent on some data-dependent quantities, namely, the cumulative loss of the best action or the variation of the gradients respectively, without lipschitzness or smoothness assumption. Although previ... | Rebuttal 1:
Rebuttal: Thank you for appreciating the online portfolio selection problem and our work.
1. **Benefit of data-dependent bounds:** This work is motivated by the high computational complexities of existing logarithmic-regret algorithms. Given that the optimal tradeoff between regret and efficiency remains u... | Summary: The paper presents beyond-the-worst-case regret bounds for Online Portfolio Selection (OPS). In general online learning, beyond-the-worst-case bounds are established using structural assumptions on the loss functions, such as Lipschitzness and smoothness, but the loss functions in OPS are neither Lipschitz nor... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work.
1. **Novelty of the Smoothness Characterizations:**
We are not aware of any similar results in the literature. The closest is perhaps the well-known local smoothness property of self-concordant functions.
2. **Optimal regret-efficiency tradeoff:**... | Summary: The paper studies follow the regularized leader algorithm (FTRL) on the online portfolio selection problem without the assumption of no junk bonds. This makes the resulting loss function non-Lipschitz and non-smooth and makes analyzing the regularized follow the leader algorithm hard to analyze. The paper prop... | Rebuttal 1:
Rebuttal: Thank you for the comments.
1. **On stating Theorem 3.2 for generic losses:** Yes, it is possible to state Theorem 3.2, which is currently stated for online linear optimization, for generic convex losses. Denote the loss function in the $t$-th round by $f_t$. The only modification required is to ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies how to achieve adaptive regret bound, including gradient-variation bound and small-loss bound, for the online portfolio management problem, without the classical no-junk bund assumption. The authors successfully achieve this goal by observing a new kind of smoothness for the function -log wx... | Rebuttal 1:
Rebuttal: Thank you for correctly pointing out our main contribution and emphasizing the novelty in the proposed methods.
1. **On achieving optimal small-loss bounds for both smooth and non-smooth functions simultaneously:** This is an interesting direction. Such generalization requires non-trivial work an... | null | null | null | null | null | null |
Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification | Accept (poster) | Summary: The authors in this paper propose EMBROID aim to improve language models (LM) (such as GPT-3.5) without additional labeled data. Unlike prompt-based models that focus on prompt designs, this paper attempts to modify the predictions of the data point $x$ by considering its neighboring samples. First, the propos... | Rebuttal 1:
Rebuttal: We thank XCbh for their review! We are glad to hear they appreciated Embroid’s motivation, the theoretical analysis, and the extensive validation.
**Limited number of technical problems are solved.** We would like to clarify the novelty of our work. Specifically, our contribution is synthesizing... | Summary: The paper aims to improve prompt performance through a technique of prompt patching where multiple neighbourhood instance of a given datapoint (retrieved through the embedding space of BERT or RoBERTa like model) are used in prediction from the LLM along with the original instance. Finally a majority vote is i... | Rebuttal 1:
Rebuttal: We thank XCbh for their review! We are glad to hear they appreciated Embroid’s simplicity, its theoretical analysis, and the empirical results.
We will update the paper with results on new instruction-tuned models. We will also update the writing in Section 6.2 to clarify the workings of the dif... | Summary: This paper presents EMBROID, a prompt-patching method that corrects erroneous predictions for a prompt via agreements over KNN examples. For this, it employs N different embedding models to get smoothed neighborhood prediction vector. The smoothed information is integrated to combine the voting with quality pa... | Rebuttal 1:
Rebuttal: We thank Bw2X for their review! We are glad to hear they appreciated Embroid’s novelty, the analysis, and our empirical evaluation.
**Evaluation on multi-class.** We found that Embroid can be applied to the multiclass setting through a one-vs-all approach. Please see the global review for more d... | Summary: The authors propose Embroid, a promising method that exploits availability of diverse pre-trained LLMs to create something akin to (but better than) an ensemble approach for prompt-patching.
Strengths: - a promising and easy-to-use method for prompt-patching
- robust theoretical analysis
- extensive empirica... | Rebuttal 1:
Rebuttal: We thank YSHE for their review! We are glad to hear they appreciated the ease of use, our theoretical analysis, and our empirical evaluation.
**Evaluating on stronger general models.** Our original work found Embroid had a substantial win rate (80.6%) and average improvement (4.9 points F1) on G... | Rebuttal 1:
Rebuttal: We thank all reviewers for their feedback. We are glad reviewers recognized and appreciated the novelty/simplicity of our method [Rd7v, Bw2X, YSHE, XCbh, bLRH], our theoretical analysis [Rd7v, YSHE, XCbh, bLRH], and our empirical validation [Rd7v, YSHE, Bw2X, XCbh, BLRH].
We have made a number o... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors focus on few-shot prompted classification using language models. In this setting, they focus on the challenge of developing optimal few-shot (in-context) prompts for language models in domains where data collection is prohibitively expensive. Rather than engineering the prompts themselves, the prop... | Rebuttal 1:
Rebuttal: We thank RD7v for their review! We are glad to hear they appreciated the novelty of our method, its intuitive appeal, and the structure of our evaluation. Our response primarily serves to answer the questions they raised.
**Confusion regarding the definition of “prompt”.** We consider the “promp... | null | null | null | null | null | null |
AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback | Accept (spotlight) | Summary: This paper presents a simulation framework, AlpacaFarm, for developing LLMs with human feedback.
AlpacaFarm adopts LLMs (e.g., GPT-4) to generate feedback (i.e., the ranking of candidate responses given the query), and evaluate the performance by calculating the win-rate against the baseline.
This framework c... | Rebuttal 1:
Rebuttal: We thank the reviewer for their useful review, which we have incorporated to improve our paper.
# Simulated annotators
> *The paper mentioned that 25% of the simulated preferences are randomly flipped. Any explanation on the ratio?
We have expanded our explanation in Appendix B.1 about the 25... | Summary: This paper introduces AlpacaFarm, a simulator that enables faster and cheaper research and development of fine-tuning LLMs with human feedback. The authors propose to use an LLM to simulate human feedback, which is 45x cheaper than using crowdworkers and displays high agreement with humans. They also identify ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their questions and will address their concerns in the updated manuscript.
## Limitations
> *The authors mention some of the assumptions and limitations of the paper but I strongly suggest having a separate section that discusses these in greater depth.*
We agree with ... | Summary: The authors provide a simulator for experiments with LLMs that aim to learn from human-feedback, in particular, human binary comparisons. This allows researchers to run exploratory experiments with, e.g., RLHF, quickly and cheaply, without having to collect human data. The main contribution is the open source ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and insightful feedback, which we incorporated in the updated manuscript.
## Limitations
>*The paper could explore the limitations of Alpaca Farm more.*
We agree with the reviewer’s suggestion and have incorporated the feedback. Please see the [general re... | Summary: The paper identifies three major challenges in training models with human feedback: (a) the cost of *preference* data collection, (b) the lack of trustworthy eval, and © the absence of implementations for reference methods. I completely agree with the fact that the process of training LLMs with human feedback ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging and thorough review. We will clarify and emphasize the answers to all their questions in the updated manuscripts.
## Training in simulating vs with humans
> I will be interested in understanding the gap between the performance of the methods trained wi... | Rebuttal 1:
Rebuttal: # General
We thank the reviewers for their insightful and constructive feedback.
We are pleased that the reviewers found our paper well-written [goj1, pSDY, JtTg, NPeD], thorough [goj1, pSDY], and believe that it may be an impactful and valuable contribution to the community [goj1, pSDY, JtTg, N... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Tackling Unconditional Generation for Highly Multimodal Distributions with Hat Diffusion EBM | Reject | Summary: This work tries to improve the unconditional generation performance of Energy Based Model (EBM) by combining several techniques. It includes a pertrained diffsion model as a part of the generator and train the energy function and generator through cooperative learning. The performance of HDEBM outperforms many... | Rebuttal 1:
Rebuttal: Thank you for your thorough review; we have addressed your comments as follows:
* *Limitations of training the diffusion model:* While training diffusion models on complex datasets can require significant computational resources, a major benefit of our method is that one only needs to learn a trun... | Summary: The paper proposes *Hat Diffusion Energy-Based Model (HDEBM)*, a hybrid model with a generator and an EBM component that can be primarily applied for unconditional image generation tasks. It is built upon the framework of Hat EBM, which produces the final image sample $X$ by combining (through addition) a raw ... | Rebuttal 1:
Rebuttal: We're grateful for the effort in reviewing our work and for your valuable suggestions. Our clarifications and responses follow below.
* *Regarding novelty:* Like the related works TDPM and ES-DDPM, the primary novelty of our works comes from the design choices that we make to incorporate a truncat... | Summary: The authors propose the Hat Diffusion Energy-Based Model (HDEBM), which incorporates a distilled truncated diffusion model as a generator network for a Hat EBM. They note that a perfectly-trained truncated diffusion model can be used to define an MCMC process whose steady-state distribution is the data distrib... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful insights and suggestions to better our work. We have addressed each of your points below.
* *Reorganization to improve clarity:* We agree that the clarity of our presentation could be improved by bringing details from the appendix into the main text. Future revisions ... | Summary: Hat EBM introduced a framework to incorporate an arbitrary generator network $G : \mathcal{Z} \to \mathcal{X}$ (for example a GAN generator or a VAE) into an EBM by defining a joint energy function over the generator latent space and a residual image space that bridges between the generator output and the grou... | Rebuttal 1:
Rebuttal: We appreciate your thorough and positive review. Reponses to your main comments are below.
* *Choice of EBM model family:* Please see our response to a similar question from Reviewer 88Mj.
