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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models | Accept (poster) | Summary: The paper formulates hard prompt compression as a rate-distortion problem. An algorithm is provided for hard prompt compression, as well as another to estimate the RD curve (Algorithm 1). Experiments on synthetic as well as benchmark datasets are provided with comparisons to previous methods.
Note:
- I'm more... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading of the paper and constructive suggestions and feedback. We provide our response to the reviewer’s individual comments here, and strongly encourage the reviewer to check our global response for updates regarding our new natural language experiments, whi... | Summary: The paper studies the distortion-rate function for prompt compression and proposes an linear programming based algorithm that produces compressed "hard" prompts and is suitable for black box models. The authors provide empirical results on a synthetic dataset illustrating a gap between the existing prompt comp... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and constructive feedback. We provide our response to the reviewer’s individual comments here, and strongly encourage the reviewer to check our global response for updates regarding our new natural language experiments, which is also relevant to the reviewer’... | Summary: This paper proposes an information-theoretic framewok for token-level prompt compression for large language models, where the rate is characterized by the expected ratio between the compressed prompt and the original prompt, and the distortion is a cross-entropy based distortion or an accuracy based distortion... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. We provide our response to the individual comments here, and strongly encourage the reviewer to check our global response for updates regarding our new natural language experiments, which is also relevant to the reviewer’s comments. We also beli... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for acknowledging our contributions and providing constructive feedback. Our ***key contributions*** lie in providing a principled framework for prompt compression (as acknowledged by Reviewers cHot and sxbS), showing a large gap between optimality and current schemes (a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Hyper-opinion Evidential Deep Learning for Out-of-Distribution Detection | Accept (poster) | Summary: This manuscript takes a significant step forward in the realm of evidential deep learning by considering a more holistic hyper-opinion evidence framework. The approach presented offers a novel perspective for optimizing evidential deep learning models, notably enhancing their ability to detect out-of-distribu... | Rebuttal 1:
Rebuttal: Dear Reviewer cb7H,
We sincerely appreciate the time and effort you have dedicated to reviewing our paper and providing us with valuable feedback. Here are our replies.
> The manuscript exhibits some shortcomings in the handling of certain details. The hyper-domain does not encompass the set its... | Summary: This paper studies the problem of out-of-distribution (OOD) detection. Traditional Evidential Deep Learning framework collects sharp evidence that supports a single category while ignoring vague evidence that supports multiple categories, leading to inaccurate uncertainty estimation and decreased OOD detection... | Rebuttal 1:
Rebuttal: Dear Reviewer Y3V6,
We sincerely appreciate the time and effort you have dedicated to reviewing our paper and providing us with valuable feedback. Here are our replies.
> To ensure fairness, the value of $W_{prior}$ should be explicitly stated and consistent with EDL, cause the value of $W_{prio... | Summary: This paper introduces an out-of-distribution detection method based on evidential deep learning. This method models the evidence in hyper-domain, and the hyper-opinion in Subjective Logic is used to replace the multinomial-opinion in traditional evidential deep learning. The Hyper-opinion Evidential Deep Learn... | Rebuttal 1:
Rebuttal: Dear Reviewer jaEe,
We sincerely appreciate the time and effort you have dedicated to reviewing our paper and providing us with valuable feedback. Here are our replies.
> The origin of the sample uncertainty depicted in the upper portion of Figure 1 is not clearly defined. It is imperative to cl... | Summary: This paper provides a method for Out-of-Distribution detection called Hyper-opinion Evidential Deep Learning which is based on Evidential Deep Learning. It models the evidence on the hyper-domain, considering the extra vague evidence containing multiple possible categories. The measurement of vague evidence ma... | Rebuttal 1:
Rebuttal: Dear Reviewer hxrE,
We sincerely appreciate the time and effort you have dedicated to reviewing our paper and providing us with valuable feedback. Here are our replies.
> Eqs. 13, 14, and 15 are hard to understand. The authors can detail on the meanings between the different W's.
$W$ is a matri... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence | Accept (poster) | Summary: This paper proposes a new optimizer based on Adam, called MicroAdam, which reduces memory footprints, via sparisification, quantization, and error feedback. Theoretical convergence guarantees are provided. Empirical results show good performance on several fine-tuning tasks
Strengths: 1. This paper proposes a... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback. We address your questions below.
**Weakness 1:** Pretraining
We would like to emphasize that we designed MicroAdam for the finetuning (FT) use case and our research question was “are all gradient entries useful for fine-tuning?”, which is why... | Summary: This paper proposes a memory-efficient Adam-based optimizer called MicroAdam. The key idea is to compress gradients using Top-K sparsification before passing them to the optimizer state, along with an error feedback vector that is itself compressed via quantization. For general smooth non-convex (and PL) funct... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback. We address your questions below.
**Weakness 1:** Complexity of compressors
We agree that memory savings depend on the choice of compressors, and that efficient implementations are needed. However, we do have the advantage that both gradient s... | Summary: The paper introduces MicroAdam, a novel optimizer designed to improve memory efficiency while maintaining competitive performance with established optimizers such as Adam. The authors provide theoretical analyses and experimental results to demonstrate the benefits of MicroAdam in various settings.
Strengths:... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback. We address your questions below.
**Weakness 1:** About Figure 1
The motivation for this illustration is to show that some form of EF is _necessary_ for good convergence, even in the case of toy instances. Specifically, prior heuristic methods... | Summary: The paper presents a new optimizer that approximates Adam but has a lower memory footprint. Adam stores for each parameter two additional values --- the exponential moving averages of the gradients and the gradient squared. The current optimizer saves space by using the fact that for most parameters, the gradi... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback. We address your questions below.
**About Summary:** Memory savings
The memory savings of MicroAdam are ~20% if we compare the entire memory usage, but the key comparison point should be on the size of optimizer states, as this is the quantity... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for the useful feedback! We have provided individual responses to each review, and briefly summarize the main points here:
- To address the concern about Algorithm 1, we provided a detailed explanation of each line in the algorithm at the end of our response to... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The script proposed a memory-efficient method with a theoretical guarantee.
Strengths: The topic on memory-efficient optimizers is important. I did not observe obvious flaws in the theory.
Weaknesses: See below.
Technical Quality: 2
Clarity: 1
Questions for Authors: See below.
Confidence: 4
Soundness: 2... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback. We address your questions below.
**Limitation 1-1:** Contribution and novelty in MicroAdam
We focus on reducing the memory cost of adaptive optimization, and start from the idea that not all gradient components carry useful information for o... | null | null | null | null | null | null |
Unveiling The Matthew Effect Across Channels: Assessing Layer Width Sufficiency via Weight Norm Variance | Accept (poster) | Summary: The paper studies the effects of layer widths on neural network performance. By studying the affects of the weight norm in different channels, the work discovers several distinct stages of training, which are apparent across different modalities and architectures. The authors also show how the insights could h... | Rebuttal 1:
Rebuttal: Dear reviewer nGxF,
Thank you very much for your thoughtful and valuable feedback. Your recognition of the intuitiveness of our findings and the potential for our insights to further improve neural networks is very encouraging and motivating for us. We are committed to further advancing this line... | Summary: The paper proposes a method to optimize neural network layer width by analyzing the variance of weight norms across channels during training. This approach helps determine if a layer is sufficiently wide. Empirical validation shows that adjusting layer widths based on these patterns can reduce parameters and i... | Rebuttal 1:
Rebuttal: Dear reviewer nv2M,
Thank you for your valuable feedback. We will address each of your concerns and questions in the following section.
> W1: The layer width optimization is related to channel pruning and NAS-based channel number search, which have achieved significant success in finding optima... | Summary: This paper tries to address an issue in deep neural networks: the trade-off between computational cost and performance, particularly focusing on the width of each layer. Traditionally, layer widths in neural networks are determined empirically or through extensive searches. The authors propose a novel approach... | Rebuttal 1:
Rebuttal: Dear reviewer iwdV,
Thank you for your valuable feedback. We address each of your concerns in the following.
> W1: The empirical justification of the proposed method is somehow not convincing enough. It is hard to say there is a hard cutoff.
With all due respect, our results indicate that ther... | Summary: This paper investigates the relationship between the differences in weight norms across channels and the adequacy of layer widths. The authors suggest that knowing these patterns(IS/DS) can help set layer widths better, leading to better resource use, fewer parameters, and improved performance in different net... | Rebuttal 1:
Rebuttal: Dear reviewer Q6z7,
Thank you for your valuable feedback. We address each of your concerns and questions in the following.
> W1: Retraining is time-consuming. Considering that IS/DS indicates whether layers learn similar neurons, could we use the cosine similarity of a pre-trained model to di... | Rebuttal 1:
Rebuttal: Dear AC and Reviewers,
We sincerely thank you for the time and effort you dedicated to the reviewing process. We are delighted to hear that reviewers find the paper to be well-written (Q6z7, nV2M, nGxF), novel (Q6z7, nV2M), and interesting (nGxF). To further address the comments and questions pos... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations | Accept (poster) | Summary: This paper proposes a novel aggregation algorithm FLoRA for low-rank federated fine-tuning. Compared to prior works, FLoRA stacks the LoRA matrices together rather than averaging them, thus avoid the mathematical errors during the aggregation process on the server. The paper provides experiments compared with ... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's recognition of the contributions of our paper. Regarding the weaknesses raised, we address all the concerns in detail below.
>W1 The paper misses some related works. For example, [1] also talks about LoRA in federated fine-tuning and optimize its efficiency an... | Summary: Previous methods using Low-Rank Adaptation (LoRA) for efficient federated fine-tuning may have led to mathematically inaccurate aggregation noise, reducing the effectiveness of fine-tuning and being unable to handle heterogeneous LoRA. The authors analyzed the mathematical inaccuracies in LoRA aggregation in e... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's recognition of our proposed method's effectiveness and broad application scenarios. Regarding the weaknesses raised, we address all the concerns in detail below.
>W1: Writing needs to be calibrated. The sentences in line 64 and line 70 are repeated. 2. The dis... | Summary: This paper focuses on developing a novel aggregation strategy for training LLMs in FL with LoRA. Specifically, they identify that separately aggregating two matrices of LoRA module is not identical to aggregating model updates. Therefore, they propose a stack based aggregation strategy that merge matrices befo... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's recognition of the effectiveness of our proposed method. Regarding the weaknesses raised, we address all the concerns in detail below.
>W1: The novelty is limited. Although the proposed method is effective, the contribution of the stack-based aggregation strat... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Beyond Euclidean: Dual-Space Representation Learning for Weakly Supervised Video Violence Detection | Accept (poster) | Summary: This paper presents a pioneering approach called Dual-Space Representation Learning (DSRL) for the task of weakly supervised video violence detection. The overall framework contains the Hyperbolic Energy-constrained Graph Convolutional Network (HE-GCN) for capturing event hierarchies and the Dual-Space Intera... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation of the paper. We have carefully considered your constructive and insightful comments and here are the answers to your concerns.
***Q1: Novelty: Some modules in this paper lack innovation, such as the cross-graph attention mechanism is commonly ... | Summary: This paper presents a novel approach called Dual-Space Representation Learning (DSRL) for weakly supervised Video Violence Detection (VVD). Traditional VVD methods rely heavily on Euclidean space representation, which often fails to distinguish between visually similar events. The proposed DSRL method leverage... | Rebuttal 1:
Rebuttal: ***Q1: For the HE-GCN module, I am curious about the relationship between HDE and LSHAD. The authors should explain the relationship between HDE and LSHAD in detail?***
Thank you for the valuable comments. We will elaborate on the relationship between *HDE* and *LSHAD* in detail.
In fact, *HD... | Summary: This paper proposes leveraging dual-space learning, encompassing both Euclidean and Hyperbolic spaces, to enhance discriminative capacity by capitalizing on the strengths inherent in hyperbolic learning. To overcome the limitations of the hard node strategy in the previous method, this work introduces DSRL (Du... | Rebuttal 1:
Rebuttal: Thank you so much for acknowledging the strength of our method. We have carefully considered your constructive and insightful comments and here are the answers to your concerns.
***Q1: It would have made the manuscript more convincing if the authors could provide inference visualizations of the... | Summary: The paper introduces a novel method called Dual-Space Representation Learning (DSRL) aimed at enhancing the detection of video violence, particularly in scenarios where the violence is weakly supervised and visually ambiguous.
DSRL combines the strengths of both Euclidean and hyperbolic geometries to capture... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and suggestions to improve this work. We will address your concerns below.
***Q1: Reasons for the design choices in the LSHAD with its multiple hyperparameters and threshold criteria***
Inspired by the Global-first principle [1] that humans alway... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for providing constructive feedback that helped us improve the paper. We are glad the reviewers find that
**Our method is interesting and novel**
* "The method is innovative." ---8atP
* "The proposed method is totally interesting and novel." ---5VkS
* "This... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
RanDumb: Random Representations Outperform Online Continually Learned Representations | Accept (poster) | Summary: The paper shows that a fixed random feature space of the data, followed by some linear classifier, outperforms continually learned feature spaces across multiple benchmarks. The paper then shows an ablation study of the suggested method.
Strengths: **Clarity:** The suggested idea is simple, and it is easy to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback. We appreciate the recognition that our idea is simple and easy to understand, as well as the recognition of the comprehensive nature of our ablation study and the improvements demonstrated across multiple benchmarks. The reviewer also pointed out ... | Summary: This paper proposes RanDumb, a representation learning-free method for online continual learning (OCL). It uses data-independent random Fourier transform to project the data to a high-dimensional space (embed), scales the features to have unit variance (decorrelation), and finally performs classification with ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time reviewing our work and providing encouraging remarks such as *"no major technical flaws,"* *"interesting and novel,"* and *"extensive benchmarks."* We also appreciate giving the presentation of our work a remark of being *"excellent."*
Below, we ... | Summary: To obtain powerful representation in an online continual learning setting, the authors propose a new learning method referred to as RanDumb, that embeds raw pixels using a fixed random transform, approximating an RBF kernel initialized before any data is seen.
The proposed model trains a simple linear classif... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time in reviewing our work and for providing their concerns and suggestions. We appreciate the recognition of the work's contribution in *"challenging the prevailing assumptions about effective representation learning in online continual learning."* The acknowledgme... | Summary: The authors show that a model with random Fourier features as a representation, followed by a normalisation then nearest class means for classification, outperforms most Continual Learning methods in online CL benchmarks. The representation is not learned as opposed to other online CL methods the model is comp... | Rebuttal 1:
Rebuttal: Thank you for your insightful question. To compare the effect, we will use two baselines: ER [13] and iCARL [54].
**Effect of gradient-free classifiers:** The primary difference between these methods is that ER trains the last layer along with the previous network, while iCARL learns a non-gradi... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
How Do Large Language Models Acquire Factual Knowledge During Pretraining? | Accept (poster) | Summary: This paper aims at understanding how large language models acquire knowledge from the pretraining process.
To this end, the authors propose a dataset consisting of paragraphs about fictional entities and three different kinds of probes that can be used to test whether the model successfully acquires the knowle... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer 1qMb for the detailed review and valuable feedback.
We will respond to the weaknesses and questions in order:
> The interpretation about batch size may be too assertive. It is not certain that the retention rate will really go to 0 as the number of tokens increases. T... | Summary: The paper proposes to inject synthetic factual knowledge in the pretraining data of large language models and measure the acquisition and retention of this knowledge over time. The study shows that model acquires factual knowledge gradually with many exposures of the same fact, and forgets over time if not rei... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer fwEj for the detailed review and valuable feedback. We will respond to each weakness and question in order:
> the model trained with paraphrased knowledge show lower performance after learning on the semantic probe and the composition probe compared to the model traine... | Summary: This paper explores the process by which LLMs accumulate factual knowledge during pretraining. It finds that while more data exposure can improve immediate knowledge acquisition, it does not significantly affect long-term retention due to subsequent forgetting. The study reveals that larger batch sizes and ded... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer fwEj for their time and effort in reviewing our work.
We appreciate the reviewer's acknowledgment of the contributions of our work:
- The creation of FICTIONAL KNOWLEDGE dataset for controlled experiments on factual knowledge acquisition dynamics in LLM pretraining
- ... | Summary: This study presents a comprehensive empirical analysis of how large language models (LLMs) acquire factual information during pre-training. The researchers used a novel and straightforward method: they introduced new fictional information into the training corpus and reran the pre-training process to observe w... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer ewnz for their positive and thoughtful feedback. We appreciate the recognition of our contributions, particularly our insights on factual knowledge acquisition dynamics in LLM pretraining, as well as the commendation on our writing clarity and experimental design.
We w... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their commitment to thoroughly reviewing our work. We greatly appreciate the reviewers’ recognition of this work’s contributions:
- **The development of a novel dataset (FICTIONAL KNOWLEDGE), experimental methods, and evaluation metrics to study how knowledge ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The author's present a dataset and method to measure factual knowledge acquisition for LLM pretraining.
The author's conduct thorough investigations and effects (invented) factual knowledge during LLM pretraining and discover empirical trends and evidence for observations that have been recently observed (but... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer ZYtT for acknowledging our contribution to studying the pretraining dynamics in LLMs, and commendation to our method and analysis regarding this topic. Also, we appreciate the reviewer’s effort to improve our work.
We would like to discuss each point in order:
> It wo... | null | null | null | null | null | null |
A two-scale Complexity Measure for Deep Learning Models | Accept (poster) | Summary: This paper proposes a new capacity measure for a general statistical model, called two-scale effective dimension (2sED), and provides an upper bound (under some assumptions on the model) on the generalisation error for this model based on the proposed measure. Then, the authors show how to lower bound the prop... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and the feedback.
We next answer the criticism/questions:
**(1) Weakness comment about the notation:**
Thank you for the comment. We will make the notation consistent.
**(2) Question regarding Assumption (iii)**
We agree with the reviewer that assumption ... | Summary: In this work, a new measure of model complexity, 2sED, is introduced. It is used to derive a new generalization bound for statistical models. A special case of 2sED is shown for Markovian models. Experiments show that the complexity measure correlates with the training loss of neural networks.