* *Comparison to the progressive distillation $G_2$ in isolation:* The truncated diffusion model $G_2$ can ... | Rebuttal 1:
Rebuttal: Thanks to all reviewers for their time and insightful comments and suggestions. Our paper will certainly benefit from incorporating reviewer feedback in future revisions. Our global response includes:
* a larger scale HDEBM experiment
* results of ADM for unconditional ImageNet 128x128
* addition... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors describe a method to facilitate faster sampling in diffusion models whilst retaining quality, with a mix of energy and diffusion model. This consists of using an implicit generator, followed by noising then demonising from a retrained distilled diffusion model as a corrector, followed by an energy ... | Rebuttal 1:
Rebuttal: Thank you for your time and your suggestions for improving our work. We agree that our central technical innovation is that taking gradients through the truncated and distilled diffusion, which allows us to learn energy and generator networks that are adapted to the truncated diffusion. We address... | null | null | null | null | null | null |
Make the U in UDA Matter: Invariant Consistency Learning for Unsupervised Domain Adaptation | Accept (poster) | Summary: This paper presents a novel unsupervised domain adaptation method called invariant CONsistency learning (ICON). ICON is very simple; assuming that labeled source samples and clustered target samples are available, ICON uses BCE losses to make the inner product of the features (softmax-normalized) of any two sa... | Rebuttal 1:
Rebuttal: Thank you for the in-depth review. We address all weaknesses below.
**W1 - SoTA > ICON?** ICON is SoTA on the ResNet-50 backbone. UDA performance is sensitive to backbone choice. We choose the most classic and widely used ResNet-50 to demonstrate the superiority of ICON. In contrast, CDTrans uses... | Summary: This paper proposes ICON (Invariant CONsistency learning), a method to utilize the distribution of unlabeled target data in the UDA task.
And it obtains stable performance improvements over 8 UDA tasks.
Strengths: 1. The idea of the article is simple, but it makes sense that the distribution of unlabeled targ... | Rebuttal 1:
Rebuttal: Thanks for the constructive feedback. We address all questions below.
**Q1 - Why low-dimensional pseudo-labels are more accurate?**
We first clarify that our cluster labels are not conventional pseudo-labels, because they are not aligned with the classes in the source domain (details in Reviewer ... | Summary: This paper deals with unsupervised domain adaptation problem. This paper focuses on how to exploit the inherent distribution of target domain to improve the adaptation performance. In detail, it trains two classifier: one is on source domain and the other one is on target domain. Each classifier is trained wit... | Rebuttal 1:
Rebuttal: Thanks for the in-depth review. We will address all weaknesses.
**W1 - Orthogonality of ICON.**
Sorry for the misleading term. We intend to mean that our ICON loss can be plugged into different self-training baselines ($\mathcal{L}_{st}$ in Eq. 2).
We discussed the choice of the self-training bas... | Summary: This paper proposes a new UDA method which strives to produce a consistent classifier for labels in source domain and clusters in target domain. Specifically, this paper introduces an auxiliary task for distinguishing whether the input image pair share the same class/cluster or not. This binary classification ... | Rebuttal 1:
Rebuttal: Thanks for the constructive feedback. We will fix the typos in Q1 and address all concerns below.
**W - Lack of baselines t-SNE in Figure 1.** Sorry for the confusion. Actually, the goal of Figure 1 is not to compare our ICON with baselines, but to depict the condition where a model generalizes. ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors propose an unsupervised domain adaptation method, ICON. The algorithm is similar to self-training on the unlabeled target data, but at the start of each epoch, the unlabeled data are first projected from feature space to a reduced-dimension space and clustered. An auxiliary loss enforces consistent... | Rebuttal 1:
Rebuttal: Thanks for the in-depth comments and suggestions. We will address all concerns below.
**W1 - Why ICON outperforms self-training.** We clarify that the key to ICON's success is invariant consistency instead of dimensionality reduction with UMAP.
- For empirical evidence, we perform additional expe... | null | null | null | null | null | null |
Recovering from Out-of-sample States via Inverse Dynamics in Offline Reinforcement Learning | Accept (poster) | Summary: This paper aims to tackle a critical challenge in offline reinforcement learning, which involves recovering the state distribution during testing from out-of-sample states. To address this, the authors propose two methods, OSR and OSR-v, which leverage a learned inverse dynamics model to regularize the policy ... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful comments. We provide clarification to your questions as below. We appreciate it if you have any further questions or comments.
**Q1:... more comprehensive ablation study...**
**Response:** Per your suggestion, we have conducted a more comprehensive ablation study, and ... | Summary: The paper proposes a solution to the problem of state distributional shift in offline RL - the agent takes unreliable actions in out-of-sample states during testing. The paper introduces the use of inverse dynamics models to guide the state recovery behavior of learned policy. Without constructing forward mode... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful comments. We provide clarification to your questions as below. We appreciate it if you have any further questions or comments.
**W1:It seems there is a non-negligible difference between the proposed method (theory) and the implementation. Eq. 7 and Eq.11 are not equival... | Summary: The paper addresses the issue of state distributional shift in offline reinforcement learning, where an agent tends to take unreliable actions when faced with unseen states during testing. The authors propose a solution to encourage the agent to follow the state recovery principle when making decisions. In add... | Rebuttal 1:
Rebuttal: Thank you for your comment. We appreciate your questions and provide clarification below.
**Q1: ... noisy dataset be mitigated to better reflect the distribution of out-of-sample states ...?**
**Response:** Before answering this concern, please note that modeling OOD samples is not our ultimate... | Summary: The authors tackle the state distributional shift problem in offline reinforcement learning, by learning to *recover* to states that are close to the in-distribution region, where the proposed method is named Out-of-sample State Recovery (OSR). They augment the offline dataset by generating new samples with Ga... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful comments. We provide clarification to your questions as below. We appreciate it if you have any further questions or comments.
**W1: The noise injection ... not be enough in more complex environments with more state dimensions...**
**Response**: We agree that Gaussian ... | Rebuttal 1:
Rebuttal: Thank you for all reviewers' thoughtful and constructive comments on our works discussing a significant but overlooked issue, state distribution shifting, on offline reinforcement learning.
In summary, our response includes the following aspects:
1.**[Efficiency of noise injection and OOD sampl... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning | Accept (poster) | Summary: This paper introduces A-Crab, an offline RL algorithm derived from ATAC, which incorporates a modified loss function for the Q-function. Instead of employing the square Bellman error used in ATAC, A-Crab utilizes the importance-weighted Bellman error. With this modified loss function, A-Crab effectively addres... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful and insightful comments. Below are our responses.
>Would it be possible for you to present empirical results on D4RL datasets that compare the performance of A-Crab with that of ATAC?
Yes. Please see the “global” response for details.
>The importance sampli... | Summary: The paper proposes an offline reinforcement learning algorithm called Actor-Critic Regularized by Average Bellman error (A-Crab). A-Crab modifies the pessimistic offline RL framework by replacing the usual squared TD error with an importance sampled TD error. Due to the linearity of the importance sampled TD e... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful and insightful comments. Below are our responses.
>The authors unrealistically assume the action space, the policy space, the importance sampling weight function space, and the value function space to be finite.
The finite cardinality assumption on all the ... | Summary: The paper introduces A-Crab, which combines marginalized importance sampling with the actor-critic paradigm to achieve optimal statistical rate in offline RL. Fm theoretical analysis, this algorithm is also more computationally efficient and relies on a weaker average notion of policy coverage compared to prio... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful and insightful comments. Below are our responses.
>The empirical evaluation of the algorithm is a major concern.
We provided empirical results to demonstrate the algorithm’s effectiveness. See the “global” response for details.
>In Line 224, where does the ... | Summary: This paper proposes a novel algorithm called A-Crab (Actor-Critic Regularized by Average Bellman Error) for offline reinforcement learning (RL) in complex environments with insufficient data coverage. The algorithm combines the marginalized importance sampling framework with the actor-critic paradigm and addre... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful and insightful comments. Below are our responses.
>The paper provides no empirical evaluations or demonstrations of the proposed algorithm. Neither does it shed light on the design of practical algorithms.
We showed empirical evaluation results to demonstrat... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their helpful and insightful comments. Below we first address common issues. Since all the reviewers mentioned that adding experimental results would make our theoretical results more solid and significantly enhance our paper, we compared our A-Crab algorithm to the ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Likelihood Ratio Confidence Sets for Sequential Decision Making | Accept (poster) | Summary: This paper proposed to use the likelihood ratio approach to provide an any-time valid confidence sequence, which is suitable for problems with well-specified likelihood. It discusses how to provably choose the best sequence of estimators and sheds light on connections to online convex optimization. To countera... | Rebuttal 1:
Rebuttal: Thank you for your comments. As you mention, the LinUCB analysis uses the radius of the confidence set to derive the overall regret bound for the bandit problem. Of course, we can do this with our confidence sets as well, and in the linear case, obtain immediately the optimal regret. Indeed, the p... | Summary: This paper examines the confidence set of an estimator, defined by a likelihood ratio. The contributions stated within the work, alongside my corresponding queries, are outlined as follows:
* For generalized linear models, we theoretically analyze the geometry of the LR confidence sets under mild assumptions. ... | Rebuttal 1:
Rebuttal: Thank you for your feedback on improving the clarity and coherence of our paper. We very much welcome the fact you see our contribution as substantial and we will use your insights to improve the exposition. The reason we separated the exposition of our first and third contribution is because we s... | Summary: The paper proposes to use likelihood ratios to construct confidence sequences that facilitate the downstream online decision making under uncertainty. The weighting and corresponding bias estimation are proposed to avoid regret blow-up in low-noise setting. The paper offers theoretical insights for the bias es... | Rebuttal 1:
Rebuttal: Thank you for your comments, we will try to polish notation and improve the readability of the manuscript. Now to specific concerns and questions:
**C:**: *"The experiment results shown in Figure 2 lack statistical significance."* **R:** We provide the same plot with the mean in the attach... | Summary: This paper proposes a new construction of confidence sets for a parametric setting, where the likelihood of the noise process is explicitly given.
The proposed method is based on a weighted variant of the sequential likelihood ratio (LR) statistics, which was proposed in universal inference (Wasserman et al., ... | Rebuttal 1:
Rebuttal: Thank you for your comments. Indeed you are right; this is a typo on our side. What we meant to say is if the $\log$ of the expression goes to zero (or the expression goes to 1), then only the true parameter $\theta^*$ is included in the set. We bear in mind here that the prediction game is played... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their time and effort spent. We are pleased that most reviewers see the benefits of our work. We hope that our individual responses clarify any misunderstandings and that the reviewers will consider raising their scores if they see fit.
We really believe t... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement. | Accept (spotlight) | Summary: By utilizing theoretical tools from quantum physics, the authors propose that a locally connected neural network can accurately predict data if and only if the data distribution exhibits low quantum entanglement under certain feature partitions. Based on this result, they develop a preprocessing method to enha... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We respond to your comments and questions below. If our response is satisfactory, we would greatly appreciate it if you would consider raising your score.
> *The numerical experiments are insufficient, it is only applied to randomly arranged data instead of original d... | Summary: The paper investigates criterion that make data distributions suitable for being accurately fit by neural networks using tools from tensor networks. Specifically, it shows that some locally connected neural networks (with polynomial activations) fit a data distribution accurately if and only if the quantum ent... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback, and in particular for describing our contributions as “significant progress on an important and difficult question”! We respond to your comments and questions below.
> *The presentation of the paper is rather dense in some places, which is understandable due ... | Summary: This paper focuses on the problem that which data distribution is more learnable by locally-connected neural networks such as CNN, RNN, and local-attention. The paper introduces the notation of quantum entanglement, theoretically proves and empirically verifies that the network can achieve accurate predictions... | Rebuttal 1:
Rebuttal: Thank you for your feedback, and specifically for noting the soundness of our theory and the clarity of our presentation. We respond to your comments and questions below. If our response is satisfactory, we would greatly appreciate it if you would consider raising your score.
> *The notion of ent... | Summary: This paper investigates the representation power of locally connected neural networks, a prevalent family of deep learning architectures, using tools from quantum physics. In particular, following the established equivalence between locally connected neural network and locally connected tensor network, the aut... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and support! We greatly appreciate your willingness to further increase your evaluation if your questions are addressed. We treat them below.
> *The paper's presentation could benefit from further improvement, especially in providing more qualitative discussio... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The fundamental question of what makes a data distribution suitable for deep learning is addressed in this study, focusing on locally connected neural networks. The study uses theoretical tools from quantum physics to tackle this problem. The main theoretical finding is that a specific type of locally connecte... | Rebuttal 1:
Rebuttal: Thank you for your feedback, and specifically for noting the soundness of our theory and experiments, as well as our account for background and related work. We respond to your comments and questions below. If our response is satisfactory, we would greatly appreciate it if you would consider raisi... | null | null | null | null | null | null |
Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy | Accept (poster) | Summary: This paper studies distributed mean estimation under privacy and communication constraints. This paper focuses on characterizing the recently defined notion of $f$-DP for communication-efficient mechanisms, where $f$-DP can be converted to the standard $(\epsilon,\delta)$-DP. The paper analyzed the $f$-DP of d... | Rebuttal 1:
Rebuttal: Dear Reviewer mVNo,
We appreciate your time in reviewing our paper and providing helpful comments. We believe that your concerns are due to misunderstandings. Different from existing methods (e.g., SQKR) which ignore the privacy amplification in compression, the proposed ternary compressor achiev... | Summary: This paper investigates the f-DP guarantee of several discrete-valued mechanisms in the local-DP model. In particular, closed-form expressions for binomial noise mechanism and Binomial mechanics are derived.
Then, the paper considers the popular problem of aggregating d-dimensional vectors from local users s... | Rebuttal 1:
Rebuttal: Dear Reviewer VYNX,
We appreciate your time and effort in reviewing and providing a positive evaluation of our work. Please find the point-by-point response to the comments below.
**Comment**: It's a bit hard to interpret/digest the closed-form f-DP guarantee of all these mechanisms. Have some p... | Summary: This paper analyses the privacy that is provided by stochastic rounding methods when doing distributed mean estimation with local differential privacy. It finds that they can contribute to the privacy guarantee thus achieving a better tradeoff.
It then "breaks" the privacy communication utility trade-off in t... | Rebuttal 1:
Rebuttal: Dear Reviewer Mzdj,
We appreciate your time in reviewing our paper and providing constructive comments. We believe that the concerns are mainly due to misunderstanding, please find our response below.
**Our contribution**: We would like to clarify that, as we discussed in the global response, ou... | Summary: In a federated setting where a server coordinates the collaborative analysis of multiple users with local data, communication efficiency and data privacy are two major issues of consideration. Classical DP mechanisms such as Laplace or Gaussian mechanisms add noises as real numbers -- at the same time, to save... | Rebuttal 1:
Rebuttal: Dear Reviewer 191s,
We appreciate your time and effort in reviewing and providing a positive evaluation of our work. Please find the point-by-point response to the comments below.
**Comment:** Are there any lower bounds on the tradeoff of the three considerations?
**Response:** We fully agree ... | Rebuttal 1:
Rebuttal: Dear Chairs and Reviewers,
The authors would like to thank you for your time in handling our paper and providing insightful comments and suggestions. We are happy to know that the reviewers find our study is of practical use (reviewer 191s), our proposed method is natural and simple to implement ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Sample Difficulty from Pre-trained Models for Reliable Prediction | Accept (poster) | Summary: It is known that state-of-art deep-network based machine learning models are poorly calibrated. It is also somewhat widely known that the poor calibration is because we do not model data uncertainty while training.
This work proposes to use CLIP pretrained model for estimating the sample difficulty, which when... | Rebuttal 1:
Rebuttal: Thank you for appreciating our convincing experimental study and strong evaluation. We address the detailed concerns below. We hope that you may find our response satisfactory and raise your score accordingly.
**Q1: Limited originality, such as example weighting and RMD:** We understand your view... | Summary: To address the over-confidence problem of the uncertainty estimation in deep learning, this paper for the first time proposes to use the pre-trained large models to estimate the learning difficulty of each sample. Then, the estimated sample difficulty information is embedded in the final loss for training deep... | Rebuttal 1:
Rebuttal: Thank you for appreciating our essential problems and new contributions and providing valuable comments. We address the detailed concerns below. We hope that you may find our response satisfactory and raise your score accordingly.
**Q1: why RMD is better than MD?** The key difference between RMD ... | Summary: The authors propose to improve model calibration by leveraging information about a sample's difficulty. To do this, they cluster samples using embeddings obtained from large, pre-trained models, and then use a sample's distance to samples from the same class as proxy for difficulty. They show improvements on i... | Rebuttal 1:
Rebuttal: We thank you for finding our work novel and clarifying empirical results. We address the detailed concerns below. We hope that you may find our response satisfactory and raise your score accordingly.
**Q1-a: a big fuzz about using the RMD, even though simple K-means clustering already helps** It ... | Summary: This paper proposes a difficulty-aware uncertainty regularization approach, which first pre-defines the difficulty of each training sample and then differently regularizes training samples during training. To quantify the sample difficulty, the authors utilize pre-trained large models like CLIP to extract feat... | Rebuttal 1:
Rebuttal: We thank you for finding our work clearly motivated and clarified, as well as providing valuable suggestions to further improve our paper. We address the detailed concerns below, by including the suggested standard deviation, a new experiment to further demonstrate the applicability beyond CLIP, a... | Rebuttal 1:
Rebuttal: Firstly, we would like to express our gratitude for the thoughtful reviews, which help to further improve our paper. We are pleased that the reviewers found our paper to be **novel (innovative)** (Reviewers FmxN, YeMZ, Lxfs), **well-written and convincing** (All), **straightforward to understand**... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In settings where deep neural networks are used for critical tasks, it is crucial to ensure that they are calibrated and capable of reliable predictions. It is desirable to have the ability to measure the confidence of the model's predictions and reject those that have high uncertainty. To achieve this, the au... | Rebuttal 1:
Rebuttal: We thank you for finding our work innovative, well-written and straightforward to understand, and showing convincing validation, as well as providing valuable comments. We answer the specific questions below. We hope you will find our response satisfactory and raise your score accordingly.
**Q1: ... | null | null | null | null | null | null |
Structured State Space Models for In-Context Reinforcement Learning | Accept (poster) | Summary: The authors propose a modification to S5 that enables "resetting" the recurrent state, allowing it to function as an RNN replacement in RL. They update the scan operator to utilize the `done` flag and use this to reset the recurrent state. They evaluate the resettable S5 on a portion of the POPGym suite and th... | Rebuttal 1:
Rebuttal: We would like to first thank the reviewer for their detailed and technical review. We are glad that the reviewer finds our approach promising and the experiments fair and broad.