Strengths: 1. A... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and the feedback.
We next answer the questions/comments raised:
**(1) Main weakness regarding the significance:**
It is correct that 2sED is in general expensive to calculate. However, we show that for the case of Markov models we have a lower bound that can... | Summary: This paper proposes a two-scale complexity measure that can be used to derive generalisation bounds on the empirical risk. This measure is intuitive and comes from the box-counting dimension of the parameter space with the Fisher metric. From what I can understand, authors please correct me if I'm wrong, the i... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and the positive feedback.
Your summary of the paper is correct and very nice.
Your “weakness” comment is also correct. Computing the eigenvalues of FIM is computationally intensive (as the FIM get’s very large). One main contribution of our work is that we sh... | Summary: The authors introduce a new complexity measure for machine learning models. The complexity measure induces generalization bounds and admits approximations for Markovian models. The authors show that this method is helpful for estimating the generalization error on several feed-forward neural networks.
Strengt... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and the positive feedback.
We are happy to add a more thorough discussion of previous work on the expressivity of neural networks to the manuscript.
The effective dimension (ED) is a rather novel capacity measure that has been introduced recently (by Figalli e... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
What type of inference is planning? | Accept (spotlight) | Summary: This paper studies 3 types of factor graph inference for planning and in particular their relationship to optimal planning. The well known $\exp(\lambda R(x, a, x'))$ factor is used. MAP inference computes the maximum energy configuration over states and actions which corresponds to zero posterior entropy. Mar... | Rebuttal 1:
Rebuttal: Thank you for your time in reviewing this paper. We would like to address one of your comments:
**One thing that would be of interest is how "reactivity" interacts with online re-planning.**
We do a worked out example in Appendix F.2, in which we show how for some MDPs online replanning is not a... | Summary: This paper investigates the concept of "planning as inference", which frames planning problems (i.e. coming up with actions to reach a goal or attain reward) using the vocabulary and mathematics of probabilistic inference. The paper surveys existing formalizations of this broad concept, finding that none of th... | Rebuttal 1:
Rebuttal: Thank you for your careful review this paper, and for your insightful comments and suggested improvements, which we will incorporate.
**Please define what f(x, a) is**
We tacitly take f(x, a) to correspond to the factor graph of Fig. 1 [left], but this is not explicitly mentioned in the text. We... | Summary: This work introduces a variational inference (VI) framework to characterise different types of inference for planning, specifically for finite-horizon MDPs represented as probabilistic graphical models. They also develop an inference algorithm (VBP) that takes inspiration from the loopy belief propagation and ... | Rebuttal 1:
Rebuttal: Thank you for your time reading this paper, we hope that you will find the following clarifications useful in its judgement.
**Why do you focus on finite-horizon MDPs? Do your result easily translate to infinite-horizon MDPs?**
When thinking of planning as inference in a factor graph, it is simp... | Summary: This paper shows that planning in an MDP can be posed as a specific form of
inference in a graphical model; in this context of inference, different forms
of inference can be applied, with different results. Additionally, alternate
forms of inference such as variational techniques can be used, which allow
bette... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and positive impression, we will take your comments into account to improve the clarity of the final manuscript.
**Are there any computational constraints on the VBP? Is it comparable in speed to the gradient descent techniques?**
VBP computational and converge... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes a framework to understand, as the title suggests, what type of inference is (probabilistic) planning. The authors make use of variational inference which encompasses marginal, MAP, and marginal MAP (MMAP) inference to theoretically compare the power of such types of inference in bounding the... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and catching several typos. We have corrected the paper accordingly and include further explanations below.
**The introduction mentions that planning inference is the same as `value iteration', but nowhere else in the paper is value iteration nor Bellman backups... | null | null | null | null | null | null |
Probabilistic and Differentiable Wireless Simulation with Geometric Transformers | Reject | Summary: The paper proposes the use of the Wireless Geometric Algebra Transformer (Wi-GATr) to model signal propagation. Based on the Wi-GATr network, it introduces a differentiable prediction model and a diffusion model. Compared to traditional statistical and ray-tracing methods, the proposed approach not only addres... | Rebuttal 1:
Rebuttal: We thank reviewer yJCq for providing a detailed review of our paper.
We are happy to read that they found it logically structured and that they appreciated the novel treatment of forward and inverse problems with a diffusion model.
Now, we address reviewer yJCq’s concerns and also indicate if it i... | Summary: This paper proposes the use of a transformer architecture to model electromagnetic propagation of physical systems. The approach is claimed to outperform existing methods by (i) computational efficiency (compared to raytracers) and (ii) enabling solving inverse problems. The method is evaluated on a number of ... | Rebuttal 1:
Rebuttal: We thank reviewer 6syy for providing a detailed review of our paper.
We are glad that they appreciated the generality of the approach, and in particular that they found our paper well-written and easy to follow.
Now, we address reviewer 6syy’s concerns and also indicate if it is shared by fellow r... | Summary: Authors proposed transformer based ideas on very well studied area: Wireless environment simulation.
The key idea here is to capitalize Geometric Transformers to simulate radio environments. It true that wireless (directional) signal propagation is a ray tracing approach, meaning a highly directional wireless ... | Rebuttal 1:
Rebuttal: We thank reviewer 2AZ5 for providing a detailed review of our paper.
Overall, we are glad that the reviewer finds using transformers for radio propagation modeling an interesting idea and appreciates that we include relevant information in our environment input.
Now, we address reviewer 2AZ5’s con... | Summary: The motivation for this work is that modeling the propagation of electromagnetic signals is critical for designing modern communication systems. Ray tracing simulators are not suitable for inverse problems or integration as channel models in designing communication systems.
In this context, the goal of this p... | Rebuttal 1:
Rebuttal: We thank reviewer 3vGS for providing a detailed review of our paper.
Overall, we are glad that the reviewer appreciates our novel wireless tokenization scheme, our diffusion approach, and that we contribute a novel large-scale dataset to the machine learning community.
Now, we address reviewer 3vG... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thorough and constructive feedback.
We are glad to hear that reviewers **LEve**, **3vGS**, **6Syy**, and **yJCq** appreciate the versatility of our approach to forward, inverse, and generative problems. Reviewer **LEve** specifically highlights the data efficienc... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents the Wireless Geometric Algebra Transformer (Wi-GATr), a new architecture for simulating wireless signal propagation in 3D environments. This model utilizes geometric algebra to handle the geometric complexities of wireless scenes and ensures E(3) equivariance to respect the symmetries of the... | Rebuttal 1:
Rebuttal: We thank reviewer LEve for providing a detailed review of our paper.
We are glad that the reviewer appreciates our equivariant approach to model diverse environments, the strong performance in our thorough evaluation, and the data efficiency and versatility of our approach on forward and inverse p... | null | null | null | null | null | null |
Subject-driven Text-to-Image Generation via Preference-based Reinforcement Learning | Accept (poster) | Summary: Summary:
This paper proposed a method which utilized ALIGN model to calculate $\lambda$-Harmonic reward function and combine binary cross-entropy function and DPO as the preference loss to supervise image generation models.
Strengths: Strength:
1. The method is computation-efficient while achieving the highes... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and thoughtful feedback. Please see our responses to your specific questions below.
**Q: More visual results**
We have added 32 images generated by RPO to the attached PDF on global response, specifically focusing on prompts that are both unseen in the training ... | Summary: The paper introduces a novel method for generating text-to-image outputs that incorporate specific subjects from reference images. The authors propose a λ-Harmonic reward function and a Reward Preference Optimization (RPO) algorithm, which simplifies the training process and enhances the model's ability to ma... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and thoughtful feedback. Please see our responses to your specific questions below.
**Q: Limited evaluation metrics.**
Limited evaluation metrics are a common issue in subject-driven tasks. We appreciate you raising this issue and providing reference metrics, su... | Summary: This paper proposes a Reward Preference Optimization (RPO) method, introducing the 𝜆-harmonic reward function to address overfitting and accelerate the fine-tuning process in subject-driven text-to-image generation. Experimental results demonstrate the effectiveness of the proposed approach.
Strengths: 1. By... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and thoughtful feedback. Please see our responses to your specific questions below.
**Q: Limited novelty of reward function and loss.**
Yes, the $\lambda$-harmonic reward function is indeed a classical Pythagorean mean with weights. In fact, when $\lambda = 0.5... | Summary: This paper presents a method for generating personalized images from text using the λ-Harmonic reward function in a system called Reward Preference Optimization (RPO). Specifically, RPO fine-tunes a pre-trained text-to-image diffusion model using a reinforcement-learning-based objective concerning with the har... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and thoughtful feedback. Please see our responses to your specific questions below.
**Q: How does the method allow for adjusting $\lambda$ during inference, and how does it compare to classifier-guidance inference?**
Our contribution is on the fine-tuning step o... | Rebuttal 1:
Rebuttal: # Global response
We would like to thank all the reviewers for providing high-quality reviews and constructive feedback. We are encouraged that the reviewers think our paper *"generated images presented in the paper demonstrate superior text fidelity of the proposed method. (Reviewer tGfs)"*, *"p... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a harmonic reward function for text-to-image personalization. Specifically, it uses this reward function to perform early stopping and regularization. Experimental results demonstrate the effectiveness of the proposed method.
Strengths: * Their proposed reward-based model selection and reg... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and thoughtful feedback. Please see our responses to your specific questions below.
**Q: Comparison to DCO.**
Lee et al. [1] use SDXL [2] as the backbone model and apply LoRA to fine-tune the pretrained model. For a fair comparison, we only use LoRA to fine-tune... | null | null | null | null | null | null |
Trade-Offs of Diagonal Fisher Information Matrix Estimators | Accept (poster) | Summary: The authors study two popular estimators of the Fisher information matrix with respect to neural network parameters. They derive upper and lower bounds for the variance of these estimators and showcase them in applications to regression and classification problems.
Strengths: - Analyzing the convergence prope... | Rebuttal 1:
Rebuttal: Thank you for recognizing our contributions and their soundness.
> Re: *Significance vs [37]*
Please see our global response to all reviewers.
> Re: *Better clarity on which estimator is preferred*
Through our analysis, We have identified certain scenarios where one estimator
is superior to th... | Summary: The paper analyses two estimators of the Fisher Information matrix and specifically their variances, which (from reference 37) have closed-form but non-practical expressions. The authors show a sequence of inequalities and derive practical bounds for these variances, both element-wise and trace-wise, both cond... | Rebuttal 1:
Rebuttal: Thank you for recognizing our contributions and praising our writing.
We respond to your remarks and suggestions as below.
> Re: *The notation* $\\hat{\\mathcal{I}}_{1}(\\theta_i)$
We propose to explicitly define it as $\\hat{\\mathcal{I}}_{1}(\\theta_i) := (\\hat{\\mathcal{I}}_1(\\theta))\_{i i... | Summary: In this paper, the authors analyzed two different estimators, $I_1(\theta)$ and $I_2(\theta)$, for the diagonal elements
of the Fisher information matrix of a parametric neural net model. These diagonal elements are approximations for the entire matrix,
which is unfeasible to be calculated in real neural net... | Rebuttal 1:
Rebuttal: Thank you for recognizing our strength and potential usefulness
in application areas including continual learning.
> Re: *Significance vs [37]*
We kindly refer the reviewer to our global response.
> Re: *Assumption of* $\\theta$ *being the true value*
We clarify that our result holds for any $... | Summary: Summary
-------
The paper studies two estimators for estimating the diagonal of a Fisher information
matrix (FIM) of a parametric machine learning model. Both estimators are based on equivalent
expressions for the FIM. The first is the standard definition, expressed in terms of the
derivatives of the log likel... | Rebuttal 1:
Rebuttal: We thank the reviewer for praising the technical developments of the paper.
> Re: *Usefulness of FIM estimation, e.g. in NN optimization*
As stated in the submitted paper, the FIM can be used (Line 117) "to study the
singular structure of the neuromanifold [2,40], the curvature of the loss [8],
... | Rebuttal 1:
Rebuttal: We extend our appreciation to all reviewers for their thoughtful reviews.
We are pleased to acknowledge the positive remarks on "[...] fair amount of technical
meat [...]" (**Reviewer 7Xmq**) and that the paper "[...] is very
extensive and mathematically sound" (**Reviewer QMbw**).
Despite the tec... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Identifiability of Poisson Branching Structural Causal Model Using Probability Generating Function | Accept (spotlight) | Summary: In order to achieve causal structure learning on PB-SCM, this paper explores the identifiability of PB-SCM using the Probability Generating Function (PGF). Furthermore, this paper enables the identification of the local structures by testing their corresponding component appearances in the PGF. Building on th... | Rebuttal 1:
Rebuttal: > **W1:** While these experiments ... argument for the method's generalizability.
**A1:** We appreciate the suggestion that makes this paper more complete and closer to realistic applications. We further examined our method using a shopping mall paid search campaign dataset [1]. This dataset cont... | Summary: The paper focuses on causal discovery from observational count data, proposing a method to address the identifiability gap in Poisson Branching Structural Causal Models (PB-SCM) using Probability Generating Function (PGF). The authors develop a closed-form solution for the PGF of PB-SCM, enabling the identific... | Rebuttal 1:
Rebuttal: > **W1:** The proposed method cannot handle high-dimensional covariates.
**A1:** Thank you for pointing out this issue; this will be an important problem for us to address in future work. As mentioned in the conclusion section, the performance of our method can be limited by the scale of the dime... | Summary: This paper addresses the identifiability of Poisson Branching Structure Causal Model (PB-SCM) using the probability generating function, during which a compact, exact closed-form PGF solution is developed and the identifiability of PB-SCM is complete based on such relation. A practical algorithm for learning c... | Rebuttal 1:
Rebuttal: > **W1:** This paper states that the cumulant-based ... connection between these two methods. \& **Q1:** What is the connection between the cumulant-based method and the proposed PGF-based method?
**A1:** Thank you for your question allowing us to further clarify the connection between the cumula... | Summary: This paper investigates the causal structure learning on the Poisson Branching Structural Causal Model (PB-SCM)and its identifiability using probability-generating function (PGF). The identifiability is established by developing the closed-form solution of the PGF which can be utilized to identify the causal s... | Rebuttal 1:
Rebuttal: > **Q1**: The authors propose to use the local PGF for identifying the causal structure. It seems that the global PGF can also be used for identifying the causal structure and it would be more beneficial if more discussions could be involved.
**A1**:Thank you very much for this suggestion to impr... | Rebuttal 1:
Rebuttal: Dear Reviewers w346, Gqam, nGrv, and CtKZ,
Thanks for the thoughtful and constructive reviews, which improve the completeness and readability of our paper. It is encouraging that reviewers think that the proposed PGF-based method for identifying PB-SCM is novel (w346, Gqam, CtKZ) and interesting ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SGD vs GD: Rank Deficiency in Linear Networks | Accept (poster) | Summary: This paper presents an interesting dichotomy between SGD vs. GD, and how stochasticity in the gradients have the ability to implicitly regularize towards low-rank solutions. I believe this point is best highlighted by Theorem 5.1, where the show that the highlighted term in Equation (5.6) has a repulsive force... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments, and for pointing out the missing reference.
> The result in Theorem 4.2 also highlights how large step sizes can be beneficial for generalization.
Yes, the presence of $\eta$ does control the rate of decrease of the determinant. Hence, the large... | Summary: This paper studies and analyzes how the rank of the parameter matrices evolves for two-layer linear networks when using GD and label noise SGD. The paper basically shows that while GD preserves the rank at initialization throughout its trajectory, SGD reduces the rank, thereby removing spurious directions. A s... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their insightful suggestions.
> Low-rank solutions and prediction error.
We agree with the reviewer's comment that the implicit regularization might only be useful for generalization if it is aligned with the ground truth model (i.e., in the classical exa... | Summary: This paper studies the implicit bias of SGD for two-layer linear networks. The authors study this primarily using (stochastic) gradient flow (S)GF, the continuous time version of (S)GD. To model the stochasticity, they approximate the SGD noise with independent label noise. Under these assumptions, they prove ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, encouraging remarks, and meticulous proofreading of our work. They will help to make our work clearer.
> It was nice to see some experiments validating the theory, but I felt that the dimensionality of the problems should be increased to be more ... | Summary: The paper proposes an analysis of the gradient flows of two layer linear neural networks with a squared loss borrowing tools from differential equations. The paper's result establishes that the stochastic gradient method generates solutions (limit of the flow as time goes to infinity) with determinant of the p... | Rebuttal 1:
Rebuttal: We thank the reviewer for their interesting questions and encouraging comments.
> Numerical experiments are also on the same and in very small data. It would benefit the paper to run experiments on larger / more complex models to discuss the limits and validity of this theory.
Please refer to... | Rebuttal 1:
Rebuttal: ## Broader empirical evaluation
We would like to thank the reviewers for their positive assessment of the paper and their appreciation of our work. Below, we address general comments that were raised multiple times. Individual responses can be found following each review. All referenced figures c... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MetaCURL: Non-stationary Concave Utility Reinforcement Learning | Accept (poster) | Summary: This paper addresses concave utility RL in a non-stationary episodic setting, where the transition probabilities as well as the utility function may change from one episode to the other. The paper proposes an algorithm, dubbed MetaCURL, which dynamically select the best performing "expert" within a set of base... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. Below, we address the main concerns:
- **Motivating the Setting:** In our common response to all reviewers we further elaborate on how the applications presented in the paper align with CURL. We plan to include them earlier in the introduction... | Summary: This paper focuses on CURL, i.e., the Concave Utility Reinforcement Learning problem, which can be treated an extension of traditional RL to deal with convex performance criteria. The authors introduce MetaCURL for non-stationary MDPs. This is a theory heavy paper.
Strengths: Proofs are provided. It seems th... | Rebuttal 1:
Rebuttal: ### Motivational examples:
We agree with the suggestion to provide examples to motivate our setting. We will include these examples in the introduction on the extended version.