### On the Reset
>the reset, the main contribution of the paper, appears to be a trivial change to S5
It’s not immedi... | Summary: This paper investigates the effectiveness of structured state-space sequence (S4) models and in particular its variant S5 in reinforcement learning settings. To apply S5 to reinforcement learning, the authors propose a modified associative operator that handles episodic resets, allowing S5 to train over sequen... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback. We are happy to hear that the reviewer agrees that we “demonstrate the effectiveness of the S5 model for partially-observed and meta-RL tasks, both in terms of asymptotic performance and training/inference speed” and that “the meta-RL environmen... | Summary: Structured state space sequence (S4) models deliver good performance on long-range sequence modeling tasks, fast inference speed and parallelize training, making them suitable for many RL settings. The authors propose a modification to the recently proposed S5 architecture and apply it to RL tasks. Their propo... | Rebuttal 1:
Rebuttal: We would like to first thank the reviewer for their extremely thorough review. We are glad that the reviewer finds that investigating S4-like models for RL is an important contribution, especially since they have not been widely-adopted or thoroughly investigated in RL.
### In-Context Learning an... | Summary: The authors propose a modification of the S5 sequence architecture with a resettable hidden state that leads to a drop-in replacement of RNNs and Transformers in partially observed / memory-intensive RL tasks. They show that S5 exceeds baseline performance while being more computationally efficient to train th... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their well-thought review. The reviewer brings up many good points that we would like to address.
Firstly, we are glad that the reviewer finds that our paper clearly demonstrates that resettable S5 can replace LSTM’s and Transformers and that our proposed ... | Rebuttal 1:
Rebuttal: We are grateful to the reviewers for their insightful feedback. We appreciate the consensus that the proposed S5 architecture offers **clear advantages over standard RNNs and Transformers in partially-observed RL, both in terms of performance and runtime.** This is the key takeaway of our work, an... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Adaptive Selective Sampling for Online Prediction with Experts | Accept (poster) | Summary: This paper presents an adaptive label-efficient forecasting technique for online binary prediction with expert advice. The proposed approach implements a label querying probability that is a function of the observed scenario, rather than based on pessimistic conditions. This enables the method to adapt, i.e., ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and helpful comments.
*Regarding general losses and predictions:*
As you mention, applying the same approach to general losses would quickly lead to complications, but generalizing the analysis is an intriguing direction.
Please see Point 2 in the ... | Summary: The paper considers a binary prediction game on $0-1$ loss, it proposed efficient sampling scheme via an modification of the exponentiated weight forecaster, which selectively acquire labels $y_t$ based on $Ber(q_t)$, where the design of $q_t$ is correlated to the disagreement among experts’ predictions at eac... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and helpful comments.
*Regarding the assumption that $\Delta>0$:*
Please see Point 1 in the global response, where we also highlight the more general assumption in Appendix F.
Note that, under the assumption stated in the main paper, it is allowed
... | Summary: This paper proposes an interesting novel approach to prediction with expert advise. In the standard prediction with expert advise setup, the learner receives experts' predictions, commits to its own and then sees the true outcome as produces by the (possibly adversarial) nature. Suppose that obtaining the true... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and helpful comments.
*Regarding the restrictiveness of the conditions for the label complexity bound:*
While the assumed setting in Theorem 3 is relatively benign, it includes many relevant settings, and the results hold under the more lenient ass... | Summary: This paper investigates the PEA problem in the context of online binary classification where the cost of obtaining labels for streaming data is high, necessitating selective label collection adaptively. To this end, the authors introduce a carefully designed label collection strategy based on the classical Hed... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and helpful comments.
*Regarding the assumption in Theorem 3:*
The assumed setting is relatively benign, but it does include many practical i.i.d. settings, and the stated results do hold under a more general assumption (as detailed in Appendix F).... | Rebuttal 1:
Rebuttal: ## Global response to all reviewers
We thank all reviewers for their careful reading and helpful comments.
We are happy that you consider the paper to be sound, novel, interesting, and well-written.
Below, we address three points that were raised by multiple reviewers:
*1. Regarding the assumpt... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Training-free Diffusion Model Adaptation for Variable-Sized Text-to-Image Synthesis | Accept (poster) | Summary: This paper proposes both an analysis and a contribution to fix a problem found during the analysis.
They start with the premise that diffusion models should be able to generate arbitrary size images, and training specialized models for each image size is too expensive, which is correct. Using diffusion models... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. We really appreciate your interest and support in our paper.
**Q1**: Relation between time complexity and the new scaling factor.
**A1**: Yes, it needs constant O(1) time complexity to calculate the new scaling factor.
---
Rebuttal Comment 1.1:
Comment: I've ... | Summary: This paper analyzes the issues of using a fixed-resolution diffusion model to generate varied-size images and proposes a scaling factor to stable the attention entropy which remedies the issue. The method is evaluated on text-to-image models when the inference resolution is moderately different than the model ... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful comments. We will explain your concerns point by point.
**Q1**: Comparison to candidate methods.
**A1**: We would like to point out the advantages of our method (modifying fixed-resolution models) against other methods (e.g. cascaded diffusion models and LIIF) in three... | Summary: This work adapts a pre-trained Stable Diffusion model for variable-resolution image generation. Since Stable Diffusion is trained on a fixed image resolution, naively varying the output size results in abnormal patterns in the images. This paper tracks the problem down to self-attention weights in the denoiser... | Rebuttal 1:
Rebuttal: Thanks for your careful and valuable comments. We will explain your concerns point by point.
**Q1**: Comparison to baselines.
**A1**: We would like to point out the advantages of our method (modifying fixed-resolution models) against other methods (e.g. cascaded diffusion models and LIIF) in thr... | Summary: In this paper, the authors propose a new scaling factor for attention based text-to-image generative models in order to handle variable sized generations. The authors establish the relationship between attention entropy and token size and use this newly found relationship to design a scaling factor that takes ... | Rebuttal 1:
Rebuttal: **Q1**: More explanation on Equation 5, 6 and 7.
**A1**: In Equation 5, we derive the static relationship between attention entropy $A_{i}$ and token number $N$, i.e. $Ent(A_{i}) = \log N - \frac{1}{2} \lambda^{2} C + O(1)$, where $\lambda$ is the scaling factor and $C$ is a constant number unrel... | Rebuttal 1:
Rebuttal: Thanks for all the reviewers and AC for your time and valuable comments!
This comment is followed by the PDF page with new figures to support our views in information richness.
Pdf: /pdf/bebc28b02cbd13eb4b5f18b0f661ca9e7e03f13e.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data | Accept (poster) | Summary: This paper introduces MuSe-GNN, a model for learning gene embeddings from single-cell sequencing and spatial transcriptomic data that is based on multimodal machine learning and deep graph neural networks. While incorporating regularization with weighted similarity learning and contrastive learning to learn c... | Rebuttal 1:
Rebuttal: We thank the reviewer for the supportive comments. The detailed response to each point is as follows.
**1. What is the difference between this paper and the Geneformer [1]. What about the comparisons of the performance of the experiments if they can be compared together?**
We appreciate your que... | Summary: The authors proposed a novel deep graph neural network, named MuSe-GNN, to learn gene representations from single-cell sequencing and spatial transcriptomic data.
The idea is to construct gene graphs from single-cell sequencing and spatial transcriptomic data, and then learn gene embeddings via cross-graph tr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the supportive comments. The detailed response to each point is as follows.
**1. This idea is not new, and the models they used are all well established. Lack of comparison with related works. Lack of ablation studies.**
We appreciate your comments concerning the nove... | Summary: This paper addresses the heterogeneity problem in the gene representation learning from multi-context biomedical sequencing profiles. Specifically, gene connections are formulated through co-expression network, then the proposed MuSe-GNN utilizes cross-graph Transformer to generate gene embeddings, while desig... | Rebuttal 1:
Rebuttal: We thank the reviewer for the supportive comments. The detailed response to each point is as follows.
**1. The novelty of the proposed method is limited.**
We appreciate your inquiry concerning the novelty of our work, which we address in the following from two aspects.
The first aspect pert... | Summary: The paper describes an approach that combines Multimodal Machine Learning and Deep Graph Neural Networks to learn gene representations from multi-omics and multi-tissue data. The main issue that this paper tries to address is that existing approaches fail to obtain gene representations that are consistent acro... | Rebuttal 1:
Rebuttal: We thank the reviewer for the supportive comments. The detailed response to each point is as follows.
**1. It seems that only the hyper-parameters of MuSe-GNN were tuned, while other competitors were not.**
We appreciate your question. We have mentioned the details of parameter tuning for other ... | Rebuttal 1:
Rebuttal: # General response
We would like to thank the reviewers for their overall positive comments on the aims of our manuscript, as well as their insightful comments and inquiries.
We appreciate all the reviewers for their comments about the strengths of MuSe-GNN, including the importantance of the to... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Reversible and irreversible bracket-based dynamics for deep graph neural networks | Accept (poster) | Summary: This paper provides a unified framework inspired the bracket-based dynamical system to analysis the oversmoothing problem in GNN. The past work may leverage the opposite physics concept such as the reversible processes, irreversible process and therefore it is not clear how such concept help to design GNNs, wh... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We are happy that you have enjoyed our paper and that you appreciate the results of the physics-based simulations. Below are responses to your specific concerns in the “Weaknesses” section:
1. *This paper may be a little hard to understand for the reader wi... | Summary: This work provides a comprehensive overview of graph attention networks (GATs), shedding light on their fundamental concepts and principles. Additionally, the authors introduce a set of novel GNN architectures that leverage structure-preserving bracket-based dynamical systems. By incorporating these systems in... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We are glad that you appreciate our analysis of graph attention networks in the context of the graph exterior calculus, as well as our interpretation of the attention mechanism as a learnable inner product on features. Please see below for responses to your ... | Summary: The work proposes structure-preserving bracket-based dynamical systems to learn physical systems using GNNs. The authors proposed four formulations depending on completeness and character (conservative or dissipative). The models are demonstrated to be effective in physical system and node classification tasks... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We are glad that you appreciate the idea of attention as a learnable inner product, and the versatility of our bracket-based architectures in capturing physical principles and performing a variety of tasks.
We believe the primary weakness mentioned in your re... | Summary: This paper proposes a bracket-based dynamical system framework to design structure-preserving graph neural networks (GNNs). Specifically, the authors leverage four formalisms: Hamiltonian, Gradient, Double-Bracket, and Metriplectic, that model physical systems with different completeness and dissipation charac... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We are happy to hear that you enjoyed our bracket-based parameterizations and that you appreciate the analysis of existing GNN architectures in terms of this framework. Your weakness related to the clarity of the exposition has been well received: we plan to... | Rebuttal 1:
Rebuttal: Thank you all for your insightful comments and useful feedback on our work. We have heard the shared criticism that (1) parts of our manuscript could be difficult to understand for a general machine learning audience, and (2) that the role of depth in our architectures could be made clearer in th... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Reference-Based POMDPs | Accept (poster) | Summary: This paper presents a method to solve POMDPs by considering a reference policy. The solution of a reference-based POMDP is presented. Then the existence and uniqueness of the solution are proved. Then the author shows the connections between a reference-based POMDP and a standard POMDP. In the end, an online p... | Rebuttal 1:
Rebuttal: Thank you for the positive review and for your questions.
Re “Weaknesses: purpose of theorem 3.1”. This is the main Theorem in this paper and is a straightforward extension of the LS-MDP of Todorov (see eq 31 and 32) to the POMDP. It’s main point is to demonstrate that: 1) the Bellman equation f... | Summary: The paper proposes to regularize online policy search in POMDP by providing a reference stochastic policy. The idea is illustrated on two synthetic grid domains.
Strengths: The paper attempts to systematically incorporate prior knowledge to improve online POMDP planning.
Weaknesses: 1) The proposed approach ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to provide feedback. See responses below.
On “ The approach is not new – the reference policy is what is known …” We respectfully disagree.
First, we would like the clarify the main contributions of the paper by referring you to the general comments of our rebuttal... | Summary: The authors propose reference based POMDPs, where the agent needs tradeoff between achieving environment rewards and following a reference policy.
Strengths: generally, I think the reference based pomdps are a good idea and can solve some real world problems;
Weaknesses: see below
Technical Quality: 3 good
... | Rebuttal 1:
Rebuttal: Thank you very much for your review and feedback.
Re Question 1. It should be $\pi(a, o | b)$ or equivalently $\pi(b’ | b)$. By definition of the problem, one chooses belief-to-belief distributions over next beliefs (or, equivalently, action-observation pairs). This is a relaxation of the conce... | Summary: The paper introduces and investigates the concept of reference-based POMDP, which addresses the challenge of finding an optimal policy in partially observable environments. The main objective is to simplify this problem by leveraging a baseline fully observed policy. The authors propose that solving a referenc... | Rebuttal 1:
Rebuttal: Thank you very much for your review and feedback.
RE Weaknesses. We will provide additional related work, in relation to Max Entropy RL and stochastic control on top of those already provided. The paper is inspired by an interesting series of papers by Todorov (see ref 18-20), namely Linearly So... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their contributions and respond to some general points here.
First, we would like to emphasise again the key contributions of this paper. The major contribution of the paper is that we propose a reformulation of POMDPs, such that the optimisation for... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper is well written, clear and concise, with a well defined scope and goal.
This work extends prior work on Linearly Solvable MDPs (LS-MDPs) to the partially observable setting. LS-MDPs are alternate decision processes whose control paradigm is shifted (w.r.t. standard MDPs) such that the system define... | Rebuttal 1:
Rebuttal: Thank you very much for the positive review and feedback.
Just as a clarification, you mentioned in your review that the extension of LS-MDPs to partially observable domains is “very straightforward”. We do note however that there are some interpretive challenges of formulating “belief-to-belie... | null | null | null | null | null | null |
Cal-DETR: Calibrated Detection Transformer | Accept (poster) | Summary: The paper proposes a method to improve the calibration performance of transformer-based object detectors. In their approach, they first present a way to quantify the uncertainty of each logit using the variance of the outputs of different transformer decoder layers. Then, with the motivation that a higher unce... | Rebuttal 1:
Rebuttal: **Q1: I think the logit mixup strategy that the authors introduce is quite related to a cited work [36]… I think this is still an important contribution but I'd like to see an explicit discussion...**
**A1:** Thanks for finding the logit mixup strategy an important contribution. Our approach can... | Summary: This paper proposing a mechanism for calibrated detection transformers (Cal-DETR), particularly for Deformable-DETR and UP-DETR, which consists of quantifying uncertainty, an uncertainty-guided logit modulation and a logit mixing approach. Results show the method improves the baselines in calibrating both in-d... | Rebuttal 1:
Rebuttal: **Q1: Will the method still be effective when using a strong or even a sota model, like DINO?**
**A1**: As suggested by the reviewer, we provide the results on COCO (in-domain) and CorCOCO (out-domain) with the DINO model, as shown below. Our **Cal-DETR** improves the calibration performance of ... | Summary: This paper focuses on performing calibration for DETR, particularly for Deformable-DETR and UP-DETR. The authors first propose an approach for quantifying uncertainty in DETRs, which is built from the variation in the output of decoder layers. Then they develop an uncertainty-guided logit modulation mechanism ... | Rebuttal 1:
Rebuttal: **Q1: The proposed method has limited novelty. Although the authors claim…**
**A1:** To the best of our knowledge, this is the first work that strives to improve the calibration performance of recent SOTA ViT-based detectors by proposing an uncertainty-guided logit modulation and a logit mixing a... | Summary: This paper proposes a calibrated detection transformer model, which equips the DETR variants with an uncertainty-guided logit modulator and a mixup augmentation for the classification branch of the detector. Specifically, the authors first quantify the uncertainty with the variance among the predicted logits f... | Rebuttal 1:
Rebuttal: **Q1: It is unclear why the variation in the output of decoder layers can measure the uncertainty of the class prediction…**
**A1:** Decoder layer contains multiple dropout layers that make it stochastic in nature and so allows capturing variation in logit space to estimate uncertainty. Logits ar... | Rebuttal 1:
Rebuttal: We thank the reviewers (wX9Z, 95V6, co8a, Gc9J) for the positive and thoughtful feedback, and we appreciate the comments to improve our work.
**Reviewer Gc9J:** "The proposed method does not require an extra hold-out validation set, as it is a training time approach. Improvement in the calibratio... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Binary Classification with Confidence Difference | Accept (poster) | Summary: This paper studies binary classification problems. A new data type is introduced, where each observation consists of two input instances, together with the "confidence difference," defined as the difference of the conditional probabilities (output=1|input) of the two input instances. New loss functions are int... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. We are encouraged that you agree with the novelty and contributions of our paper. Below are the answers to your questions.
***
**Q1: Difference in experimental design between this paper and [11].**
**A1:** In [11], they only used examples from ${(+1, +1),(+1, -... | Summary: The paper discusses weak supervised learning for binary classification, specially in the setting where labeled data is not available. Based on pairwise-comparison (Pcomp) confidence, where we are given data pair $(x_1, x_2)$ and binary label {+1, -1} of $x_1$ being more or less probable of being positive compa... | Rebuttal 1:
Rebuttal: First, we are very grateful for your time and effort in reviewing this paper. Below are the responses to your questions and comments.
***
**Q1: The claim that the confidence difference has a lower bias is not well justified.**
**A1:** Thank you for your comment. As discussed in the Introduction ... | Summary: - This work proposes a confidence label based training approach called _ConfDiff_ for binary classification models as an improvement over traditional hard labels. Specifically, authors argue that obtaining hard labels for traditional supervised learning paradigm, or confidence metrics around positive labels fo... | Rebuttal 1:
Rebuttal: First of all, we are very grateful for your time and effort in reviewing this submission. We are encouraged that you agree with the contributions of our paper. Below are the responses to your comments.
***
**Q1: Add experimental results from the vanilla ResNet.**
**A1:** We agree with you that ad... | Summary: The paper proposes to solve a classification problem using weakly supervised learning problem called confidence-difference (ConfDiff)
classification, where unlabeled data pairs are equipped with confidence difference specifying the difference in the probabilities of being positive. The authors further develop ... | Rebuttal 1:
Rebuttal: First, we would like to thank you for your time and effort in reviewing our submission. Next, we would like to respond to the main concerns raised in the comments.
***
**Q1: Comparison with supervised learning based on ordinary labels.**
**A1:** We agree with you and list the performance of the s... | Rebuttal 1:
Rebuttal: First of all, we sincerely thank all the reviewers for their great efforts in reviewing this submission and providing helpful and valuable comments. Since we cannot revise our paper during the rebuttal period, we plan to make the following revisions in our paper:
- According to Reviewer Qs9H and ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography | Accept (poster) | Summary: This paper presents CRoSS, a novel image steganography framework leveraging text-driven diffusion models. It offers improved security, robustness, and controllability compared to cover-based methods.
Strengths: CRoSS is the first work to introduce diffusion models to image steganography and achieves these be... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments! If there are any remaining questions that have not been adequately addressed, please feel free to continue the discussion with us.
> ***We earnestly request you to reconsider your assessment of our work, taking into consideration different aspects.***
- ... | Summary: This paper addresses coverless image steganography by taking the prompt as the guidance to generate stego images using Stable Diffusion. It shows better controllability with language-driven model, better robustness and security with stronger generation power of diffusion probabilistic model. Experimental resul... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments! If there are any additional comments to be added, please continue the discussion with us.
> ***Weakness #1: The novelty is limited.***
- Our contributions are primarily demonstrated in the following aspects. $\textbf{\textcolor{red}{These major contribu... | Summary: This paper introduces diffusion models to the field of image steganography. It argues the significant advantages in controllability, robustness, and security compared to cover-based image steganography methods. It utilized the power of Stable Diffusion to translate between two images without training. This pap... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments! We hope that our rebuttal has addressed all your concerns. If there are still aspects that need further clarification, please feel free to continue the discussion with us!