### Questions about the setting:
- **1.** We focus on the model-based RL framework commonly used in theoretical work... | Summary: The paper studied the Concave Utility Reinforcement Learning (CURL) problem in non-stationary environments, which extends classical reinforcement learning (RL) to handle convex performance criteria in state-action distributions induced by agent policies. The paper proposed MetaCURL to address the challenges po... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and for recognizing the new theoretical insights in our paper. We address the questions below:
- **1. tabular MDP:** Thank you for bringing this related work to our attention, we will include a citation in our paper. Thanks for raising this question, we be... | Summary: The authors present a policy learning algorithm for non-stationary (+ uncertain) environments and convex utilities.
The proposed algorithm is a meta-algorithm which runs multiple black-box algorithms and aggregates outputs with something they call a sleeping expert framework. The algorithm achieves optimal dy... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and questions. We agree that the paper is notation-heavy and may be difficult to follow in some parts. We welcome any further suggestions on how to improve the readability of the paper.
### Questions
- **Baselines vs blackbox:** Yes, baselines algorithms a... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and feedback in evaluating our paper. While we agree that the assumption in the dynamics of Equation (8) may seem restrictive, we explain below why studying this case is important for CURL. Additionally, we highlight some of the novel contributions of our... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents theoretical results on the CURL algorithm for non-stationary MDPs. The proposed meta CURL models non-stationarity factors using external noise and achieves low dynamic regret in near-stationary environments, with regret only related to the frequency and magnitude of changes. Overall, the wo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and for recognizing the new theoretical insights in our paper. We address the questions below:
- **Q1:** We take into account two types of non-stationarity: on the objective functions and on the dynamics from the external noise distribution $h_n^t$. Each i... | Summary: This paper addresses online learning in non-stationary episodic loop-free Markov decision processes (MDPs) with changing losses and probability transitions. It extends the Concave Utility Reinforcement Learning (CURL) problem to handle convex performance criteria in state-action distributions, overcoming the n... | Rebuttal 1:
Rebuttal: ### Questions
- **Novelty:** Our algorithm runs without needing knowledge of the environment changes as input, but the final error guarantee does depend on these variations, specifically through the non-stationarity measures $\Delta^p$ and $\Delta^p_\infty$. This agrees with the lower bound in [33... | null | null | null | null |
One-Step Effective Diffusion Network for Real-World Image Super-Resolution | Accept (poster) | Summary: In this paper, authors propose a novel one-step effective diffusion network, termed as OSEDiff, for the Real-ISR. The proposed OSEDiff adopts LQ image as the input and directly output the final output with the help of the decoder of VAE, thus eliminate the uncertainty introduced by random noise sampling and ac... | Rebuttal 1:
Rebuttal: **Q1. Comparison with SinSR.**
The improvements of OSEDiff over SinSR mainly come from the pre-trained SD model and the VSD loss. The SD model, pre-trained on large-scale data, contains rich prior knowledge of natural images, enabling effective one-step generation. Additionally, we finetune the m... | Summary: This paper introduces a diffusion-based real-world image super-resolution method, OSEDiff, which can efficiently generate high-quality images in just one diffusion step. Firstly, in order to eliminate the uncertainty introduced by random noise sampling, the authors propose to directly feed the low-quality imag... | Rebuttal 1:
Rebuttal: **Q1. Unclear motivation.**
Thanks for the comments. The goal of this work is to develop an efficient and effective Real-ISR method by using the pre-trained SD prior. In the research and development of Real-ISR methods, how to construct LQ-HQ training pairs is a critical issue. Therefore, we spen... | Summary: This paper presents a novel approach to real-world image super-resolution for a one-step effective diffusion network (OSEDiff). The proposed OSEDiff effectively eliminates the uncertainty introduced by random noise sampling in previous methods, achieving significant performance improvements across multiple ben... | Rebuttal 1:
Rebuttal: **Q1. Comparison with DMD.**
Thanks for the nice suggestion. While both OSEDiff and DMD draw upon the concept of variational distillation from ProlificDreamer, they differ significantly in several aspects. First, DMD is designed for text-to-image tasks, whereas OSEDiff is tailored for image resto... | Summary: The paper introduces OSEDiff, a one-step diffusion network for Real-World Image Super-Resolution. OSEDiff performs variational score distillation in the latent space to ensure predicted scores align with those of multi-step pre-trained models. By fine-tuning the pre-trained diffusion network with trainable L... | Rebuttal 1:
Rebuttal: **Q1. Multiple inference steps for OSEDiff.**
Please kindly note that OSEDiff is specifically designed for one-step diffusion for Real-ISR. Unlike previous multi-step methods (e.g., StableSR, PASD, SeeSR), which are all based on ControlNet by using noise as input and LQ image as control signal, ... | Rebuttal 1:
Rebuttal: Dear Reviewers, Area Chairs, and Program Chairs:
We are grateful for the constructive comments and valuable feedback from the reviewers. We appreciate the reviewers' recognition on the novelty of our method (Reviewers KDhM and 4vqL), its superior performance (Reviewers jXQz and px3F), efficient t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Open-Vocabulary Object Detection via Language Hierarchy | Accept (poster) | Summary: The paper proposes a novel method for leveraging image-level annotations for object detection pretraining, specifically for zero-shot and open vocabulary detection settings. The proposed method, DetLH, leverages self-training to generate pseudo-labelled object proposals, and then adjust the pseudo-label for ea... | Rebuttal 1:
Rebuttal: **[Response 1] The affect of the reliability score:**
Thank you for your suggestion. As suggested, we conduct the new ablation study to examine the effect of the reliability score in the proposed DetLH. As the table below shows (on Object365), DetLH effectively mitigate the label noises with the ... | Summary: This paper focuses on scaling the detectors’ vocabulary with image-level weak supervision. To better leverage the image-level labels in this task, this paper introduces a language hierarchical self-training (LHST) framework that incorporates language hierarchy (i.e., WordNet) with self-training. In addition, t... | Rebuttal 1:
Rebuttal: **[Response 1] The necessity of the investigated problem:**
Thank you for your suggestion. We would clarify that, compared with recent MLLM-based detection/grounding methods, our approach of scaling the vocabulary of detectors using image labels offers significant advantages in training efficienc... | Summary: * The authors propose to expand the training data for object detection using classification datasets through two main contributions:
1. Combine a language hierarchy with self-training to extend the training dataset, while minimizing label noise by re-weighting categories with reliability scores.
2. Bridge th... | Rebuttal 1:
Rebuttal: **[Response 1] Analysis of discrepancies of the taxonomies of image vs. box categories:**
Thank you for pointing out this issue! As suggested, we analysed the mismatch between image-level and box-level categories and how much it could affect the detection performance. As the table below shows, th... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for your positive comments on our work. In particular, it is encouraging that the reviewers acknowledge that 1) our work is novel and appealing [TMzm,VMio]; 2) the proposed method is effective [TMzm,PjbE,VMio]; 3) the evaluation is extensive [TMzm,PjbE,VMio] and 4) the paper... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Confident Natural Policy Gradient for Local Planning in $q_\pi$-realizable Constrained MDPs | Accept (poster) | Summary: This is the first work which addresses and achieves polynomial sample complexity for the learning problem of CMDPs in the more general setting of linear function approximation with $q_{\pi}$ realizability. The authors propose a primal-dual algorithm and utilize a local access model (can be viewed to in betwee... | Rebuttal 1:
Rebuttal: Yes, you are correct that linear MDP implies $q_\pi$ realizability. However, $q_\pi$ realizability DOES NOT imply linear MDP, and one can show this via a counterexample. To name a few references that discuss this, please refer to Proposition 4 of Andrea Zanette et al., Learning near optimal poli... | Summary: This paper studied constrained Markov decision processes (CMDP) and proposed a confident policy gradient algorithm for $q_\pi$ realizable MDPs. Here, a $q_\pi$ realizable MDP assumes that the Q function can be approximated by a linear function w.r.t. some feature of state-action pairs. By using primal-dual met... | Rebuttal 1:
Rebuttal: Please note that our algorithm is NOT a straightforward extension of CAPI-QPI-Plan. Moreover, our paper distinguishes between the relaxed-feasibility and strict-feasibility problem settings. Treating each setting uniquely is a key strength of our work. In the relaxed-feasibility problem, the retur... | Summary: **Problem setup**
The authors consider the task of global planning in large Constrained
Discounted MDPs. They assume local access to a simulator which can be queried at previously
encountered state-action pairs to obtain a next state sample and immediate
reward, and that the $Q$-value of any policy is line... | Rebuttal 1:
Rebuttal: You are correct that the mixture policy randomly selects an index $I \in {0, \ldots, K-1}$ with probability $1/K$ at deployment and then follows the policy $\pi_I$ for all subsequent steps. The value function of the mixture policy, $v_{\bar \pi_K}(s)$, is defined as the expected return when the mi... | null | null | Rebuttal 1:
Rebuttal: We would like to begin by thanking all the reviewers for their time and dedication in evaluating our work. We start our rebuttal by emphasizing that our algorithm is not a straightforward extension of the work by Weisz et al. (2022).
First, we want to point out that the algorithm CAPI-QPI-Plan b... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Markov Equivalence and Consistency in Differentiable Structure Learning | Accept (poster) | Summary: This paper proposed a differentiable DAG learning method using the log-likelihood loss and the minimax concave penalty (MCP). The author proved that under such construction, the minimizer of the loss identifies the sparsest graph (i.e. it has the minimal number of edges) which can generate the observational di... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to the reviewer for their time, effort, and valuable suggestions. We would like to take this opportunity to address all the concerns raised in the reviews.
> The method can be regarded as a direct combination of several works. For example, the log-likelihood... | Summary: This paper introduces new identifiability results (MEC) based on maximum likelihood estimation complemented with sparsity regularization (quasi-MCP) for both a Gaussian linear model and a more general, potentially nonlinear models, under the very standard faithfulness assumption. The paper contains a theoretic... | 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.
> **Comparison to previous work**
>
We thank the reviewer for highlighting this related work;... | Summary: The authors analyze a framework of sparsity-regularized maximum likelihood learning under the NOTEARS constraint for score-based causal discovery. Drawing from the sparsest permutations principle (a hybrid method), they show that a sparsity regularized likelihood objective is able to recover an element of the ... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to the reviewer for acknowledging the value of our contributions and clarity of presentation. All the points addressed below will be updated in our paper.
> The section on scale invariance …. or remark.
We will move these details to the appendix, and shorte... | null | null | Rebuttal 1:
Rebuttal: **(1) Another example beyond the linear gaussian model.**
Note that Assumption A holds as long as $P(X_i|X_A)$ has a unique SEM parametrization for any $i$ and $A$, where $A\subseteq[p]\backslash i$. Because for any fixed topological sort $\pi$, we could let $A = \\{\text{parents node of }i \text... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multi-times Monte Carlo Rendering for Inter-reflection Reconstruction | Accept (poster) | Summary: This paper proposes a comprehensive inverse rendering method that incorporates indirect illumination to enhance the decomposition quality of environment lighting and BRDF materials. Specifically, this work uses multi-time Monte Carlo integration to model light transport and devises algorithms to accelerate com... | Rebuttal 1:
Rebuttal: 1. This is indeed a current limitation of our approach. Using Monte Carlo integration to calculate the reflection equation itself is more computationally intensive than the real-time rendering split sum method used by nvdiffrec, because we have to use ray tracing and integration. To make matters w... | Summary: This paper proposes an inverse rendering method that handles multi-time Mante Carlo integration which models indirect illumination. It reduces the computational cost by pre-computing the diffuse map based on a Lambertian model. It also proposes to use spherical Gaussian encoding to improve the initial SDF reco... | Rebuttal 1:
Rebuttal: ***Overclaim of novelty***
The paper Neural Microfacet Fields for Inverse Rendering is indeed an excellent work that considers indirect lighting, but their method for computing indirect lighting is significantly different from ours. As stated in their paper, they approximate the rendering equat... | Summary: The authors propose an inverse rendering method that reconstructs the geometry, materials, and lighting of 3D objects from 2D images, effectively handling scenes with multiple reflections and inter-reflections. To address the high computation cost of Monte Carlo sampling, the authors propose a specularity-adap... | Rebuttal 1:
Rebuttal: ***Missing validation on real data:***
Thank you very much for your instructive feedback, which pointed out the oversight in our experiments. The NeRO real-world dataset is indeed a challenging dataset because the color of the object's surface can change significantly with different viewing angl... | Summary: This paper presents a method for learning disentangled scene representations from images. The proposed pipeline has two stages: the first recovers geometry using an SDF-based model to learn geometry from the images through differentiable rendering. The second applies differentiable ray tracing to predict mater... | Rebuttal 1:
Rebuttal: ***More experiments on real-world datasets***
Thank you for your valuable suggestions. Experiments on real-world datasets are important for assessing the practicality of inverse rendering tasks. Therefore, we have supplemented the experiments on two real-world datasets, i.e., NeRD and NeRO. On ... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their constructive comments. Your suggestions have been invaluable in refining and strengthening our work. In this general response, we will address the three important parts that were commonly mentioned in the discussions, namely the experiment on the real-wor... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Interpretable Decision Tree Search as a Markov Decision Process | Reject | Summary: This paper formulated the problem of finding an optimal decision tree as Markov Decision Problem and solve the scalability problem using an information-theoretic test generation function. This method provides a trade-off between the train accuracy and tree sizes, the decision tree naturally offers interpretabi... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you in advance for engaging in a discussion with us. We appreciate your remarks that show you have studied our work well; thank you!
$\textbf{Novelty of DPDT}$
Our MDP formulation of decision tree learning is the first to be applicable to both continuous and categorical dat... | Summary: The authors pose binary decision tree construction within the framework of Markov Decision Processes. They first propose methods for constructing an MDP from a decision tree construction problem, exploring varying test generating functions that trade off the coverage of the search space vs the size of the sear... | Rebuttal 1:
Rebuttal: Dear reviewer,
We thank you so much for your review. Your commments really reflect your inverstment in reading and understanding our work and we are very excited to engage in a discussion with you!
$\textbf{DPDT has many advantages over other MDP formulations}$
It is true that various RL appro... | Summary: This paper models the construction of decision trees as a reinforcement learning problem. Currently SOTA algorithms for constructing decision trees have the drawbacks that 1) they take long to compute at depths > 3, and 2) the trees constructed are complex and difficult to interpret. By modelling the problem a... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for your comments. We are surprised by the low rating given your positive feedbacks. Please engage with us in the following discussion so that we can convince you to raise your score and accept our work.
$\textbf{Clarifying section 4: formulating decision tree learning a... | Summary: The paper proposes to use an approach for learning interpretable
decision trees using markov decision processes. The results are shown
to be competitive with branch and bound methods.
Strengths: None of notice, given the listed weaknesses.
Weaknesses: There exists extensive experimental evidence challenging ... | Rebuttal 1:
Rebuttal: Dear reviewer, we argue that learning decision trees that are interpretable in the simulatability sense (the ability for a human to read the decision path of a model from input to decision) [1, sec. 3.1.1], is a very relevant problem in machine learning. Indeed many recent works present decision t... | Rebuttal 1:
Rebuttal: Dear all, in addition to the attached 1-page pdf containing additional plots to showcase the superiority of DPDT over CART in terms of tree interpretability, with multiple seeds, we would like to share with you an open source implementation of DPDT that fits the scikit-learn framework in an effort... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Robust Conformal Prediction under Joint Distribution Shift | Reject | Summary: This paper adresses the issue of conformal prediction under distribution shift with multiple test domains. The goal is to reduce the deviation in coverage caused by different potential distribution shifts across these domains. The paper firstly proposes a way of disentangling a joint distribution shift (shift ... | Rebuttal 1:
Rebuttal: Thank you for your questions.
>My main concerns are w.r.t. practicality and evaluation. A fundamental requirement of the proposed algorithm and D-NTW is the availability of labelled samples from every test domain $Q _{XY}^{(e)}$ in order to obtain the conformal test score distributions, from which... | Summary: This paper studies the coverage difference caused by covariate and concept shifts. Authors introduce the Normalized Truncated Wasserstein distance (NTW) as a metric for capturing coverage difference expectation under concept shift by comparing the test and weighted calibration conformal score CDFs. They also d... | Rebuttal 1:
Rebuttal: Thank you for your questions.
>section 3.1 and 3.2 introduce some important definitions: while authors provide some explanation, the theoretical understanding of them are very limited.
Section 3.1 and 3.2 introduce the process of quantifying the empirical coverage gap caused by concept shift, and... | Summary: The paper "Robust Conformal Prediction under Joint Distribution Shift" investigates the problem of predictive inference under the setting where we have both covariate shift and concept shift. The authors propose a conformal prediction-based procedure that accounts for such distribution shifts and illustrate th... | Rebuttal 1:
Rebuttal: Thank you for your questions.
>It is assumed that the likelihood ratio dQ/dP is known...
**The likelihood ratio is not assumed known and it is estimated by kernel density estimation (KDE).** Please read the author rebuttal for details.
> In this work, the proposed methods have no theoretical gua... | Summary: The authors propose a method to train a predictive model (a regressor in their experiments) that minimizes an objective comprising the average performance loss across multiple domains (environments) along with a penalty term for the normalized truncated Wasserstein (NTW) distance between the non-conformity sco... | Rebuttal 1:
Rebuttal: Thank you for your questions.
> What specific benefits it offers over other state-of-the-art approaches that address differences in the non-conformity score distributions? I suggest that the authors demonstrate the validity and efficiency of the prediction intervals obtained for different alphas o... | Rebuttal 1:
Rebuttal: **Kernel Density Estimation for Likelihood Ratio**
The likelihood ratio is not assumed to be known and is approximated by kernel density estimation (KDE), which can estimate the calibration and test feature distributions. In our experiments, we applied the Gaussian kernel, a positive function of ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper tackles the challenge of obtaining conformal predictions that remain robust under distribution shifts. This is an important issue in many machine learning applications where the underlying data distribution may change across the training (or calibration) and test data sets. The authors propose an alg... | Rebuttal 1:
Rebuttal: Thank you for your suggestions and questions.
>Missing References: Important related works, such as "Conformal prediction beyond exchangeability"...