> ***Weakness #1: The invertibility is not perfect, and how to address the artifact... | Summary: In this work, the authors introduce an image steganography framework (CRoSS) that leverages the properties of diffusion models to enhance the security, controllability, and robustness of the steganography process. The authors show how the diffusion model can integrate with image steganography to achieve these ... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments! We hope that our response addresses all of your concerns. All discussions and supplementary experiments will be included in our revised version. If there are any remaining questions that have not been resolved, please feel free to continue the discussion w... | Rebuttal 1:
Rebuttal: We sincerely appreciate all the constructive comments from the reviewers! Below is our brief overall response.
> ***Firstly, we are delighted to observe that the reviewers have acknowledged various aspects of our work:***
- Reviewer mtD2 and Reviewer Nci3 hold a $\textbf{\textcolor{red}{positiv... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
High Precision Causal Model Evaluation with Conditional Randomization | Accept (poster) | Summary: The authors formulate and evaluate an approach to solving a non-standard problem: evaluating a causal model M when additional data (not used to construct M) is available from a non-randomized experiment. In particular, the authors focus on comparing IPW estimates from the non-RCT data and from the inferences o... | Rebuttal 1:
Rebuttal: Thank you very much for your review. Below we address your question respectively.
> Some diagnostic tests are in order to increase confidence in the output of the pairs estimator. For example, you could introduce noise into the model’s estimates and see if the estimated error increases.
Thank yo... | Summary: This paper proposes a new estimator for the causal error, that achieves lower variance than previous approaches.
The estimator consists in the difference between a IPTW-like estimator using the causal model and a direct IPTW causal effect estimator.
The paper shows that under clear assumptions, this estimator ... | Rebuttal 1:
Rebuttal: Thank you very much for your positive opinions for our work. We are pleased to see that you appreciate our efforts in technical soundness, presentation, as well as evaluations.
> The main theoretical comparison of this paper seems to be the naive IPTW estimator. As the authors state in the relate... | Summary: This paper constructs a new estimator for IPW evaluation by comparing the IPW estimator applied on the model-predicted treatments versus the observed treatments. The paper presents a theoretical result that this estimator has lower variance than the naive one and aims to demonstrate this via empirical experime... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review and valuable suggestions to improve.
> The paper could benefit from more clarity in the writing. For example, in line 124, it would be great if there can be some intuition or example on when P(T=1|X) is skewed and what that means (is it overfitting or misspec... | Summary: This work aims to evaluate the fidelity of causal models in estimating true treatment effects across different treatments. The golden approach involves comparing treatment effects derived from the target causal model and those obtained from Randomized Controlled Trials (RCT). Practical, time, cost, and ethical... | Rebuttal 1:
Rebuttal: Thank you so much for your constructive and positive feedback to our paper. We would address your comments below.
> Despite their approach being supported by the theoretical results and extensive...the authors have not provided an appendix.
We apologize for the missing appendix, this is due to ... | Rebuttal 1:
Rebuttal: We thank the reviewers for your encouraging review and valuable suggestions to improve. We acknowledge that the reviewers highlighted the **effectiveness and simplicity of our approach (PdWH, r45Q, t8vT, yvJa), novelty or soundness of our results (PdWH, zDTG, t8vT, yvJa), extensiveness of experi... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper considers the estimation of causal error using the IPW estimator in conditional randomized experiments. Given that the allocation probabilities are readily available in these types of experiments, IPW estimators are often used. The authors propose to use the same IP weights for both the causal predic... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback and suggestions. Below, we respond to each of your comments.
> Often the IP weights are used in conjunction with an OR model to construct a DR estimator. In fact, there is really no good reasons to use the naive IPW estimator considered by the authors.
We ack... | null | null | null | null | null | null |
Generalization in the Face of Adaptivity: A Bayesian Perspective | Accept (spotlight) | Summary: This paper explores the problem of adaptive data analysis - when a single dataset is repeatedly used for adaptively chosen queries, overfitting can occur rapidly. To reduce this bias, a popular approach is to add noise to the output of each query. Intuitively, this prevents the analyst from learning too much a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the useful feedback.
We appreciate the suggestions on how to make the presentation of pairwise concentration more accessible. We felt a tension between simplicity versus presenting the more general notion, which may have broader consequences and applications beyond the u... | Summary: This paper makes progress in adaptive data analysis by providing a better analysis of the Gaussian mechanism (for answering statistical/linear/counting queries), and showing that adding Gaussian noise ensures generalization error that scales with the **variance** of the queries.
Previously, the differential-p... | Rebuttal 1:
Rebuttal: We thank the reviewer for the useful feedback.
The restriction to linear queries is indeed a limitation of the current work, and we hope to extend the results beyond linear queries in future work, as we briefly mention in the discussion.
Thanks for your comment about wanting more discussion/intu... | Summary: Overfitting can occur when a single dataset is used for several statistical tasks. It is known the application of additive noise techniques from differential privacy can prevent overfitting in a variety of statistical settings. The issue is that traditional mechanisms from differential privacy, which are inher... | Rebuttal 1:
Rebuttal: We thank the reviewer for the useful feedback.
Regarding the first point under ``weaknesses'': Our results can handle queries of differing variances, as long as the mechanism has access to bounds on their variances. In such a case, the mechanism can scale the added noise at each iteration $\eta_{... | Summary: In this paper, the authors generalize and improve the sample complexity that scales with variance compared to the sensitivity/range in the state-of-art DP result for adaptivity guarantee based on a novel definition of pairwise concentration (PC). The paper demonstrates such generalization in both bounded queri... | Rebuttal 1:
Rebuttal: We thank the reviewer for the useful feedback.
Regarding the first point under ``weaknesses'': note that the case of $\alpha \ge \frac{\Delta}{2}$ is trivial, since it can trivially be achieved by $r = \frac{1}{2} \left(\underset{x \in \mathcal{X}}{\min} \left(q(x) \right) + \underset{x \in \math... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Object-centric Learning with Cyclic Walks between Parts and Whole | Accept (poster) | Summary: The paper studies the problem of unsupervised object-centric learning. Previous methods usually leverage reconstruction loss as the supervision signal. The authors propose a novel method that leverages a contrastive cyclic walk loss instead, which was originally proposed for learning correspondence between pix... | Rebuttal 1:
Rebuttal: **oB3o.1 - Weaknesses: Without the reconstruction loss, the pipeline may lose most of the information on objects that are useful for downstream tasks. The paper does not conduct relevant experiments to explore this point, which is also crucial for evaluating object-centric representation learning ... | Summary: This paper introduces Cyclic Walks, an approach to obtain object-centric representations from images. The idea is to adapt contrastive random walks used for learning spatiotemporal correspondences for learning slots without a slot decoder. The other key ingredient of this approach is to use a frozen unsupervis... | Rebuttal 1:
Rebuttal: **VTwm.1. Weaknesses: Missing a baseline: I would like to see Cyclic Walks compared against KMeans clustering on the frozen DINO feature tokens with K equal to the number of slots used in the Cyclic Walks approach. This simple baseline would inform the extent to which the learned Slot Attention mo... | Summary: This paper works on unsupervised object discovery, that is, learning to decompose the compositional components of a scene. Based on frozen DINO features, it proposes to introduce random walks between part-level features (dense output of DINO) and object-level features (output of SlotAttention). Each object-lev... | Rebuttal 1:
Rebuttal: **8Kba.1 - Weaknesses: Kindly suggest for direct comparison with the following related works.**
We now include the following results of all the methods suggested by the reviewer on Pascal VOC 2012 and COCO-stuff27 in the table below. We are unable to compare with Odin, since Odin is a self-superv... | Summary: The paper presents a method for unsupervised object discovery, while using cyclic walks between part and whole features as a supervision signal. While previous methods mainly use RGB or feature reconstruction as supervisory signal, this method uses a form of contrastive learning. Their lack of decoder archite... | Rebuttal 1:
Rebuttal: **38HN.1 - Questions: How does the method compare against reconstruction methods on the original CLEVR or ClevrTex dataset, is anyone fine?**
As suggested by the reviewer, we conducted experiments on the ClevrTex dataset and evaluated the performance of all baseline methods and our method. In ter... | Rebuttal 1:
Rebuttal: We thank the reviewers for feedbacks and suggestions. We present the figures in PDF and the responses to individual reviewers’ questions below. The original questions from the reviewers are copied and bolded.
Pdf: /pdf/15e00fd08624d1487de3a7ab91122cc722cb91da.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression | Accept (poster) | Summary: The authors first prove that the excess risk of multinomial logistic regression with under subgaussian data distribution is lower and upper bounded by terms involving the ratio of the Fisher information of the unlabeled data and the Fisher information of the labeled data.
The authors then propose an algorithm ... | Rebuttal 1:
Rebuttal: We thank reviewer PwwV for the review and comments!
* **Not easy to read:**
We acknowledge that it might be hard to comprehend Section 4.3 for readers unfamiliar with regret minimization. We have tried explain the overal merit of our approach while leaving most technical details in Appendix. We ar... | Summary: This paper studies active learning when the underlying data distribution follows the multinomial logistic model. This paper considers the pool-based setting and designs an algorithm to select the sample to query in a batch fashion. There are two main contributions: (1) The paper shows that the excess risk is l... | Rebuttal 1:
Rebuttal: We thank reviewer B8jR for the review and comments!
* **Novelty of Theorem 3 compared to [11]:**
* Our primary contribution concerning Theorem 3 in comparison to the work presented in [11] is the generalization to sub-Gaussian distributions for the points. The proofs in [11] rely on an assumption... | Summary: This paper proposes a novel active learning algorithm called FIRAL for pool-based active learning in multinomial logistic regression. The paper investigates the theory and algorithms for pool-based active learning and compares FIRAL to other active learning methods in terms of classification error. The authors... | Rebuttal 1:
Rebuttal: We thank reviewer zZMa for the review and comments!
* **Introduction on related work:**
We will add more discussion on related work in the new version.
* **Classifier choice:**
We used the multinomial logistic regression classifier in order to be able to conduct the theoretical analysis. We beli... | Summary: This paper develops a pool-based active learning method for multinomial logistic regression, following a long line of work using the Fisher Information Ratio as a criterion for active set selection. The paper establishes FIR to tightly (within constants) characterize excess risk (Theorem 3), so that this can b... | Rebuttal 1:
Rebuttal: We thank reviewer C5TF for the review and comments!
* **Relaxation problem:**
The reviewer is correct. The constraint on Equation (14) should be $z\in[0,1]^m$. We are sorry for the confusion caused by the expression. We will fix it in the new version.
* **Why use FTRL for sparsification problem?:... | Rebuttal 1:
Rebuttal: # General Response:
We thank the reviewers for their careful read of our paper, their comments, and their suggestions. We have fixed all the typos mentioned in the reviews. We have submitted responses to individual researchers. We also submitted a PDF with additional results.
There were two issu... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors present theory and algorithms for training multinomial logistic regression models in the pool-based active learning setting; how we should choose $b$ extra points to label from a pool of unlabeled ones, so that when we train a model including the newly acquired labeled points the excess risk of the... | Rebuttal 1:
Rebuttal: We thank reviewer 3UFt for the review and comments!
* **On the algorithm complexity:**
Please see our general response.
* **Experiment on pretrained frozen embeddings:**
We compared various active learning methods on CIFAR-10 using frozen pretrained embeddings with a dimension of 512. The resu... | null | null | null | null | null | null |
Direction-oriented Multi-objective Learning: Simple and Provable Stochastic Algorithms | Accept (poster) | Summary: This paper proposes a gradient manipulation method named SDMGrad for multi-task learning (MTL). SDMGrad improves the previous MGDA method by using two constraints. The first one is to constrain the common descent direction nearby the one computed with a specific preference (such as the average direction), whic... | Rebuttal 1:
Rebuttal: **Answer to Q1** We would like to clarify the novelty of our algorithmic designs and the difference from previous works.
1. As we discussed in lines 152-158, optimizing the constraint-based regularization (see eq. (7)) in CAGrad involves the evaluations of product $\\|h_0\\|\\|g_w\\|$ and the rati... | Summary: The contributions are as follows:
- First, this work gives a new framework of direction-oriented multi-objective optimization.
- Second, they propose an algorithm, SDMGrad (and an objective sampling version when the number of objectives is large)
- Third, they give a convergence analysis for their algorithm an... | Rebuttal 1:
Rebuttal: **Q1. Could the authors share some examples where this direction based regularization would be useful?**
**A:** This direction based regularization would be useful if one target is to minimize a specific objective function, e.g., the average loss in MTL. A more specific example is provided in A.1... | Summary: Authors presented a new stochastic gradient method for multi-objective optimization (MOO) problem, called SDMGrad.
Compared with previous SMG, they claimed SDMGrad dose not need to increase the batch size linearly with the iteration numbers.
Compared with previous MoCo, they claimed SDMGrad could be applied in... | Rebuttal 1:
Rebuttal: **Q1. Could you detail the advantages and novelty of SDMGrad over MoCo? First, Line 5 in Algorithm 1 seems similar with Equation 4 of MoCo. Second, authors commented MoCo "a relatively strong assumption that the number T of iterations is much larger than the number K of objectives." Well, in the r... | Summary: This paper proposes a direction-oriented multi-objective gradient descent algorithm under stochastic gradient settings. The authors show that the algorithm can benefit from the direction-oriented mechanism and ensure optimal convergence. In addition, an objective sampling strategy is applied to the proposed al... | Rebuttal 1:
Rebuttal: **Q1. There is no respective analysis of the direction bias in the analysis.**
**A:** Our analysis of the direction bias can be found in Proposition 1. It can be seen that the bias is upper-bounded by an exponentially decaying term $4C_g^2(1-2\beta_t\rho)^S$ plus two small terms $\frac{\beta_tC_g... | Rebuttal 1:
Rebuttal: **To all reviewers:**
We thank all reviewers for their time and valuable comments! Based on the reviewers’ suggestions, we have added the following additional experiments:
1. Comparison to additional baselines including SGD, unitary scalarization, RLW, IMTL, and NashMTL.
2. Running time compariso... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces the stochastic direction-oriented multi-objective gradient descent (SDMGrad) and SDMGrad-OS (OS stands for objective sampling). The idea of SDMGrad is to make the objective “direction-oriented”, which is done by regularizing the descent direction towards a specific direction. This directi... | Rebuttal 1:
Rebuttal: **Q1. Need to tune lambda to get better results. It does not seem to be easy to pick a good starting point as the algorithm is not very robust to the choice of lambda. Also, it is not clear how to choose $\rho$.**
**A:** Good question! From the formulation of our proposed SDMGrad, we know that i... | Summary: This paper proposes stochastic variants of the MGDA for multi-objective optimization (MOO) problem, named "Stochastic Direction-Oriented Multi-objective Gradient descent" (SDMGrad) and SDMGrad-OS with efficient sampling. Optimization convergence analysis to the Pareto stationary point is provided, with improve... | Rebuttal 1:
Rebuttal: **Q1. How does your algorithm compare to the simple SGD baseline in terms of convergence to Pareto stationary point in clock time?**
**A:** For the SGD baseline, we use the implementation by [1]. The experiments on Cityscapes, NYU-v2, and MT10 are provided in Table 1-3 in the global response PD... | null | null | null | null |
When Does Confidence-Based Cascade Deferral Suffice? | Accept (poster) | Summary: The paper consists of 2 parts. Part 1 contains theoretical analysis of when confidence-based deferral rules for cascades of 2 or more models succeed or fail, based on a proposed risk function (equation (1) in section 3) presenting a tradeoff between accuracy and computational cost of invoking subsequent models... | Rebuttal 1:
Rebuttal: > Section 4.1 seems to have a technical flaw in reasoning, despite that Lemma 4.1 appears to be correct… Section 5 presents all results in terms of accuracy-deferral curves… I cannot see any curve or any piece of information at all mentioning the risk $R(\cdot)$ in (1)
**There appears to be a mis... | Summary: This paper explores the cascade deferral problem, which is an issue in the context of machine learning models arranged in a cascading order, where a decision needs to be made about whether to defer the processing of data from one model to the next in the cascade. The challenge is to optimize the deferral decis... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reviewing our work, and for the complete summary which we agree with.
> The empirical results of this paper are primarily based on two datasets: CIFAR-100 and ImageNet. Additionally, the experiments were conducted on image classification tasks only.
In the p... | Summary: This paper systematically investigates why and when the confidence-based deferral rule work, and particularly, identifies cases when it fails. To enable this investigation, they provide a theoretical characterization of the problem and the optimal deferral rule. They also provide a post-hoc solution that can w... | Rebuttal 1:
Rebuttal: > The listed conditions about when the confidence-based method fails are not that surprising. Anyhow, this might not count as a weakness, as it is also reassuring that the theory does produce intuitively sensible results.
We are glad the reviewer finds the results intuitive. We would like to emph... | Summary: The authors present a theoretical analysis for the Bayes optimal deferral rule for a cascade of K=2 classifiers and a certain population risk.
Based on this rule, they characterize when confidence deferral rule using exact posterior probabilities is similar to the Bayes optimal deferral rule is some sense.
Bas... | Rebuttal 1:
Rebuttal: Thanks for the positive feedback, and for recognizing the importance of our theoretical analysis. We appreciate the reviewer’s comments on the presentation. We will revise accordingly.
> The empirical coverage may be somewhat improved, e.g., by using practical datasets without the controlled mo... | Rebuttal 1:
Rebuttal: We thank all the reviewers for constructive comments. We will revise our submission accordingly. In what follows, we clarify common points raised by multiple reviewers. *Please note that we have also attached a PDF to this rebuttal.*
> Meaning of “produce the same order” in Lemma 4.1 [Reviewer k... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the authors study the problem of confidence-based cascade deferral for the classification task. They first proposed the formulation of Bayes optimal deferral rule, which relies on both the base model and its successive model, and then proposed several baselines that works by mimicking the Bayes ... | Rebuttal 1:
Rebuttal:
> The analyses are conducted on a case-by-case basis. There is still progress needed to develop a consistent and computationally friendly deferral rule.
We reiterate that by Proposition 3.1, the Bayes-optimal deferral rule is based on thresholding $s^*( x ) = \eta_{h^{(1)}(x)}(x) - \eta_{h^{(2)}... | null | null | null | null | null | null |
On the Variance, Admissibility, and Stability of Empirical Risk Minimization | Accept (spotlight) | Summary:
This paper proves that variance for ERM enjoys a a minimax rate. The findings indicate that in scenarios where ERM is not optimal, the source of suboptimality lies within the bias component. Furthermore, these insights are extended to encompass an admissibility-type theorem for both fixed and random design, a... | Rebuttal 1:
Rebuttal: Thank you for your kind report and for pointing out that typo. In the revised version, we will follow your suggestion and embed a tabular summary of the results. We will move many of the remarks to the appendix.
---
Rebuttal Comment 1.1:
Title: The rebuttal solves all my concerns
Comment: The re... | Summary: This paper explores the minimax optimality of ERM in terms of the bias and variance of the ERM method.