[R1] uses total variation to bound coverage gap. However, total variation measures half the absolute area between two probability density functions ... | null | null | null | null | null | null |
Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement | Accept (poster) | Summary: The paper studies the Rényi differential privacy (RDP) guarantees of the subsamplied Gaussian mechanism with fixed-sized random minibatches, when the so called add/remove neighborhood relation of datasets is considered. It uses similar techniqes as were used in the paper
Mironov, Ilya, Kunal Talwar, and Li Z... | Rebuttal 1:
Rebuttal: **Relation to Zhu et al. (2022)**
Please see the response to this comment in the general rebuttal section above. As the analysis of Zhu et al. does not investigate application to DP-SGD, the results of Theorem 11 in the DP-SGD setting are hypothetical until or unless directly demonstrated. The re... | Summary: This paper analyzes Differentially Private-SGD with fixed batch size (with and without replacement), through the lens of Rényi-DP. The bounds without replacement have a much better constant than previous ones; the ones with replacement are brand new.
Strengths: - The results are important and likely to be imp... | Rebuttal 1:
Rebuttal: **DP-SGD Specific**
That is an accurate comment, in that we do not use the fact that the additive terms involve gradients. Our method is applicable to any Gausian Mechanism with fixed-size subsampling where each sample contributes an additive term to the mean and those terms are uniformly bounded.... | Summary: The paper first proves new privacy bound for the subsampled Gaussian mechanism under fixed-size sampling with and without replacement, which improves over the tightest known prior results in (Wang et al. 2019) by a constant of four. The proofs rely on a careful coupling of the sampling processes on neighboring... | Rebuttal 1:
Rebuttal: **"The reason for the improved constant factor compared to Wang et al. is not crystal clear"**
Thanks for pointing this out. We clarified further at the end of the introduction (see also the discussion in Appendix D4) that we compute the Taylor expansion of the Renyi divergence in the sampling pro... | Summary: This paper studies the Renyi DP guarantees of a with- or without replacement subsampled Gaussian mechanism. Authors present a privacy analysis tailored for the subsampled Gaussian mechanism, which improves the earlier general bound for $\epsilon(\alpha)$ by Wang et al. 2019 by a factor of four. Authors show an... | Rebuttal 1:
Rebuttal: **Relation to Zhu et al. (2022)**
Please see the response to this comment in the general rebuttal section above.
**Lossy conversion to $(\epsilon, \delta)$ guarantees**
We acknowledge (as the reviewer correctly states) that RDP guarantees are not lossless when converted to $(\epsilon, \delta)$ ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and very useful feedback! We provide responses to each reviewer individually. Please see below for responses to a common point.
**Relation to Zhu et al. (2022)**
Reviewers uSpK and Rv66 ask whether our WOR result of Theorem 3.1 is a corollary of Theorem 11... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving Viewpoint-Independent Object-Centric Representations through Active Viewpoint Selection | Accept (poster) | Summary: This paper introduces an image selection method designed to incrementally enrich an observation set with informative images from an extensive unknown set, aiming to maximize information gain with a limited or specific number of images. The methodology innovatively integrates Multi-Viewpoint Slot Attention for ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable feedback and constructive comments. We have carefully considered each point raised and provide our responses below.
**1. Computational Complexity**
We acknowledge that our method may be more time-consuming due to the presence of two loops in the selection... | Summary: This work introduces a method for multi view object centric reconstruction. The authors extend a previous work LSD[10] from single view to multiple unposed views of the same scene and introduce an active view selection mechanism at training time. The method takes N views, decomposes the scene into K slots and ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable feedback and constructive comments. We have carefully considered each point raised and provide our responses below.
**1. Presentation**
Thank you for your feedback on the presentation. We will revise Section 3.1.2 to provide more details and clarity regar... | Summary: This paper describes a novel active viewpoint selection strategy (AVS) for enhancing multi-viewpoint object-centric learning methods. The core idea is to select the most informative viewpoints actively rather than using random or sequential strategies, which can be inefficient and may omit critical scene infor... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable feedback and constructive comments. We have carefully considered each point raised and provide our responses below.
**1. Computational Complexity**
We acknowledge that our method may be more time-consuming due to the presence of two loops in the selection... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Tropical Expressivity of Neural Networks | Reject | Summary: This paper proposes new methods to count the number of linear regions in neural networks by viewing them as tropical Puiseux rational maps. By computing their Hoffman constant, the authors are able to identify a sampling radius which ensures that all the network’s linear regions will be intersected. They use t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough reading of our work and detailed feedback. We would like to address weaknesses raised by the reviewer.
### Rigor of Tropical Geometry and Group Theory
We apologize for the confusion caused: while we focus on the tropical geometric interpretation of neural n... | Summary: This study investigates the expressive power of deep fully-connected ReLU networks (or a piecewise linear function) from the perspective of tropical geometry. The number of linear regions gives an estimate of the information capacity of the network, and the authors provide a novel tropical geometric approach t... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their feedback on our work. We are
pleased that the reviewer found our work to be sound and would like to take
this opportunity to respond to the concerns raised.
### Linear Regions as an Estimate of Information Capacity
In choosing the number of linear re... | Summary: The paper studies the expressivity of neural networks as captured by the number of linear regions using tools from tropical geometry. There are three main contributions, two of which are theoretical and the other is about open source library that allows the analysis of neural networks as Puiseux rational maps.... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their feedback on our work. We are happy that the reviewer found the soundness and contributions of our submission to be good; and that the reviewer appreciated the practicalities of our work in relation to existing theory at the intersection of tropical geometr... | Summary: This work provides a geometric characterization of the linear regions in a neural network via the input space. Although linear regions are usually estimated by randomly sampling from the input space, stochasticity may cause some linear regions of a neural network to be missed. This paper proposes an effective ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their careful and thoughtful reading
of our work. We were pleased to read that the reviewer found our work
novel and interesting, and that we were able to communicate and present
the concepts and our contributions clearly. We especially appreciate that
impor... | Rebuttal 1:
Rebuttal: We would first and foremost like to thank all the reviewers for their time invested in reading and providing thoughtful feedback on our work. We were pleased to find that they found the work well-written and clearly presented, and that they found the intersection of tropical geometry with neural n... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance | Accept (poster) | Summary: The study introduced a specialized dataset and learning approach that aimed to enhance LLMs' use of generic facts in reasoning. The results indicate that this methodology not only improved their general reasoning skills but also significantly developed their abstract reasoning abilities, suggesting a shift fro... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments, the following is the detailed response:
# 1. More experiments (Weakness 1)
We have conducted experiments on LLaMA-3 which is shown in Table 5, our MeanLearn can improve the performance of LLaMA-3 on both vanilla accuracy and AbsAcc. We do not test GPT-4/4o is b... | Summary: This paper explores the abstract reasoning abilities of LLMs by creating a specific evaluation metric and a dataset called AbsR, developed using GPT-4. It presents a method, Meaningful Learning (MeanLearn), to improves both general and abstract reasoning accuracies of LLMs by teaching them to apply generic fac... | Rebuttal 1:
Rebuttal: We would like to clarify some critical misunderstandings in the preliminary review, which may have negative impacts on your assessment of our contributions.
# 1. The paper is rushed (Weakness 1)
Both sections 2 and 5 contain results, and this arrangement is intentional and not rushed. As in many p... | Summary: This paper introduces a novel framework aimed at enhancing the abstract reasoning capabilities of large language models (LLMs) through a method called "Meaningful Learning." It specifically targets the challenge LLMs face in abstract reasoning despite their robust general reasoning abilities. The authors ident... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments, the following is the detailed response:
# 1. Limited scale (Weakness 1 and Question 4)
## 1.1 Limited scale
Due to limited computational resources, we conduct our experiments on small LLMs (7B-13B). It is worth noting that our main focus is also to improve smal... | Summary: This paper addresses the challenge that LLMs face in abstract reasoning, where they often struggle to apply general facts to new situations despite their impressive performance in other reasoning tasks. To tackle this issue, the authors introduce an abstract reasoning dataset called AbsR, which incorporates ge... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments, the following is the detailed response:
# 1. Evaluation on Advanced LLMs (Weakness 1)
The benefits of our method MeanLearn do not diminish, but it is LLaMA-3 may need more data compared with other base models. The reason is because LLaMA-3 breaks conventional d... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Boosting Perturbed Gradient Ascent for Last-Iterate Convergence in Games | Reject | Summary: This paper studies last-iterate convergence rates of online learning in monotone games. The main contribution is an algorithm called Gradient Ascent with Boosting Payoff Perturbation (GABP). The GABP algorithm achieves (1) $O(\log T / T)$ last-iterate convergence with full gradient feedback, which is near-opti... | Rebuttal 1:
Rebuttal: We are deeply appreciative of your positive feedback and constructive comments, especially your invaluable suggestions on improving our presentation. We will incorporate your feedback into our presentation to make it better. The detailed answers to each of the questions can be found below.
---
#... | Summary: The paper introduces a novel algorithmic approach to enhance the convergence of first-order methods in the context of monotone games. The authors propose a payoff perturbation technique that introduces strong convexity to players' payoff functions, which is crucial for achieving last-iterate convergence. This ... | Rebuttal 1:
Rebuttal: We thank you for your positive feedback and constructive comments. The detailed answers to each of the questions can be found below.
---
### Weakness 1
> Experimental Validation: While the paper provides empirical results, the experiments could be expanded to include a broader range of game typ... | Summary: This work focuses on last-iterate convergence of game dynamics. A payoff perturbation technique is proposed by adding strong convexity to players' payoff functions. Despite it is a well studied technqiue in learning in repeated games with first-order methods, especially in last-iterate convergence, a novel pe... | Rebuttal 1:
Rebuttal: We thank you for your positive feedback and constructive comments. The detailed answers to each of the questions can be found below.
---
### Weakness 1
> The game considered in this paper is motivated by real-life examples. But the authors only give one example motivating monotone games.
>
##... | Summary: This paper studies first order methods to solve monotone games where the gradient of the payoff function is monotone in the strategy, along with additive noise. The authors introduce a payoff perturbation technique which introduces strong convexity to the to the payoff functions and thereby derive last iterate... | Rebuttal 1:
Rebuttal: We thank you for your positive feedback and constructive comments. The detailed answers to each of the questions can be found below.
---
### Weakness 1
> The authors should include a table which compares their paper with others in the literature. This would make it easier for the reader to plac... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach | Accept (poster) | Summary: @Authors, I would appreciate it if you could point out any inaccuracies in the following summary since it took me a long time to understand your paper and am still not completely certain my understanding is correct.
The paper aims to address a common problem for molecular dynamics simulations, which is to obt... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful evaluation and valuable comments. In what follows, we aim to address the highlighted weaknesses and respond to the reviewer's questions.
## Summary & strengths:
- Thank you for your summary, while it captures the general idea of the paper, maybe it lacks on... | Summary: In this paper, the authors investigate the possibility of estimating the leading eigenvalues and the corresponding eigenvectors for the evolution operators of Langevin dynamics using biased simulations. To this end, they rely on strong statistical guarantees and on the use of deep learning regression to build ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful evaluation and valuable comments. In what follows, we aim to address the highlighted weaknesses and respond to the reviewer's questions.
## Weaknesses:
We hope that our general reply brings more clarity. We commit to incorporate all the suggestions and addi... | Summary: This paper studies an infinitesimal generator approach for learning the eigenfunctions of evolution operators for Langevin SDEs. Due to the slow mixing caused by the high potential barriers, direct learning from simulation data can be sample inefficient. Biased simulation (based on a biased potential) is used ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful evaluation and valuable comments. In what follows, we aim to address the highlighted weaknesses and respond to the reviewer's questions.
## Weaknesses:
Thank you for motivating us to present the overarching summary of our approach. While we discuss this in... | null | null | Rebuttal 1:
Rebuttal: We wish to thank all reviewers for their insightful evaluation of our paper. We appreciate all their comments and remarks, which we’ll incorporate in our revision. Before addressing each review in detail, we’d like to point out some general remarks that apply to all of them.
## Clarity of pres... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Lever LM: Configuring In-Context Sequence to Lever Large Vision Language Models | Accept (poster) | Summary: This paper proposes Lever-LM, a small language model designed to configure effective in-context demonstration (ICD) for improving the in-context learning performance of large vision-language models. The authors construct a dataset of effective ICD sequences to train Lever-LM, which then generates new ICD confi... | Rebuttal 1:
Rebuttal: **1.1.Why training auxiliary model.**
Just as you commented, ICL does not need to update the model parameters. However, this refers to LLM or LVLM, not to the auxiliary models used for selecting good ICDs. In fact, NLP researchers have developed various auxiliary methods to retrieve and order ICD... | Summary: This paper presents a novel approach called Lever LM, which uses a tiny language model to configure effective ICD sequences for LVLMs. The key innovation is leveraging the small Lever LM to select and order ICDs, improving the ICL performance of LVLMs on tasks like VQA and image captioning. The paper demonstra... | Rebuttal 1:
Rebuttal: **1. The role of CLIP**
The role of the CLIP model is to encode the data in the supporting set. Therefore, in practical applications, the dataset can be pre-encoded and stored locally.
**2. Lever-LM Inference Time.**
During inference, only two layers of Transformer decoders are needed, which r... | Summary: This paper proposes using a Tiny Lever-LM to assist in ICD selection for LVLM's ICL scenarios, thereby enhancing ICL performance without significantly increasing computational costs. Lever-LM unifies the modeling of multiple scenarios (VQA, IC) in complex multimodal ICL, eliminating the need for manually desig... | Rebuttal 1:
Rebuttal: **1. Lever-LM with Different Sizes.**
We follow your suggestion to obtain different sizes of Lever-LM by controlling the number of Transformer layers and test these Lever-LMs on the IC. As shown in Table E, we evaluate 1-layer/4-layer Transformer decoder layers. We find that the size of Lever-LM ... | Summary: The authors focus on configuring effective in-context demonstration (ICD) sequences to improve the In-Context Learinng (ICL) performance of LVLMs. The proposed Lever-LM enables the step-by-step generation of ICD configurations and simultaneously considers the selection of ICDs and the ordering of ICD sequences... | Rebuttal 1:
Rebuttal: **1. Random Sampling.**
The goal of randomly selecting samples to form the sub-supporting set $\mathcal{D_{\mathcal{M}}}$ is to enhance training data diversity, promoting Lever-LM to capture complementary knowledge among ICDs. We initially deemed this to be a strategy sub-optimal, exploring alter... | Rebuttal 1:
Rebuttal: We gratefully thank all the reviewers for their valuable and constructive feedback. We are pleased to see that the reviewers recognize our motivation: to use a tiny model to enhance the performance of in-context learning (ICL) for LVLMs. We are encouraged to see that they find our method is novel,... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CAT3D: Create Anything in 3D with Multi-View Diffusion Models | Accept (oral) | Summary: This paper proposed CAT3D, a pipeline that enabled the production of 3D representations from one or a few input views. CAT3D comprises a multi-view generation model to synthesize novel images from different viewpoints and a Zip-Nerf to achieve 3D reconstruction based on generated views. The 3D results shown in... | Rebuttal 1:
Rebuttal: Thank you for your time and careful review of our work. Below we address your questions and weaknesses mentioned:
> Most techniques have been proposed.
We agree with you that CAT3D leverages existing techniques for individual components of the system. The innovation of CAT3D lies in the effectiv... | Summary: In this paper, the authors propose a two-stage method for 3D creation. Specifically, they introduce a multi-view diffusion model to generate novel views given observed input views. Then using these views, they perform a robust 3D reconstruction using a Zip-NeRF variant. To generate consistent views, they also ... | Rebuttal 1:
Rebuttal: Thank you for your time and feedback on our work.
> larger diffusion models can boost the performance
We certainly expect that larger models will lead to improved performance and can generate more consistent novel views. One relevant piece of evidence: we experimented with different model varia... | Summary: This paper introduces CAT3D, a novel approach for generating 3D representations from a flexible number of input images. The authors tackle the challenge of limited input data, a common bottleneck for 3D reconstruction, by leveraging the power of multi-view diffusion models. Their method generates a collection ... | Rebuttal 1:
Rebuttal: Thank you for the careful reading and kind words. Below we address your questions and weaknesses mentioned:
> performance with increasing sparsity and decreasing pose accuracy
In terms of performance while varying the number of input views with accurate camera poses, Table 1 includes qualitative... | Summary: The objective of this paper is to achieve single-view or few-view to 3D. The core of their method lies in a multi-image-based diffusion model that leverages 3D attention and raymap encoding for the camera poses. This setup is different from concurrent work, IM-3D, which repurposes video generation model to ac... | Rebuttal 1:
Rebuttal: Thank you for your time and careful review of our work. Below we address your questions and weaknesses mentioned:
> cumbersome to generate a large number of viewpoints
We agree that jointly generating all target frames from the multi-view diffusion model would enable more consistent samples. Ho... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and critical feedback to improve our work. We appreciate that the reviewers found our method simple, effective, and efficient, leading to a “compelling advancement in 3D content creation.” We address individual questions below, but first highlight some sha... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Scalability of Certified Adversarial Robustness with Generated Data | Accept (poster) | Summary: The paper presents an empirical study on how synthetic data can help improve the robustness accuracy and clean accuracy of certified adversarial robustness. While existing studies have shown promising results in using synthetic data to improve empirical adversarial robustness, the effectiveness of synthetic da... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments, which we address below.
1. While we would have loved to include an even more extensive set of models (and datasets), our focus was on the best two models for the ℓ2 and ℓ∞ norms on CIFAR-10. In particular, for CIFAR-10 with ℓ∞, ε=8/255 threat model the IBP ... | Summary: The paper explores advancements in certified defenses against adversarial attacks in deep learning models by leveraging data from state-of-the-art diffusion models during training. It addresses the current challenges where empirical methods, such as adversarial training, augment data but face difficulties with... | Rebuttal 1:
Rebuttal: Thank you for taking the time to provide valuable feedback. We’d like to address your concerns regarding the weaknesses as follows.
1. This is currently a well-known general limitation of certified robustness, and we believe should thus not have any influence in how our work is evaluated. To date... | Summary: This work proposes using data augmentation with diffusion models to improve the certified robustness of image classification models. The authors analyze the training and certification behavior of different Lipschitz-bound-based machine learning models when the training data is supplemented with additional gene... | Rebuttal 1:
Rebuttal: Thank you for the feedback, and for highlighting our typo in L180, which was meant to read “1m, 5m, or 10m auxiliary data.” We’d like to address your main concerns as follows.
**Limited Novelty and Insights**
The original idea of using data from generative models trained on the original data act... | null | null | Rebuttal 1:
Rebuttal: In response to the reviewer’s feedback, we have made the following changes to the manuscript
* In both Sec. 1 (Introduction) and Sec. 7 (Conclusion) we have added further references to Hu et al. and their prior usage of DDPM generated data, and carefully reworded related claims where appropriate ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fast, accurate training and sampling of Restricted Boltzmann Machines | Reject | Summary: Training RBMs is challenging and slow due to the multiple second-order phase transitions and associated slow mixing of MCMC sampling. The paper introduces a pre-training method, consisting in integrating the principal directions of the dataset into a low-rank RBM through a convex optimization procedure. The Gi... | Rebuttal 1:
Rebuttal: We thank the referee for his comments and questions.
**Q1:** The main reasons for using RBMs are twofold: they are easy to interpret and are particularly well suited for tabular datasets with discrete variables such as DNA or protein sequences, both in terms of generation power and sample efficie... | Summary: The work proposes to pretrain RBMS with a recently developed convex approximation, the restricted coulomb machine and then fine-tune the model using standard techniques like PCD. Further, a novel sampling technique, PTT is proposed that can sample from the final trained model by employing a sequence of model s... | Rebuttal 1:
Rebuttal: We thank the reviewer for its comments and suggestions.
**1. PT:** The reviewer disagrees with us on the overall performance of standard PT in RBMs. We have several comments on this. First, the reviewer disagrees with the statement that PT is costly and assesses that the reliability of PT in RBMs ... | Summary: The manuscript suggests to apply
Aurélien Decelle and Cyril Furtlehner. Exact training of restricted Boltzmann machines on intrinsically low dimensional data. Physical Review Letters, 127(15):158303, 2021.
to initialise persistent contrastive divergence (PCD) learning for RBM training and estimating the log... | Rebuttal 1:
Rebuttal: We thank the reviewer for its careful reading of the paper and for its constructive comments. We attempt to respond to the comments individually below.
The answers to the concerns about **novelty of this paper** have been answered in the section "Author Rebuttal", as they were shared by several r... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their comments. We answer the question about the novelty of this new work in this Author rebuttal, as this question was raised by several reviewers. We answer the remaining questions directly in each reviewer's rebuttal.
**On the novelty:** Reviewers yudf and SD84 rais... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generated and Pseudo Content guided Prototype Refinement for Few-shot Point Cloud Segmentation | Accept (spotlight) | Summary: This paper introduces a FS-3DSeg framework called Generated and Pseudo Content guided Prototype Refinement (GPCPR) for few-shot 3D point cloud semantic segmentation, leveraging LLM-generated content and reliable query context to enhance prototype quality. GPCPR includes two core components: Generated Content-g... | Rebuttal 1:
Rebuttal: > **W1. Novelty of using LLM. Specific design.**
1) Novelty:
FS-3DSeg is more complex than 2D, due to 3D data is complex. No prior work has applied LLMs to FS-3DSeg. Directly extending [1][2] to FS-3DSeg faces challenges: [1] finetunes LLMs and outputs 2D polygon coordinates, unsuitable for 3D ob... | Summary: This paper analyses and addresses two issues of prototype-based methods in the Few-shot Point Cloud Segmentation task. For the constrained semantic information issue, they present the GCPR module to enrich prototypes with text knowledge via LLM and CLIP. For the class information bias issue, the proposed PCPR ... | Rebuttal 1:
Rebuttal: Dear Reviewer FCL4,
We thank you for taking the time to review our manuscript and offer detailed and constructive comments. We appreciate your positive reception of our work and carefully considered each of your points and would like to address your comments as follows:
> **1. Computing cost and... | Summary: This paper targets few-shot 3D point cloud semantic segmentation and proposes GPCPR to explicitly leverage the LLM-generated content and query context to enhance the prototype quality. The component GCPR integrates diverse and differentiated class descriptions generated by LLMs to enrich prototypes. The compon... | Rebuttal 1:
Rebuttal: Dear Reviewer ABMp,
We would like to express our gratitude for taking the time to review our manuscript and providing detailed and constructive feedback. We appreciate your positive reception of our work and carefully considered each of your points and would like to address your comments as follo... | null | null | Rebuttal 1:
Rebuttal: To PC, AC, and all Reviewers:
We sincerely appreciate the time and effort the PC, AC, and all reviewers have dedicated to reviewing our work. We are grateful for the detailed and thoughtful feedback on our submission, particularly the positive comments and insights. Below, we summarize the streng... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LaSCal: Label-Shift Calibration without target labels | Accept (poster) | Summary: The paper addresses the problem of confidence calibration under label shift given unlabeled samples from the target domain. The first step is estimating the label distribution of the target domain. Then a calibration parameter is computed using the source domain samples where each sample is reweighted accordi... | Rebuttal 1:
Rebuttal: **Can you clearly state the novelty compared to covariate shift methods?**
Please see our general response for the perceived lack of novelty. To summarize:
- This paper introduces the first label-free, consistent estimator for target calibration error under label shift. Estimating calibration e... | Summary: This paper proposes a novel calibration error estimator under label shifts (without ground truth). They use this estimator to apply standard calibration techniques such as temperature scaling on the unlabelled target set, allowing to calibrated the model for the target domain without needing access to a labell... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback and suggestions. We implemented these baselines and performed additional experiments, which we will include in the camera-ready version. We address each question below:
1. **Add at least one more relevant baseline for model calibration under label shift**
We c... | Summary: This work considers the problem of model calibration under label shift with label-free data. The work obtains the unsupervised calibration error through the kernel-based estimation of the TARGET data with the SOURCE data and applies it to the TS calibration method. The problem's has some novelty and is technic... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review and feedback on our paper. We provide an explanation to the questions below:
1. **Some of the baselines use source data for calibration.**
While it is true that compared to i.i.d calibration methods, unsupervised calibration methods (LaSCal, HeadToTa... | Summary: This paper proposes a consistent estimator of class-wise expected calibration error (class-wise ECE) for unsupervised domain adaptation under label shift assumption, i.e., the class proportion of the source $p_s(y)$ and target distribution $p_t(y)$ differs while the class-conditional probability $p(X|y)$ remai... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback and future work suggestions. We performed further experiments which serve as an interesting addition to our paper. Regarding the questions/concerns:
1. **Proposed method is perceived as straightforward**: While our method builds upon the well-known importance w... | Rebuttal 1:
Rebuttal: We want to thank the reviewers for the constructive feedback on our paper. We appreciate the provided insights and are pleased to see the recognition of several strengths in our work. Below, we summarize the main strengths and weaknesses highlighted by the reviewers, and address them accordingly.
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Deep Bayesian Active Learning for Preference Modeling in Large Language Models | Accept (poster) | Summary: This work proposes BAL-PM, a stochastic acquisition policy aiming to select informative data samples (i.e., the prompt and its corresponding paired responses) that need to collect human feedback for LLMs' preference learning.
Specifically, BAL-PM mainly addresses the issue of sampling redundant data points in ... | Rebuttal 1:
Rebuttal: Thank you for sharing your concerns and questions. We address them below:
**Q14** Why is the Log-Likelihood metric used for evaluating Preference Modeling?
> We refer to **Q1** (global rebuttal).
**W3** The task is limited to text summarization.
> **R3** We understand the importance of differen... | Summary:
The authors present a method, Bayesian Active Learning for Preference Modeling (BAL-PM) that seeks to learn a preference model in a sample-efficient manner within an active learning setting. A key insight by the authors is the consideration of task-dependent and task-agnostic uncertainty to encourage d... | Rebuttal 1:
Rebuttal: Thank you for sharing your concerns and questions. We address them below:
**Q8** What is the motivation for being sample-efficient in the pool-based active learning setting?
> **A8** We clarify that the goal of a pool-based setup is to mimic an open-ended data selection setup. Although we use lo... | Summary: This paper proposed a novel framework to select the most informative preference data for training based on Bayesian active learning.
To collect the prompt-response pair (x,y), it firstly selects the prompt based on Bayesian Active Learning by Disagreement by maxmizing the information gain. Then, the selection... | Rebuttal 1:
Rebuttal: Thank you for highlighting the strengths of our work and for bringing up your concerns and questions. We aim to address them in this response.
**Q6** How does BAL-PM perform in comparison with other sampling methods [1, 2, 3, 4]:
> **A6** Thank you for the references. We highlight that these wor... | Summary: This paper presents BAL-PM, a Bayesian active learning framework for the training preference model. The authors propose an acquisition policy that seeks examples that have high epistemic uncertainty and can maximize training data’s entropy. Specifically, the epistemic uncertainty is estimated by the training p... | Rebuttal 1:
Rebuttal: Thank you for appreciating our work strengths and for raising concerns and questions. We hope to clarify them in this response. Please see the answers below:
**W1** "The evaluation metric is limited and gives no guarantee of more accurate judgment or better fine-tuned LMs."
> **R1** We kindly ref... | Rebuttal 1:
Rebuttal: We thank the reviewers for raising concerns and providing feedback to improve our work. We appreciate the acknowledgement that:
- **The paper is clear and well-written** (x99G, vibJ, tUWV);
- **The proposed method is principled and well-motivated** (all reviewers);
- **Empirical results are stron... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable | Accept (poster) | Summary: This work proposes B-cosification, a method to transform a pre-trained network to a B-cos network [1,2]. Consequently, the transformed model can be finetuned for better explanations of the model behavior while retaining predictive performance. Experiments are done on various CNN and Transformer architectures, ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and valuable feedback. We appreciate your positive remarks on our work's significance and your constructive suggestions for improvement. We have carefully considered your comments and would like to address each point you raised.
- **Inherent Interpretability of B... | Summary: This work builds on the recent line of works aiming to design inherently interpretable models. In particular, it considers the recently proposed B-cos networks and shows how pre-trained conventional CNN/VIT models can be converted into a B-cos network through fine-tuning. The authors first discuss which parts ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We appreciate your recognition of the significance of our work and your constructive suggestions. We have carefully considered your comments and would like to address each of the points you raised.
- **How do localization scores vary with epochs?:** As we writ... | Summary: This work targets the interpretability of modern architectures via a process that the authors call B-cosification. Contrary to the original B-cos networks that are trained from scratch by architecturally enforcing alignment between inputs and weights, B-cosification constitutes a post-hoc method, aiming to con... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback on our work. We have carefully considered your comments and would like to address each point you raised.
- **Limited novelty**: To the best of our knowledge, we are the first to investigate how to transform existing uninterpretable models into inherently in... | Summary: The authors discuss the B-cosification of a pre-trained model.
The B-cosification involves changing the operations performed in the linear layers to one involving a cosine. Not all properties of B-cos models are in the end satisfied, yet performance in terms of accuracy and localization are on par with a fully... | Rebuttal 1:
Rebuttal: Thank you for the detailed feedback and the suggestions to improve clarity; we will incorporate them as well as the following answers in our revision.
- **Full fine-tuning**: The research question we examine is how to leverage existing pre-trained uninterpretable models to make training more inter... | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed comments and constructive feedback. We are encouraged to find that the reviewers appreciate that our proposed approach allows us to convert pre-trained models to be interpretable whilst maintaining performance (Z7HS, DdYE). We are further encouraged to fin... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HHD-GP: Incorporating Helmholtz-Hodge Decomposition into Gaussian Processes for Learning Dynamical Systems | Accept (poster) | Summary: This paper proposes a novel dimensionality reduction method.
The method relies on the Helmholz-Hodge decomposition to identify the dynamical system through a decomposition into a curl-free and a divergence-free part.
Furthermore, the method introduces a way to incorporate priors that constrain the identified v... | Rebuttal 1:
Rebuttal: # Response to Reviewer KheP
We are very grateful for your valuable comments and acknowledgement of our main contributions. We sincerely appreciate your time and effort in reviewing our paper. Here is our response to your comments.
---
> **Question 1**: How do you know what priors to choose? Isn... | Summary: The authors tackle the problem of modelling dynamical systems in scenarios where it may not be possible to straightforward to determine the exact form of the ODEs governing the system and optimise their parameters directly. Whilst physics-informed Bayesian models which learn divergence-free vector fields are c... | Rebuttal 1:
Rebuttal: # Response to Reviewer X44d
Thank you very much for your thoughtful review and constructive comments. We are very pleased that you recognize the importance and contribution of our work. We have carefully gone through your comments and suggestions, and we believe addressing these points in the man... | Summary: The paper formulates a vector-valued GP model to infer unknown vector fields. The authors discuss how to impose symmetry-based constraints on the GP models to ensure physically meaningful decompositions. The paper provides theoretical proofs to support the construction of curl-free and divergence-free vector f... | Rebuttal 1:
Rebuttal: # Response to Reviewer w6Wk
Thank you very much for your constructive comments and for taking the time to review our manuscript. We've carefully read your comments and our response is as follows.
---
> **Question 1**: In [R1], a Gaussian Process for dissipative Hamiltonian systems has been prop... | Summary: This paper derives a G-equivariant versions of the both curl (through the Haar Integration kernel) and divergence free (through GIM kernel) kernels. This is then used to define a prior over the Helmholtz decomposition and is identifiable wrt to relevant functions in the Euclidean group (translation, rotation, ... | Rebuttal 1:
Rebuttal: # Response to Reviewer DJX1
We are very grateful for your valuable comments and acknowledgement of our main contributions. We greatly appreciate the time and effort you put into reviewing our paper. Below are our responses to your questions.
---
> **Question 1**: Is the proposed symmetry-preser... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Layer-Adaptive State Pruning for Deep State Space Models | Accept (poster) | Summary: This submission proposed to conduct pruning on Deep State Space Model (DSSM). Basically, it formulates the output distortion (energy loss) after pruning, the derive that the importance of state is related to $H_{\inf}$ norm, which is used as the pruning criteria. Besides, it proposes to use a greedy optimizati... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and suggestions based on their insightful paper summary. We appreciate their assessment that our method, which is closely coupled with the structure of DSSMs, is particularly interesting.
### **Weakness:**
> Since it is a pioneer work, more comparison meth... | Summary: In this paper, the authors propose Layer-Adaptive $\mathcal{H}{\infty}$ STate pruning (LAST), a deep state space model (SSM) pruning method that optimizes the state dimension of a deep diagonal SSM in terms of model-level energy loss. Experimental results on different tasks demonstrate that the proposed method... | Rebuttal 1:
Rebuttal: We are glad the reviewer appreciated the novelty and effectiveness of the proposed method. As the reviewer suggested, we compare our method to three magnitude pruning methods that use magnitude-based criteria from traditional DNNs and state pruning granularity as in the proposed method.
State pru... | Summary: Inspired by the traditional layer-adaptive neural network pruning, this paper develops and verifies a layer-adaptive model order reduction (MOR) method to reduce the state dimension in DSSM models. The proposed method reveals state importance and prunes insignificant subsystems for a desired compression level,... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's assessment that the paper contributes to new areas of DSSMs and the experiments are thorough to show the robustness and effectiveness of the proposed method. The reviewer's concerns were the practical impacts and related works of reducing state dimension.
> Please use... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers and ACs for their efforts in reviewing our paper. We are glad that the reviewers found the proposed method novel and noted that the experiments support its effectiveness. We sincerely appreciate the insightful and constructive feedback, and we have carefully responded to all... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups | Accept (poster) | Summary: The paper addresses the problem of online prediction from experts (OPE) under differential privacy (DP) constraints in a federated setting. OPE operates in a set of rounds, and consists on choosing at each round the expert that minimizes the regret over observations of data. The selection of the expert at each... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and thoughtful comments. We address the reviewer's questions in the following and will revise our paper accordingly. We hope the responses below address the reviewer's concerns.
**Q1:** There are certain non-major weaknesses in motivating the proble... | Summary: The paper studies the problem of online federated expert selection. In order to make the proposed algorithm robust against adversaries, the paper proposes algorithms with differential privacy guarantees. Both stochastic and oblivious adversaries are investigated by the paper. The paper provides theoretical gua... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and thoughtful comments. We address the reviewer's questions in the following. We hope the responses below address the reviewer's concerns.
**Q1:** The paper can benefit from extending its experimental study.
**A1:** Thanks for the helpful suggesti... | Summary: This paper studies differentially private federated online prediction from experts against stochastic and oblivious adversaries. The goal is to minimize average regret across clients over time with privacy guarantees. For stochastic adversaries, the proposed Fed-DP-OPE-Stoch algorithm achieves regret improveme... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and thoughtful comments. We address the reviewer's questions in the following and will revise our paper accordingly. We hope the responses below address the reviewer's concerns.
**Q1:** The lack of experiments on real-world dataset scenarios is a si... | Summary: The paper studies the problems of differentially private federated online prediction from experts against both stochastic adversaries and oblivious adversaries. The main contributions are three-fold. First, for stochastic adversaries, the paper proposes a differentially private mixture of experts algorithms an... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading and thoughtful comments. We address the reviewer's questions in the following and will revise our paper accordingly. We hope the responses below address the reviewer's concerns.
**Q1:** My only concern of the paper is on the application side. While ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their feedback, which has greatly improved our paper. We are glad that our work is recognized for studying "an important and unexplored research area" (Reviewer RFE1) and developing Fed-DP-OPE-Stoch with "novel techniques" (Reviewer hhm9) to handle stochastic adversaries... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration | Accept (poster) | Summary: This paper introduces the Rank Calibrated Class-conditional Conformal Prediction (RC3P) algorithm, designed to address the issue of large prediction sets in conformal prediction (CP), especially in imbalanced classification tasks. The RC3P algorithm enhances class-conditional coverage by selectively applying c... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. Below we provide our rebuttal for the key questions from the reviewer.