They find in some settings that the variance is always at the minimax rate, implying that suboptimality can only occur due to bias. This paper also explores stability of ERM, finding that almost-minimizers ar... | Rebuttal 1:
Rebuttal: Thank you for your kind report and your typo fixes. We have clarified that the noise vector is drawn independently of the data points.
Regarding Assumption 8, is motivated by high-dimensional models, especially in the ``benign overfitting'' literature where we they assume that ERM interpolates t... | Summary: This paper studies the suboptimality of Empirical Risk minimization (ERM) of the squared loss, or equivalently, Least Squares (LS), for convex function classes, in both fixed and random design settings. In the context of non-parametric statistics, necessary and sufficient conditions for the optimality of LS ar... | Rebuttal 1:
Rebuttal: Thank you for your kind report and your typo fixes. | Summary: The paper considers the Empirical Risk Minimization (ERM) problem with squared loss and shows that the suboptimality of ERM is due to the bias rather than variance.
Strengths: This paper is quite theoretical and technical. The paper provides useful insights in different aspects. The paper finds that (1) the ... | Rebuttal 1:
Rebuttal: Thank you for your report. We agree that the intuitive explanations of our assumptions and results are a bit briefer than we would like, which was forced upon us by the page limit for the submission. In the revised version, we will move Theorem 2 to the appendix, along with the additional page i... | Rebuttal 1:
Rebuttal: We thank all reviewers for their thoughtful comments. We provide the following general remarks and comments for all reviewers and to the area chair:
1. The main contribution/message of the paper: Empirical Risk Minimization (ERM) with squared loss, or any "Lipschitz" loss in the observations... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the classic problem of regression under noisy observations, with a convex and closed function class $\mathcal{F}$. In particular, the paper considers the performance of the Empirical Risk Minimizer (ERM) under the mean squared error, in both the fixed design and random design settings.
At a... | Rebuttal 1:
Rebuttal: Thank you for your detailed report. Following your comments as well as those of other reviewers regarding the density of results in the paper, in the revised version, we moved the relatively technical Theorem 2 to the appendix and inserted more explanations/intuition into the main body, which the ... | null | null | null | null | null | null |
Effectively Learning Initiation Sets in Hierarchical Reinforcement Learning | Accept (poster) | Summary: The paper considers an HRL setting with options (including sub-goal reward functions) in which termination conditions for options are provided, and the goal is to learn the option policies and initiation sets simultaneously. The main focus is on learning of initiation sets in an online scenario where option po... | Rebuttal 1:
Rebuttal: We are happy that you found our method to be original and the problem setting to be important. We hope that we can clarify some misunderstandings and convince you about the value our proposals to the HRL community.
## Weaknesses
> Inferring success probability is a problem that should span other... | Summary: The paper presents an approach for efficiently learning initiation sets of options for approaches that automatically learns temporal options for hierarchical planning. The paper introduces the concept of initiation value function (a probability measure for successful option executions for a given imitation set... | Rebuttal 1:
Rebuttal: We are delighted that you thought that our paper was well written, thorough and easy to follow!
We understand your desire for more details about how our methods are incorporated into the option discovery algorithm, DSC. Section B.3 (including Algorithms 2 and 3) in the Appendix provides more det... | Summary: This paper addresses the problem of learning initiation sets in hierarchical reinforcement learning. Initiation sets define the states from which an option can be executed successfully. However, learning initiation sets is challenging because they depend on the option policy, which changes as the agent learns.... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and suggestions. We are glad that you found our work to be novel and our experimental approach to be comprehensive and systematic!
> There are certain parts of the paper that lack clarity, such as the algorithm for selecting the goal for the DSC method.
The ... | Summary: This paper studies the problem of learning initiation sets, which are important in hierarchical reinforcement learning for indicating where a policy will succeed, and identifies three main challenges that arise with existing methods to learn these sets. These three challenges are data non-stationarity, tempora... | Rebuttal 1:
Rebuttal: We are glad that you found our paper to be well written and our empirical evaluations to be diverse. We hope that we can change your mind about the potential value of our paper to the HRL community.
> In the Figure 2, can you clearly define what each method is?
We accidentally labeled the IVF a... | Rebuttal 1:
Rebuttal: We are grateful to all the reviewers for their thoughtful reviews and constructive feedback. We are happy to see that all reviewers found our work to be novel and our paper to be well written and clear in its presentation. We hope that our rebuttal helps resolve misconceptions and remaining reserv... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper discusses three difficulties in effectively learning option initiation sets in reinforcement learning --- these are non-stationarity, temporal credit assignment, and pessimism --- and proposes a method that addressed these difficulties. The proposed method is evaluated empirically in a variety of env... | Rebuttal 1:
Rebuttal: Thank you for the positive review. We are glad that you found our paper to be clear and our approach and experiments to be sound.
> It would be useful to see a discussion on the computational cost of the proposed approach.
The computational cost of the IVF approach is very similar to that of th... | null | null | null | null | null | null |
Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions | Accept (poster) | Summary: The authors propose a framework for learning Wasserstien distances and other `SFGI functions'. Two key ingredients in their approach are the characterization as all SFGI functions (a universality theorem), and a sketching mechanism which shows that the size of certain components in the networks need not depend... | Rebuttal 1:
Rebuttal:
Thank you for your very helpful comments! We will accommodate those in our revised manuscript (including mentioning explicitly the dependence on (the intrinsic) dimension). Below we will just mention a couple more major ones.
- Regarding your comment on **exponential dependence on dimension of ... | Summary: The paper introduces the concept of symmetric and factor-wise group invariant functions (SFGI), which are continuous and symmetric product functions on complex objects like point sets and graphs. The authors propose a general neural network architecture for approximating SFGI functions and combine it with a sk... | Rebuttal 1:
Rebuttal:
Thank you for your comments!
Regarding your comments on **limited comparison**: Thank you, and we have now also carried out experiments w.r.t. 2-Wasserstein distance. See our results in the attached PDF. As we reported at the beginning in the general comments to all reviewers, we can see that th... | Summary: The goal of this submission is to learn the (Wasserstein) distance between complex objects (e.g. point sets, graphs) within an arbitrary additive $\epsilon$-error.
This paper presents a general neural network architecture for approximating symmetric and factor-wise group invariant (SFGI) functions.
The propo... | Rebuttal 1:
Rebuttal:
Thank you for your comments!
Regarding your question on the handling of **points of varying sizes** (from your comment ``I am looking forward to seeing more detailed discussions of it ...''): First, in our theoretical results, the fact that the parameter size (model complexity) is independent o... | Summary: The paper proposes a neural network architecture that efficiently approximates $p$-Wasserstein distance between point sets. This is achieved by exploiting the networks' capability to approximate functions that are symmetric and invariant to group actions componentwise. The highlight of the model is that its co... | Rebuttal 1:
Rebuttal: Thank you for your comments!
Regarding your main comment that "Under the assumption of compactness, this becomes straightforward as a finite cover is ensured.", we would like to clarify the following:
Note that there are **two spaces** involved in our problem. The first space $\Omega \subset (X,... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable comments and feedback.
We are happy that reviewers appreciate our contribution of a simple neural network model (constructed based on our theoretical insights) which can estimate Wasserstein distance accurately with bounded model complexity.
Indeed, ide... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning to Receive Help: Intervention-Aware Concept Embedding Models | Accept (spotlight) | Summary: The intervenability of concept bottleneck models has been taken for granted thus far. There have been numerous methods that tried intervening or propose policies to intervene. However, previous methods did not go so far as to optimize models for interventions. This work proposes intervention-aware training and... | Rebuttal 1:
Rebuttal: Dear Reviewer 4KHx,
Thank you for your feedback and valuable questions. We appreciate that you found our work novel and clear. Below we answer the questions you raised.
### Message of the paper and experiment design
We believe there may be a misunderstanding of the methodology underlying our p... | Summary: The authors proposed a modified version of CEM that is better at receiving human test-time intervention by explicitly incorporating intervention into the training stage. Specifically, an intervention prediction module is trained to behavior clone an optimal-greedy intervention policy (Skyline). The CEM is trai... | Rebuttal 1:
Rebuttal: Dear Reviewer 23Ef,
Thank you so much for the very encouraging review and the valuable feedback and suggestions. We are glad you found our work very easy to read, well-motivated, and friendly for reproduction/real-world use. Below we discuss some of the points you raised in your review, including... | Summary: The authors in this paper proposed a novel method of improving test-time interventions for Concept Bottleneck Models (CBMs). Although many CBM works showcase their ability to do intervention, none of them explicitly motivate the learned model to do well on intervention during training phase, hence hindering th... | Rebuttal 1:
Rebuttal: Dear Reviewer oMUi,
Thank you so much for the very encouraging review and the extremely valuable feedback that came with it. We are very glad that you found our work significantly novel, well-written, and clearly motivated. Below we answer the questions you raised in your review.
### Motivation ... | Summary: The authors introduce IntCEMs, an extension of Concept Embedding Models designed
specifically to react correctly to external interventions to the learned concepts.
Compared to regular CEMs, IntCEM feature two additional elements: a policy that,
essentially, guesses what interventions a human expert would do o... | Rebuttal 1:
Rebuttal: Dear Reviewer Zgwp,
Thank you for your very insightful feedback. We are glad you found our paper’s motivation, evaluation, and relevant work interesting and generally positive. Below we address your questions.
### More explicit definition of “interventions”
This is a great point. We want to cl... | Rebuttal 1:
Rebuttal: We thank the reviewers for their very insightful feedback and for taking the time to read our work carefully. Their feedback has certainly improved the quality of our manuscript. We hope to address your concerns in this rebuttal and its corresponding supplementary document. We reply to questions s... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
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