**Q1: The theoretical analysis of the predictive efficiency could be expanded.**
A1: We agree that a deeper exploration of the conditions under which RC3P outperforms CCP ... | Summary: This paper introduces the Rank Calibrated Class-conditional CP (RC3P) algorithm, which reduces prediction set sizes while ensuring valid class-conditional coverage by selectively applying class-wise thresholding. The RC3P algorithm achieves class-wise coverage regardless of the classifier and data distribution... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. Below we provide our rebuttal for the key questions from the reviewer.
**Q1: Some notions, especially the Y and y, are confusing. Similarly, k is for label class, $\hat k(y)$ is for a different notion of label rank, but both uses k.**
A1: Our... | Summary: This paper aims to reduce the prediction set sizes of conformal predictors while achieving class-conditional coverage. The authors identify that class-wise conformal prediction (CCP) scans all labels uniformly, resulting in large prediction sets. To address this issue, they propose Rank Calibrated Class-condit... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. Below we provide our rebuttal for the key questions from the reviewer.
**Q1: Why only consider imbalanced datasets? Report the performance on balanced data?**
A1:
RC3P is a general class-conditional CP method and works for the models traine... | Summary: The paper proposes a new algorithm called Rank Calibrated Class-conditional CP (RC3P) that augments the label rank calibration to conformal classification calibration step. It theoretically proves it
Strengths: - Overall, the idea is clearly presented, and the motivation behind the problem—improving efficienc... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. Below we provide our rebuttal for the key questions from the reviewer.
**Q1: R3CP heavily relies on the ranking of candidate class labels?**
A1:
RC3P does not heavily rely on model’s label ranking to guarantee the class-conditional coverage,... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback. Below we provide a summary of our rebuttal for key questions from reviewers’ as a global response.
\
**GR1. We added 7 new experiments for all CP baselines and RC3P** (We train the model from scratch in imbalanced settings by [r1])
**(1) B... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators | Accept (poster) | Summary: The paper addresses the challenge of evaluating new sequential decision-making policies using OPE techniques. The authors propose a new algorithm, OPERA, which adaptively blends multiple OPE estimators to improve the accuracy of policy performance estimates without relying on explicit selection methods. This a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review.
> How should we construct the set of estimators to perform the proposed algorithm? Can the authors propose any general guideline?
Thank you for the suggestion. We are adding guidelines as a section in the appendix, and we summarize them here:
1.... | Summary: The paper "OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators" deals with the challenge of evaluating new decision-making policies using past data, which is vital in areas like healthcare and education where mistakes can be costly.
The authors introduce OPERA, a new a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review.
> the paper could really use a more detailed discussion on how to implement OPERA in practice. Offering guidelines or best practices for using OPERA in different situations would be very helpful for practitioners.
Thank you for the suggestion. We ... | Summary: The authors propose a novel offline policy evaluation algorithm, that linearly blends the estimates from many OPE estimators to produce a combined estimate that achieves a lower MSE.
Strengths: The paper is very well written, complete, and easy to read.
The experiments are well executed, the methods are eval... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review and pointing out minor errors. We have corrected the inconsistent symbols and clerical mistakes. Much appreciated!
> The authors propose to estimate MSE of \hat{V}_i by estimating \pi using bootstrapped D_n and calculating the squared error from an ... | Summary: The paper introduces OPERA, an algorithm for offline policy evaluation (OPE) in reinforcement learning (RL). OPERA addresses the challenge of selecting the best OPE estimator by adaptively combining multiple estimators using a statistical procedure that optimizes their weights to minimize mean squared error (M... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback! We incorporated these discussions into the paper but respond to them individually here.
> OPERA rely on the assumption that at least one of the base estimators is consistent. However, in practice, this assumption may not always hold. The paper... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Monte Carlo Tree Search based Space Transfer for Black Box Optimization | Accept (spotlight) | Summary: This paper propose a search space transfer learning method based on Monte Carlo tree search, called MCTS-transfer, to iteratively divide, select, and optimize in a learned subspace. It can provide a well-performing search space for warm start for the target problem based on the source problems. It adaptively i... | Rebuttal 1:
Rebuttal: Thanks for your valuable and constructive comments. Below please find our responses.
### Q1: Running time and computational cost analysis
Thanks for your valuable suggestions. Please refer to Q1 in general response.
### Q2: Discussion between the state value in MCTS of AlphaZero and MCTS-Transfe... | Summary: This paper proposes a search space transfer learning method based on Monte Carlo tree search (MCTS) called MCTS-transfer, which aims to accelerate the optimization process in computationally expensive black-box optimization problems.
Strengths: - Originality: The integration of MCTS with search space transfer... | Rebuttal 1:
Rebuttal: Thanks for your valuable and constructive comments. Below please find our responses.
### Q1: How does MCTS iteratively divides and selects subspaces?
We're sorry that we didn't make this part clear.
- How does MCTS iteratively divide subspaces? As described in Section 3.1, the division of spac... | Summary: This paper proposes a new space transfer method for black-box optimization by using MCTS. The search space is divided by MCTS, and the data from source tasks are used to help evaluate the value of each node of the tree. The similarity between the source and target tasks is considered and adjusted dynamically. ... | Rebuttal 1:
Rebuttal: Thank you for carefully reviewing our paper and providing constructive comments, which have helped improve the work a lot. We are very glad that you appreciate our work. Below please find our responses.
### Q1: The running time comparison of each iteration
Thank you for your valuable suggestions... | Summary: The paper proposes a tree-based search space division to enable transfer across different instances of related optimisation problems.
The authors propose both a scheme to divide the search space in a hierarchical fashion based on training task samples as well as a way of weighing the resulting subspaces agains... | Rebuttal 1:
Rebuttal: Thank you for carefully reviewing our paper and providing constructive comments, which have helped improve the work a lot. Below please find our responses.
### Q1: Lack of theory
Thank you very much for your suggestions. We fully agree that theoretical analysis of MCTS-transfer is a very interes... | Rebuttal 1:
Rebuttal: We are very grateful to the reviewers for carefully reviewing our paper and providing constructive comments and suggestions. Our response to individual reviewers can be found in the personal replies, but we also would like to make a brief summary of revisions about writing, discussion, and experim... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Simplifying Constraint Inference with Inverse Reinforcement Learning | Accept (poster) | Summary: This paper proposes a way to reduce the tri-level stucture of ICRL to bi-level, and uses solid experiment results to validate that this bi-level reformulation achieves better expirical results. The authors also intuitively explain that this is due to the fact that the tri-level optimization has a more complica... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful consideration of our paper. We understand that your primary concern with the paper is that our principal claim is trivial. While we understand that the derivation is relatively straightforward, we do not think the reduction is necessarily trivial, as evidenc... | Summary: The paper proposes a novel inverse constraint learning approach that leverages inverse reinforcement learning (IRL). Prior work by Kim et al. and Malik et al. proposed a game-theoretic approach to the constraint learning problem, where the resulting optimization problem is a tri-level optimization problem invo... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful consideration of our paper and the helpful comments and suggestions. We would hope that the improvements we have made and highlighted in the overall response will alleviate some of your concerns, but we would like to address each of your concerns here specif... | Summary: The paper explains how inverse constrained RL (ICRL) - the task of recovering (safety) constraints from expert behaviour respecting those constraints - is equivalent to (straight) inverse RL (IRL) - the task of recovering a reward function from expert behaviour approximately optimizing that reward. This allows... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful consideration of our work. We are very glad that you found our work valuable and greatly appreciate your suggestions for improvement.
Regarding your main concern, “the statistical methodology is somewhat underwhelming in Section 5”, we have incorporated man... | Summary: The authors propose a method for learning the constraints from demonstrations. To achieve this goal they take note of the similarities between inverse reinforcement learning (IRL) and inverse constraint reinforcement learning (ICRL). The authors aim to reduce the tri-level optimization of constrained inverse r... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful consideration of our work. We would hope that the improvements we have made and highlighted in the overall response will alleviate some of your concerns, but we would like to address each of your concerns here specifically:
- “The authors' claim that the tr... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful comments and suggestions. We agree with the reviewers that the main claim of our paper is that treating ICRL as a separate problem from IRL may not confer a particular benefit and, in fact, it should be beneficial to not segment the problem class because... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models | Accept (spotlight) | Summary: This paper addresses the important problem of why Adam outperforms SGD for language tasks, proposing that heavy-tailed class imbalance in the training dataset is the key factor in this performance gap. A series of experiments demonstrate that Adam consistently outperforms SGD under heavy-tailed class imbalance... | Rebuttal 1:
Rebuttal: **Weight blocks for the gradient-Hessian correlation plots**
> In the gradient norm and Hessian trace plots in Figures 24-26 in Appendix G, how are the weight blocks $w_c$ defined? Do they correspond to the last layer of each architecture?
This is correct. The plots show the last layer for each p... | Summary: This paper argues that heavy-tailed class imbalances in natural language datasets, which follow because some words are much more frequent than others, causes (or significantly underlies) the performance gap typically observed between Adam and SGD. To make this argument, the authors
1. reproduce the performanc... | Rebuttal 1:
Rebuttal: **Clarification**
> Do you know why SGD eventually finds the minimum in the imbalanced MNIST case but does not in the imbalanced ImageNet?
We think MNIST and ImageNet might have been flipped in this comment. GD and Adam are closer at the end of the given budget on imbalanced ImageNet (Fig. 3) t... | Summary: The paper shows that under the class imbalance setting which is natural in language tasks, Adam can be faster than SGD. Meanwhile, the authors investigate the linear model deeply showing the relationship between gradient and Hessian and the convergence speed of sign-gd and gd algorithm.
Strengths: 1. The auth... | Rebuttal 1:
Rebuttal: **Does the conclusion still hold when the parameters are not separable**
> When the parameters are not separable, does the conclusion still hold?
> Since the optimal solution of NN can not be infinity due to some generalization constraints (e.g. adding weight decay), will the sign algorithm still ... | Summary: This paper investigates the reason why Adam outperforms (S)GD by a large margin on language models when the performance gap is much smaller in other settings. The authors argue that language data often has a heavy-tailed class imbalance, where a large fraction of the data consists of several classes with a sma... | Rebuttal 1:
Rebuttal: **Clarification on barcoded MNIST**
> For the Barcoded MNIST dataset, I think the number of new images should be $5\times 10 \times (2^{10}-1)$ since one of the 10-bit patterns would be the same as the background. Can the authors clarify this?
Good catch, thanks! You are correct, the all-0 10-bit... | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful readings and thoughtful comments.
We answer specific questions in individual replies:
- [**ujWn:** Clarification on barcoded MNIST](https://openreview.net/forum?id=T56j6aV8Oc¬eId=2QGkcNTqaR)
- [**HWpb:** Does the conclusion still hold when the paramete... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language Models | Accept (poster) | Summary: The paper proposes a new method named LaPael to enhance knowledge injection for large language models. Different from traditional data-level augmentations or noise-based methods, LaPael operates at the latent level, preserving the semantic integrity of the text while introducing meaningful variability. Experim... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive and helpful comments. We initially address all your concerns and questions below.
`[4-1] Difference between the previous peturbation methods`
> W1. The difference between the method proposed in this paper and the previous perturbation or enhancement me... | Summary: This paper focusses on the ability of LLMs to learn new knowledge. Previous work has shown that paraphrasing techniques help learning this new knowledge. However, as the authors argue, explicit paraphrasing has a high computational cost, and the paraphrased data is of limited diversity. To circumvent this, thi... | Rebuttal 1:
Rebuttal: We do appreciate your positive feedback and valuable suggestions, and we hope our response fully addresses your concern.
---
`[3-1] Regarding new knowledge injection`
> W2. As a related point, I am wondering to what extent the used datasets can really evaluate new knowledge injection.
> Q1. It ... | Summary: This paper presents LaPael, a novel approach to injecting new knowledge into Large Language Models (LLMs). Previous works have shown that fine-tuning the model with data augmented by paraphrasing helps the model learn new knowledge. However, this requires high-quality paraphrased data each time new knowledge i... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments on our work. We understand your concerns and are certain that your comments will help improve the quality of our work. Should you have any unresolved concerns, please let us know. We are happy to discuss and do our best to address your concerns.
---
... | Summary: This paper proposed a latent paraphraser to generate paraphrased data which will be used as augmented data in LLMs' fine-tuning.
To tackle the challenges of repetitive external model interventions, the latent paraphraser (LaPael) is trained to add a small perturbation at the latent feature level of the LLM and... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive and helpful comments. We initially address all your concerns and questions below.
---
`[1-1] Regarding the comparative analysis of paraphrased sentences`
> W2. Lack of qualitative analysis of show how the phrased sentences differ from the given paraphr... | Rebuttal 1:
Rebuttal: (R1=R-Zuau, R2=R-bjHu, R3=R-WC5j, R4=R-NFcZ)
We sincerely thank the reviewers for their thoughtful and constructive feedback. We appreciate the acknowledgment that the paper is well-written and organized (R1, R4), the superiority of the proposed method (R1, R2, R3, R4), the insightfulness of the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The ALCHEmist: Automated Labeling 500x CHEaper than LLM Data Annotators | Accept (spotlight) | Summary: The paper proposes a labeling workflow (Alchemist) using LLMs where instead of labeling each data point using a teacher model, we ask the teacher model to generate a program to label for the given task. Multiple such programs are generated and using an aggregation function, we get pseudo labels. These pseudo l... | Rebuttal 1:
Rebuttal: ### Response to Reviewer b4or
We are grateful for the review and the positive assessment. Thank you for acknowledging Alchemist is novel, interesting, and our paper is well-written. We address your questions below.
* **On Performance Degradation.**
* In the majority of cases (six out of ... | Summary: The authors propose an innovative solution for high cost of APIs that using large pretrained models to generate programs that act as annotators. This idea helps replace or supplement crowdworkers and allows for distilling large models into smaller, more specialized ones. Traditional methods can be costly and p... | Rebuttal 1:
Rebuttal: ### Response to Reviewer d5ZD
Thank you for recognizing Alchemist’s flexibility, extensibility, and for your kind words about our paper! We address your questions below and include experimental results on more complex tasks and program stability.
* **On More Complex Tasks.**
* Alchemist is n... | Summary: The paper presents a new method for creating data labels that leverage a Large Language Model and the weak supervision/data programming labeling paradigm. In this work, the LLM is used to generate labeling code, typically in the form of functions, which can then be used to create weak labels for weak supervisi... | Rebuttal 1:
Rebuttal: ### Response to Reviewer uwkF
We are grateful for your review and for describing our paper as significant, well-written, and of high quality. We address your questions below and provide additional experimental results incorporating Roboshot into Alchemist!
* **On Grounding.**
* Thank you for ... | Summary: The paper proposes an automated way to label large quantities of data by leveraging large language models to generate labeling functions which can be used to label data using programmatic weak supervision. The paper demonstrates that this procedure can generate labeling functions which are more accurate than m... | Rebuttal 1:
Rebuttal: ### Response to Reviewer n36E
Thank you for recognizing our paper as well-written with well-designed experiments. We appreciate your acknowledgment of our simple, effective idea and the significance of including diverse modalities. We appreciate your thoughtful review!
* **On Comparisons with Re... | Rebuttal 1:
Rebuttal: ### General Response
We are grateful for all the comments and constructive feedback on our work. Reviewers consistently found our paper to be well-written and easy to follow and described our work as novel and offering promising performance.
Reviewer **n36E** complimented the inclusion of complex... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Universal Rates for Active Learning | Accept (poster) | Summary: This work provides a characterization of distribution-dependent learning rates for realizable active learning in terms of combinatorial complexity measures on the hypothesis class -- the main result is that any hypothesis class is universally learnable (i.e. learnable with rate $CR(cn)$ for distribution depend... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for their detailed feedback. Please find our answers to your questions below.
> Pages 8-9 probably should be polished a bit before publication. I think the content of Appendix A.4, which compares the results to passive and interactive learning analogues of this... | Summary: This paper deals with universal active learning of binary classes in the realizable setting, which continues important work in related universal settings (interactive, online, ...). The authors propose a star number based VCL-tree variant, which together with original VCL-trees characterize the 4 possible rat... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for their detailed feedback. Please find our answers to your questions below.
> Intervals and Balcan et al. [2010].
This is a good point to clarify. Balcan et al. (2010) distinguish between the *true* query complexity and the *verifiable* query complexity of a... | Summary: This paper studies active learning for binary classification. The authors provide a complete characterization of the optimal learning rates for non-adaptive active learning algorithms. The authors also develop an active learning algorithm for partial concept classes with exponential rates.
Strengths: The auth... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for their detailed feedback. Please find our answers to your questions below.
> It seems that the analysis heavily relies on the assumption that the active learning algorithm is non-adaptive. For completeness, can authors provided concrete examples of (i) non-a... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Frequency Adaptive Normalization For Non-stationary Time Series Forecasting | Accept (poster) | Summary: This paper introduces FAN, a novel instance normalization technique designed to address both dynamic trends and seasonal patterns. FAN is a model-agnostic method that can be integrated with various predictive models. It significantly enhances performance, achieving average MSE improvements of 7.76% to 37.90%.
... | Rebuttal 1:
Rebuttal: We appreciate all your suggestions and hope that the following answers have clarified your questions.
**Q1: lacking experimental descriptions, e.g. dataset metrics Trend and Seasonality Variation.**
Thank you for your valuable advice. Due to page limitations, we have briefly discussed the calcu... | Summary: The paper introduces Frequency Adaptive Normalization (FAN) to improve time series forecasting by addressing non-stationary data with evolving trends and seasonal patterns. Unlike reversible instance normalization, which handles trends but not seasonal patterns, FAN employs the Fourier transform to identify an... | Rebuttal 1:
Rebuttal: We appreciate your constructive suggestions as they can aid in enhancing our work. We hope that responses below have addressed your concerns:
**Q1: performance gap in Tab 2 and reported by SAN.**
Thanks for this question. We have rechecked the results, papers, and codes to identify the reasons f... | Summary: This paper proposes a new instance normalization solution called frequency adaptive normalization (FAN) to address non-stationary data in time series forecasting. This paper extends instance normalization to handle both dynamic trend and seasonal patterns by employing Fourier transform to identify predominant ... | Rebuttal 1:
Rebuttal: We are grateful for your positive feedback on our work, hope that the answers provided below have resolved your inquiries:
**Q1: missing related work. e.g. DEPTS (ICLR 2022), FreTS (NIPS 2024), DERITS(IJCAI 2024).**
Thank you for bringing these works to our attention! These studies indeed help ... | Summary: This paper presents FAN - frequency adaptive normalization - as an alternative approach to de-trending seasonality in non-stationary data through Fourier transform decomposition. The method relies on dynamically identifying K instance-wise predominant frequency components; the evolution of these components is ... | Rebuttal 1:
Rebuttal: We thank Reviewer LAap for such a comprehensive review of our work. We hope we have addressed all your concerns as follows:
**Q1: the ablation study and sensitivity analysis are provided only in the appendix and only utilizes K = 4, 8, 12, 24, which should be dataset-specific and not all datase... | Rebuttal 1:
Rebuttal: Dear Reviewers, ACs and the SAC:
We thank you all for the review and valuable comments. We'll clarify them in the final version to address all relevant questions and suggestions.
To address the common concerns regarding our selection of K (Reviewer LAap, Reviewer Cudr) and our model effectivenes... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Differentially Private Reinforcement Learning with Self-Play | Accept (poster) | Summary: The paper studied two-player zero-sum episodic Markov Games under JDP and LDP. The authors designed DP-Nash-VI algorithm for the problems and derives both upper bounds and lower bounds.
Strengths: 1. The paper investigated interesting problem of two-player zero-sum episodic Markov Games under JDP and LDP.
2.... | Rebuttal 1:
Rebuttal: We appreciate your high quality review and the positive score. Below we will reply to your comments.
**There is no experimental result to verify their theoretical findings.**
Thanks for the comment, we will conduct some experiments in the next version.
**In this paper, you consider bounded rewa... | Summary: The authors address multi-agent self-play reinforcement learning (RL) with differential privacy (DP) constraints to protect sensitive data. They propose an efficient algorithm that meets JDP and LDP requirements, and its regret bounds generalize the best-known results for single-agent RL, marking the first stu... | Rebuttal 1:
Rebuttal: We appreciate your high quality review and the positive score. Below we will reply to your comments.
**The overall technical contribution seems limited. Could the authors emphasize their primary technical contributions? Specifically, is it feasible to address the problem setting using existing al... | Summary: This paper explores multi-agent reinforcement learning (RL) with differential privacy (DP) constraints. The authors extend the concepts of Joint DP (JDP) and Local DP (LDP) to two-player zero-sum episodic Markov Games. They develop a provably efficient algorithm that combines optimistic Nash value iteration wi... | Rebuttal 1:
Rebuttal: We appreciate your high quality review and the positive score. Below we will reply to your comments.
**Though this is a purely theoretical paper, it may be better to include some simulation results to validate the theoretical results.**
Thanks for the comment, we will conduct some experiments in... | Summary: This paper gives an algorithm for differentially private reinforcement learning in two-player zero-sum games. The paper considers a standard model for differential privacy already established for single-agent RL. In this model, in each episode a unique user follows a policy $\pi$ recommended by the RL agent i.... | Rebuttal 1:
Rebuttal: We appreciate your high quality review. Below we will reply to your comments.
**The algorithm proposed is a straightforward combination of prior work [1] achieving privacy for single-agent RL and [2] achieving low-regret learning for self-play in zero-sum games. It is not clear from the paper wha... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation | Accept (poster) | Summary: This paper proposed a new prompt-tuning based approach called Low-Rank Prompt Adaptation (LOPA), which performs comparably to the state-of-the-art PEFT methods without the need for a server-based adapter. LOPA balances between sharing task-specific information across instances and customization for each instan... | Rebuttal 1:
Rebuttal: - *“The comparison with the PEFT methods only considered LoRA, other representative methods such as Adapter-tuning, P-Tuningv2, etc., were not taken into account. Additionally, methods mentioned in the related work such as LPT and SPT that may compete with LoPA were not compared in the experimenta... | Summary: The paper introduces Low-Rank Prompt Adaptation (LOPA), an instance-aware prompt tuning-based approach. LOPA constructs soft prompts from a task-specific component (shared across samples) and an instance-specific component (unique to each sample), combining them using a gating function. It employs a low-rank d... | Rebuttal 1:
Rebuttal: - *"The proposed approach is quite similar ... decomposition, as in LoRA, to PHM Layers." "How different and efficient is the ...also optimize the prompt generator network."*
**Response:** Thank you for the detailed comparison. Here are the key distinctions and considerations:
1. **Encoding o... | Summary: The paper introduces Low-Rank Prompt Adaptation (LOPA), a novel parameter-efficient fine-tuning (PEFT) approach that improves soft prompt tuning, delivering performance on par with LoRA and full fine-tuning methods. LOPA addresses scalability issues of traditional PEFT methods by using a low-rank decomposition... | Rebuttal 1:
Rebuttal: - *“The authors only experimented with a rather small model, i.e., 355M RoBERTa, for classification tasks while trying much larger models (up to 8B) for code generation tasks, which raises concerns about whether the proposed method works with larger models for classification tasks.”*
**Response... | null | null | Rebuttal 1:
Rebuttal: Thank you for the insightful comments. We enclose the convergence plots of the prompt-tuning based baselines and the proposed approach on a subset of NLU tasks.
Pdf: /pdf/b943f0cd04415453ce4c022555c3e65f230cb6d9.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Are Graph Neural Networks Optimal Approximation Algorithms? | Accept (spotlight) | Summary: This paper draws analogy between the optimization of, e.g., Max-Cut and Max-SAT problems, and the message-passing algorithm, and accordingly constructs OptGNN to implement the message-passing algorithms towards the problems. The generated optimal solutions are shown with provable bounds. Finally, empirical stu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We are glad that the reviewer finds that the paper is well-presented and explained and that they value both the empirical and theoretical aspects of this work!
Next, we address the weaknesses/questions in order:
>Take the example of Max-Cut problem as an... | Summary: The papers propose graph neural architectures that can be used to capture optimal approximation algorithms for a large class of combinatorial optimization problems.
Strengths: - The paper for the most part is well written with clear motivation.
- The contributions made in the paper are manifold across various... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and for appreciating both the empirical and theoretical elements of the paper's contributions. | Summary: The paper establishes that polynomial-sized message-passing GNNs, can learn and replicate the optimal approximation capabilities of traditional algorithms based on SDP relaxations for Max-CSP under the assumption of the UGC. The authors propose OptGNN, which effectively integrates the theoretical framework of... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed feedback. We appreciate their positive comments on the soundness and the structure of the paper!
To address the weaknesses:
>The paper lacks a comprehensive analysis of how well OptGNN scales with increasing graph sizes and complexity.
The co... | Summary: This work presents a significant advancement in the field of combinatorial optimization by developing a graph neural network (GNN) architecture named OptGNN. The authors demonstrate that OptGNN can capture optimal approximation algorithms for a broad class of combinatorial optimization problems, leveraging the... | Rebuttal 1:
Rebuttal: We thank the reviewer for all their detailed comments and review of our work! To address the
concerns in order.
1. For a graph $G$ with vertices $V$ and edges $E$, for embedding dimension $d$, and depth $L$, the total runtime of OptGNN is $O(L d^\omega |V| + Ld|E|)$ where $\omega$ is the matrix m... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Goal-Conditioned Representations for Language Reward Models | Accept (poster) | Summary: The paper introduces an additional contrastive loss term for reward model training that targets the learning of goal-conditioned representations that encode expected reward for partially complete sequences. The results show that this has a positive impact on reward model accuracy, downstream RLHF with the rewa... | Rebuttal 1:
Rebuttal: We thank the reviewer for their useful comments and insightful feedback.
We respond to the reviewers comments and questions.
We plan on incorporating all these responses in the final work.
**"..the mechanism for getting the prototype.."** Please see our shared response to the reviewers.
Addition... | Summary: The paper frames the reward learning problem for LLM as a goal-conditioned RL. It uses the contrastive learning loss from Eysenbach et al. 2022 as an additional objective for reward training. The main innovation is the adjustment of goal-conditioned RL to pairwise preference datasets. The paper shows that addi... | Rebuttal 1:
Rebuttal: We appreciate the reviewers insightful and useful comments. In the following sections, we address the questions and comments raised by the reviewer. We plan on incorporating all our discussions into the final version of this paper.
**"The definition of a "goal state" in the language space..."** O... | Summary: This work combines contrastive representation learning, goal-conditioned RL, and reward models used in RLHF for language model alignment. The authors introduce a new method that uses a contrastive loss to encourage the reward model to learn what they define as "goal-conditioned representations." These represen... | Rebuttal 1:
Rebuttal: We thank the reviewer for useful comments and for appreciating the originality and performance improvements of the method.
We address the reviewers questions and comments in the following sections.
Additionally, we plan on incorporating all our responses and discussions into the final version of t... | Summary: This paper presents a method of applying goal-conditioned Q-functions to learn representations via contrastive learning to capture the expected reward. By incorporating an auxiliary contrastive loss for training the reward model, the performance of language model alignment obtains improvement. Experiments on G... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their thoughtful review and insightful suggestions.
We are pleased to know they acknowledge the novelty of the goal-conditioned approach with LLMs and found our experiments to be thorough.
The reviewer brings up the interesting point of comparing our proposed me... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable and insightful comments.
It is appreciated that the reviewers found our work to be well-written, novel, and insightful for improving RM and policy performance.
In this section, we provide further elaboration on our method of constructing the goal state.
W... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Detecting and Measuring Confounding Using Causal Mechanism Shifts | Accept (poster) | Summary: This paper introduces some measures of (conditional) confounding, based on information theoretic quantities. Some of their properties and an algorithm to estimate the measures using data from different environments. Although not included in the main text, the authors test their theory on some synthetic data.
... | Rebuttal 1:
Rebuttal: > On line 158 and 169: p is defined as the inequality between two distributions, this definition doesn’t make sense... why overloading it?
We undestand the concern. To fix this, following [35], we will edit line 158 in the revised manuscript as follows.
*"For example, the $p\text{-value}(\mathbb... | Summary: The paper addresses the challenge of identifying and quantifying (unobserved) confounding in causal inference. They propose a more comprehensive approach by relaxing the classic assumption of causal sufficiency and leveraging the sparse causal mechanism shifts assumption. The authors introduce methods to detec... | Rebuttal 1:
Rebuttal: > I found the paper to be lacking in experimental evaluations. It is not clear how hard (both statistically and computationally) it is to compute the measures of confounding that were proposed, especially the ones measuring confounding among multiple variables.
Due to space constraints, we includ... | Summary: The authors mainly introduce capturing both observed and unobserved confounding using data from multiple contexts. Leveraging experimental data proposes a comprehensive approach for detecting and measuring confounding effects from three different settings that don't need the parametric assumptions and relaxes ... | Rebuttal 1:
Rebuttal: > When the environment changes, for example, if c changes to c', will the original causal relationship change?
It depends on the type of intervention. If context change is a result of soft intervention on a variable, the underlying causal relationships do not change. If the context change is a re... | Summary: This paper presents methods that:
1. define a measure of confounding between sets of variables,
2. separate the effects of observed and unobserved confounders, and
3. assess the relative strengths of confounding between sets of variables,
in three different settings. In each setting they assume that data fr... | Rebuttal 1:
Rebuttal: > The assumption "that the causal mechanism changes are known for each variable across different contexts" ..
To measure confounding between **all pairs** of nodes in a causal graph, we need to know the mechanism changes for **each variable** across contexts. However, if the number of nodes among... | Rebuttal 1:
Rebuttal: ## Common response to all reviewers
We thank all reviewers for their thoughtful feedback. We are pleased to see the following encouraging comments from the reviewers.
1. The problem addressed in this paper is of significant importance (e4Fq).
2. The ideas presented are both original (e4Fq) and no... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Deep Equilibrium Algorithmic Reasoning | Accept (poster) | Summary: This work builds deep equilibrium graph neural networks for algorithmic reasoning. The paper tests their models on a variety of algorithm problems and finds mixed results with some positive and encouraging observations. One focus of the work is on speeding up NARs, and the paper also proposes regularizers to... | Rebuttal 1:
Rebuttal: Many thanks to the reviewer for their thoughtful review and for finding our paper’s presentation excellent. Allow us to address both the comments and questions you have raised.
*The results seem mixed and not entirely positive*
The effectiveness of DEAR may be misinterpreted due to the mixture ... | Summary: This paper proposes to solve the neural algorithmic reasoning by attacking the equilibrium solutions directly, without leveraging the recurrent structures which imitate the iterations in algorithms. They proposed deep equilibrium algorithmic reasoner (DEAR), and compare it with baselines including NAR (w/ and ... | Rebuttal 1:
Rebuttal: Many thanks to the reviewer for their thoughtful review, and allow us to address both the comments and questions you have raised.
*It is not immediately clear what is the takeaway from the experiments. My understanding is that efficiency is the key selling point of the new algorithm…*
The key se... | Summary: This paper explores a novel approach to NAR using GNNs. Traditional NAR models typically use a recurrent architecture where each iteration of the GNN corresponds to an iteration of the algorithm being learned. Instead, this paper proposes that since many algorithms reach an equilibrium state where further iter... | Rebuttal 1:
Rebuttal: Many thanks to the reviewer for their thoughtful review and directly linking the weaknesses with their questions. We address all the questions below.
*The connection between the proposed architecture (PGN with gating) and denotational semantics is not clear.*
Section 4 serves as a formal motivat... | Summary: This paper proposes Deep Equilibrium Algorithmic Reasoner (DEAR) which uses a deep equilibrium model (DEQ) to solve algorithmic tasks in CLRS30. The paper first introduces denotational semantics, which can be used to denote programs. It also provides an overview of Domain theory, and uses it to show that algor... | Rebuttal 1:
Rebuttal: Many thanks to the reviewer for their thoughtful review, and allow us to address both the comments and questions you have raised.
*DEAR hurts performance on some algorithms like Binary Search… and the reasoning is unclear.*
Firstly, we would like to emphasise that the varied algorithms found in ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thorough reviews and insightful comments. Each review has helped improve our paper by identifying areas that may have been misinterpreted and required further clarification. Here, we address the most important points which were raised by several of the reviewers.
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement | Accept (spotlight) | Summary: The paper introduces a new approach using diffusion models with cross-attention to improve the learning of disentangled representations in images. By encoding an image into concept tokens and using cross-attention to connect the encoder and the U-Net of the diffusion model, the authors show that the diffusion ... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive suggestions, and positive feedback on the paper novelty, strong disentanglement results, and writing. We have carefully considered your valuable suggestions and comments and will incorporate them into our revised manuscript. Please find our detailed respon... | Summary: This paper proposes a representation learning method which employs an encoder as part of a latent diffusion model. During training the encoder takes the target clean image and encodes it into a compressed representation vector. This vector is then used to condition the denoising UNet as it tries to denoise the... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive suggestions, and the appreciation of the method, adequate experiments, attention visualization, and paper structure. We understand your concerns and we aim to move a small step to advance this field and inspire more future works. Please find our detailed r... | Summary: The paper proposes a novel method that utilizes a concept-extracting image encoder and the cross-attention mechanism in conditional diffusion models for achieving the learning of disentangled representations. Comprehensive experiments, visualizations and ablation studies confirm the effectiveness of the propos... | Rebuttal 1:
Rebuttal: Thank you very much for your appreciation and recognition of our work regarding the novelty, writing, and strong performance. We will incorporate your helpful suggestions into our revision.
**Q1**: Typos and writing related.
**A1**: Thank you very much for your helpful suggestions. We will clar... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely appreciate the time and effort you have invested in reviewing our manuscript. Your constructive feedback has been instrumental in identifying areas for improvement, and we are grateful for your positive feedback on paper novelty (Reviewer HVuH, Mm6Q), good intuition (... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Wild-GS: Real-Time Novel View Synthesis from Unconstrained Photo Collections | Accept (poster) | Summary: Wild-GS proposes a heuristic appearance decomposition strategy to deal with arbitrary images captured in the wild. Specifically, the authors decompose the appearance of each Gaussian into three components: global appearance, local appearance, and intrinsic features. Compared to existing methods, this paper ach... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful comments. Below, we address your questions and concerns. Similar questions and weaknesses are merged.
---
**W**: " This task is boring because it has already been successfully addressed in NeRF, making it likely that it can also be applied to 3D-GS."
**A**... | Summary: The authors propose a method that adopts recently introduced Gaussian Splatting to work in an in-the-wild setting. The major contribution introduces a decoupling between the global and local changes to the splats. A part of the framework shows how to leverage a given point cloud (from the camera calibration) t... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful comments. Below, we address your questions and concerns.
---
**W**: "How does a method that learns the representations (the global descriptor and triplane representation as local descriptors) in an auto-decoder version (as in NeRF-in-the-Wild) perform?"
**... | Summary: This paper proposes a new pipeline, which is based on 3D Gaussian Splatting, for in-the-wild rendering. Wild-GS decomposes the appearance into global feature vector, local feature encoded in triplane features and per-Gaussian intrinsic features. Wild-GS achieves best performance on three scenes, while keeping ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful comments. Below, we address your questions and concerns.
---
**Q**: "Is the encoder of the UNet (pretrained ResNet-18) fixed or fine-tuned?"
**A**: It is fine-tuned during the training process, which provides better performance than the fixed version.
---... | Summary: **Summary
The paper presents a method called Wild-GS, an adaptation of 3D Gaussian Splatting (3DGS) designed for creating realistic novel views from a collection of unconstrained photographs, such as those taken in varied tourist environments. The method addresses the challenges of dynamic appearances and tran... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful comments. Below we address your questions and concerns.
---
**Q**: "How do you densify and prune the 3D Gaussians, what is the starting iteration and ending iteration and the interval iterations?"
**A**: For the densification \& pruning and iteration setti... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable comments. We are encouraged that:
- our novelty is recognized (TYNp)
- the superior performance is appreciated (ZrLe, JabQ, TYNp, E9p7)
- the writing clarity is accredited (JabQ, E9p7)
We have tried our best to respond to all the valuable concerns. Pleas... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Diffusion-Inspired Truncated Sampler for Text-Video Retrieval | Accept (poster) | Summary: The paper introduces a new method, Diffusion-Inspired Truncated Sampler (DITS), designed for text-video retrieval tasks. It addresses the primary challenge of bridging the modality gap between text and video data, a problem that existing retrieval methods often fail to solve effectively. The authors propose DI... | Rebuttal 1:
Rebuttal: We much appreciate that Reviewer `vaB9` provides valuable comments and finds the proposed method shows state-of-the-art performance. We are committed to release the training and inference code, as well as the pretrained models.
**`R5.1`**: Generalization ability. The authors highlight the impor... | Summary: This paper smartly leverage the diffusion model to solve a famous modality gap problem in the a CLIP retrieval problem. The proposed method called DITS set the text embedding as the initial point in the 1D diffusion model and try to generate the video embedding. Finally, by using the contrastive loss to align ... | Rebuttal 1:
Rebuttal: We much appreciate that Reviewer `UKTF` provides valuable comments and finds the idea is novel, showing effectiveness in filling the gap.
**`R4.1`**: The figure is too small to read.(miner issue) Figure 4, it looks like the gap is still large as the mean absolute distance is ~1.28. why it is no... | Summary: The authors introduce Diffusion-Inspired Truncated Sampler (DITS) that jointly performs progressive alignment and modality gap modeling in the joint embedding space. Experiments on five benchmark datasets suggest the state19 of-the-art performance of DITS.
Strengths: 1. The motivation is clearly described a... | Rebuttal 1:
Rebuttal: We much appreciate that Reviewer `QeV9` finds the motivation is clear and easy to follow, and the proposed method achieves the state-of-the-art performance.
**`R3.1`**: In Table 1, under the CLIP-ViT-B/32 feature extractor, the author's method has limited performance improvement compared to the ... | Summary: The paper tackles the task of text-video retrieval. It aims to address the modality gap between text and video that usually stems in state-of-the-art models. In order to do this, it leverages Diffusion models. So, it introduces DITS that jointly performs progressive alignment and modality gap modeling in the j... | Rebuttal 1:
Rebuttal: We much appreciate that Reviewer `fBRP` provides valuable comments and finds the idea is novel and interesting, with good results.
**`R2.1`**: While I think that the paper is fairly well written, some parts can be a bit confusing at the first read, though they become clear if you read twice. For... | Rebuttal 1:
Rebuttal: # Global Rebuttal
We would like to express our sincere gratitude to all reviewers for the time and effort in reviewing our manuscript. We much appreciate that reviewers find the proposed method novel and achieve good results. We are committed to release the training and inference code, as wel... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces a novel method to address the challenge of bridging the modality gap between text and video data in retrieval tasks. The authors propose the Diffusion-Inspired Truncated Sampler (DITS), leveraging diffusion models to model the text-video modality gap.
DITS performs progressive alignment an... | Rebuttal 1:
Rebuttal: We much appreciate that Reviewer `CwRd` provides valuable comments and finds the proposed method novel. Since the weakness and questions are the same, we answer the weakness below. Due to the limited rebuttal space, we will not be able to copy all questions but summarize the weakness statements be... | null | null | null | null | null | null |
MoGU: A Framework for Enhancing Safety of LLMs While Preserving Their Usability | Accept (poster) | Summary: This paper addresses the limitations of previous implementations that rely on binary classification of instructions, which often mistakenly identify benign instructions as malicious, thus reducing usability. It proposes a dynamic routing mechanism to enhance the safety of LLMs while preserving their usability.... | Rebuttal 1:
Rebuttal: ### Dear Reviewer Wh4C:
Thank you for acknowledging the **better balance between safety and usability** achieved by our proposed method and for providing **extensive** experiments. However, we've noticed **some misunderstandings**. So, we would like to clarify some aspects.
- Re-clarify the novel... | Summary: The article proposes an alignment method based on LoRA and router. By modifying the first few tokens of the model output, it achieves certain effectiveness in defending against red team attacks.
Strengths: 1. This method achieves defense capabilities against red-team attacks comparable to SOTA methods while e... | Rebuttal 1:
Rebuttal: ### Dear Reviewer ja88:
Thank you for acknowledging the **comparable performance** of our proposed solution and for providing **extensive** experiments. Below, we will address each of the weaknesses and questions you raised.
**Q1**: Lack of Novelty.
**R1**: As pointed out by Reviewer mJ14 and Re... | Summary: In this paper, the authors propose a new approach to balance safety and over-refusal in LLMs. They do this by training LoRA parameters for a compliant and a rejection/safe version of the LLM, and then train a router (as in MoEs) to combine states between these two generators. They show that this improves the... | Rebuttal 1:
Rebuttal: ### Dear Reviewer mJ14:
Thank you for acknowledging the **originality and significant improvements** of our proposed solution. Below, we will address each of the weaknesses and questions in detail.
**Q1**: The biggest weakness of the paper is in its clarity. A good example here is eq (5) which fe... | Summary: The authors propose the MoGU framework, a novel solution designed to enhance the safety of LLMs while preserving their usability. The MoGU framework operates by splitting the base LLM into two specialized variants: one focusing on usability (usable LLM) and the other on safety (safe LLM). It employs a dynamic ... | Rebuttal 1:
Rebuttal: ### Dear Reviewer 3hkc:
Thank you for acknowledging the **novelty** of our proposed solution and for providing a **comprehensive setup** of experiments along with **quantitative analysis**. Below, we will address each of the weaknesses and questions in detail.
**Q1**: Lack of a more in-depth dis... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcare | Reject | Summary: This paper studies inverse RL with learnable constraints in the offline setting, focusing on practical applications in healthcare tasks. The main approach appears to be combining the decision transformer architecture in the offline RL literature with inverse constrained RL with max entropy framework. Experimen... | Rebuttal 1:
Rebuttal: **Response 1:** We did indeed introduce our challenge by raising several issues, where the challenges are the key issue we aim to address. To aid your understanding, we will briefly re-explain it as follows: Current RL methods display risky behavior, which we aim to mitigate using Constrained RL (... | Summary: The paper uses the Inverse Constrained Reinforcement Learning (ICRL) framework to infer constraints in healthcare problems from expert demonstrations. It proposes the Constraint Transformer (CT) to address the dependence of decisions on historical data, which is generally ignored in ICRL methods with Markovian... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review of our work! Please allow us to address your concerns and answer the questions.
**Weakness 1:**
**Response:**
**(1) How sensitive is the estimated policy to the generative world model?**
To explore the sensitivity of the estimated policy to the generative wo... | Summary: This paper introduces the Constraint Transformer (CT) framework to enhance safe decision-making in healthcare. The proposed CT model uses transformers to incorporate historical patient data into constraint modelling and employs a generative world model to create exploratory data for offline RL training. The au... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review of our work! Please allow us to address your concerns and answer the questions.
**Weakness 1:** Definition of Metric
**Response:**
**(1) Reward Function Design:** The reviewer may have misunderstood our reward function design. In our work, the reward function... | Summary: The authors consider healthcare applications of RL algorithms in which implicit constraint modeling is critical for safe recommendations.
This is modeled as an RL policy optimization with constraints. However, the constraints are often unknown and need to be inferred from expert data trajectories in the healt... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review of our work! Please allow us to address your concerns and answer the questions.
**Weakness 1:** It seems like the system (which is the patient) is not feasible to model as evolving according to an Markov process on the observed state at each time, but instead ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time, suggestions and questions that we believe will improve the quality of the paper. Below we summarize our overall response to the reviewer’s questions and comments.
- We will add a discussion on the relationship between the key metrics, NEWS and MAP, and the c... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
EnOF-SNN: Training Accurate Spiking Neural Networks via Enhancing the Output Feature | Accept (poster) | Summary: The paper presents the method to improve the representation power of SNNs. To this end, the paper proposed two new techniques to the training of spiking neural networks: 1) To guide learning of the last feature layer by an alignment loss with a pre-trained non-spiking network and 2) to replace the last spiking... | Rebuttal 1:
Rebuttal: Thank you for your efforts in reviewing our paper and your recognition of our good writing, technical soundness experimental results, and novel methods. The response to your questions is given piece by piece as follows.
**W1**: How can SNN representation be converted to float activation in eq 1? ... | Summary: The authors propose a novel distillation approach (EnOF) to address the mismatch in output precision between SNN and ANN, which leads to poor distillation performance. EnOF feeds the output feature of SNN into the ANN classification head to obtain its effective mapping output P_s, and then uses distillation le... | Rebuttal 1:
Rebuttal: Thank you for your efforts in reviewing our paper and your recognition of our simple but effective method. The response to your questions is given piece by piece as follows.
---
**W1**: To illustrate the advances made by EnOF and RepAct, the authors can provide the network Hessian matrix eigenva... | Summary: This paper introduces methods to improve the output feature representation of Spiking Neural Networks (SNNs) by utilizing knowledge distillation (KD) techniques and modifying activation functions. The approach involves aligning the SNN's output features with those of a pre-trained Artificial Neural Network (AN... | Rebuttal 1:
Rebuttal: Thank you for your efforts in reviewing our paper and your recognition of our good writing and technical soundness experimental results. The response to your questions is given piece by piece as follows.
**W1**: The paper could benefit from a deeper exploration of how these specific adaptations c... | Summary: This paper explores whether the benefits of rich output feature representations, known to enhance the accuracy of ANN models for classification, also apply to SNNs. If so, the authors seek to improve the feature representation of SNNs.
The authors address this problem in two steps: first, they align the SNN o... | Rebuttal 1:
Rebuttal: Thank you for your efforts in reviewing our paper and your recognition of our effective method and good results. The response to your questions is given piece by piece as follows.
---
**W1**: The proposed method requires training the ANN counterpart first and setting it aside as a pre-trained mo... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unveiling Encoder-Free Vision-Language Models | Accept (spotlight) | Summary: This paper introduces EVE, a novel paradigm for Vision-Language Models (VLMs) designed to eliminate the need for a preceding visual encoder in the LLM decoder. This approach aims to create a more flexible and lightweight vision-language framework. EVE incorporates a Patch Embedding Layer and a Patch Aligning L... | Rebuttal 1:
Rebuttal: We are grateful for your meticulous and insightful review. We have carefully considered your questions and polished our paper.
`Q1: [Encoder-Free?] How do you justify classifying EVE as an "encoder-free" model?`
**(1) A more essential difference between PEL and image encoders is whether they inv... | Summary: This paper revisits the vision-encoder-free MLLM direction, which is not a popular choice in the community at present. A new method EVE is proposed to reduce the gap between encoder-free and encoder-based MLLMs, demonstrating a large improvement against Fuyu-8B (the best encoder-free MLLM so far). EVE introduc... | Rebuttal 1:
Rebuttal: Your constructive comments are much appreciated. We have addressed all your points and revised the paper accordingly to ensure its improvement.
`Q1: IIUC, Fuyu learns visual features from scratch, while EVE actually distillates from an existing CLIP-ViT-L-336px vision encoder, so it’s not fair to... | Summary: Authors bridge the gap between encoder-based and encoder-free models, and present a simple yet effective training recipe towards pure VLMs. They unveil the key aspects of training encoder-free VLMs efficiently via thorough experiments: (1) Bridging vision-language representation inside one unified decoder; (2)... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful remarks. We have responded to all your queries and made the necessary changes to enhance the paper.
`Q1: (((1))) EVE outperforms Fuyu-8B, but lags behind many encoder-based VLMs. why we need it? (((2))) Any explanations for the benefits of 'Any image ratio'? (((3)... | Summary: This paper explores the topic of encoder-free vision language model. It proposes to directly input the image patches into the decoder network together with the language tokens, without the use of a separate visual encoder during inference time. The benefits are mainly two-fold: simpler architecture and more fl... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We respond to all questions you raise to address your concerns and make the necessary revisions to improve the quality of the paper.
`Q1: The training is using an existing visual encoder as teacher, which kind of defeats the title of "encoder-free". Is this n... | Rebuttal 1:
Rebuttal: To all reviewers:
We thank all reviewers for your constructive comments. We are encouraged by approvals on an **interesting and novel idea** (gQSV, nP6r, Z41h), **simple yet effective** (VqDP, nP6r, Z41h), **comprehensive experiment and solid results** (gQSV, nP6r, Z41h), and **insights for the V... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unscrambling disease progression at scale: fast inference of event permutations with optimal transport | Accept (poster) | Summary: This paper proposes to use Sinkhorn algorithm to compute optimal transport in order to speed up disease progression that was previously computationally prohibited. This method enables disease progression models with higher dimensionality in features as well as 1000x faster inference speed. Authors provide expe... | Rebuttal 1:
Rebuttal: Weaknesses point 1 (“On the Pixel-level experiments…”)
Yes, it is a good idea to compare with regional-level results, and one we would have included given the time. We have now included an additional analysis in the “Author Rebuttal” PDF, where we used the FreeSurfer segmentation tool to obtain p... | Summary: The authors investigate the task of disease progression modeling, an area of research that learns underlying disease trajectory from temporal snapshots of individual patients. The authors claim that all previous approaches either sacrifice computational tractability for direct interpretability in the feature s... | Rebuttal 1:
Rebuttal: Weaknesses point 1 (“Correct me if I am wrong…”)
Yes, that is exactly what our model does. It is less of a device for individual level predictions (although can be used that way; see Figure 7, where we use it to estimate individual-level stages along the group-level sequence) and more to elucida... | Summary: This manuscript derives an Event-Based Model via variational inference and optimal transform. This approach significantly enhances computational efficiency, robustness to noise, and scalability, outperforming current methods by a factor of 1000 in wall-clock.
Strengths: 1. The experiments have been performed ... | Rebuttal 1:
Rebuttal: Weaknesses point 1 (“I am not sure about the results in Section 3.3.2…”)
Generally we expect cognitive test scores to appear later than structural changes in MRI - cognitive deficit is a consequence of loss of brain tissue. As stated in L257-260, the cognitive events occur across the latter 2/3rd... | Summary: The authors propose a method to learn a latent event sequence from cross-sectional data of modest size. Each event corresponds to a single observed feature transitioning from an initial parametric distribution (i.e., 'normal') to a second, final parametric distribution (i.e., 'abnormal'). Their inference proce... | Rebuttal 1:
Rebuttal: Weaknesses point 1 (“The related work section is very brief…”)
This section was deliberately kept brief to focus on the most relevant comparable methods; specifically those that can infer group-level sequences from cross-sectional data. While there are many models that use longitudinal data (see,... | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive comments, which we have endeavored to address as faithfully as possible in our responses below.
As part of our response, please find attached a PDF containing a new result.
We look forward to continuing the constructive discussion!
Pdf: /pdf/cdc2362c... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Zero-Shot Scene Reconstruction from Single Images with Deep Prior Assembly | Accept (poster) | Summary: This paper introduces a framework called "Deep Prior Assembly," which combines various deep priors from large models to reconstruct scenes from single images in a zero-shot manner. By breaking down the task into multiple sub-tasks and assigning an expert large model to handle each one, the method demonstrates ... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer veoH for the thoughtful feedback and time invested in evaluating our work. We respond to each question below.
**Q1:Applying DeepPriorAssembly on mobile edge devices.**
We agree that DeepPriorAssembly is now not capable of directly applying on mobile edge devices... | Summary: This work introduces a system named "Deep Prior Assembly" for zero-shot scene reconstruction from a single image. It breaks down the single-image scene reconstruction task into several steps that can be solved utilizing pretrained large models, such as SAM for segmentation, Shap-E for 3D object generation, and... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer DEJS for the acknowledgment of our work and constructive feedback. We respond to each question below.
**Q1:Technical contribution.**
We are the first to explore the cooperation among large foundation models for another extremely difficult task where none of them ... | Summary: The method uses leverages multiple off-the-shelf models to parse a scene represented by a single image into 3D assets in their respective layout. Concretely they use a segmentation model to locate objects, then a diffusion model to enhance the image quality, use Shape-E to generate 3D proposals, and estimate t... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer 67ke for the invaluable feedback and time invested in evaluating our work. We respond to each question below.
**Q1:The applications of single-view scene reconstruction.**
The task of single-view scene reconstruction greatly contributes to the domain of AIGC, ... | Summary: A multistage pipeline for single image 3D reconstruction is proposed, leveraging multiple off-the-shelf models. To begin, SAM is used to segment and decompose the input image. Stable diffusion is then leveraged to complete instance segments with potentially missing information, and failures of this process are... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer osDy for the thoughtful feedback and time invested in evaluating our work. We respond to each question below.
**Q1:The robustness of DeepPriorAssembly.**
We demonstrate that the effective integration of additional large models does not compromise the robustness ... | Rebuttal 1:
Rebuttal: We are grateful to the reviewers for their invaluable feedback and the time they dedicated to evaluating our work. We are delighted that reviewers appreciated the representation and the significance of the paper.
We respond to each reviewer separately with detailed analysis, visualizations and abl... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Tree of Attacks: Jailbreaking Black-Box LLMs Automatically | Accept (poster) | Summary: Note: I have seen this paper before, and I have heard of the method. However, I have not previously read it in full, and I do not remember anything about the authors. So this should still be a fully blind review.
This paper introduces TAP -- a black-box method to develop attacks against LLMs. The attack proc... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We are happy that you appreciate our paper’s contribution and we take your concerns seriously. We respond to them below and will include our responses in the form of expanded discussions in the final version.
**"Recent work…has shown…LLM autograders for jailb... | Summary: This paper presents an automated method, Tree of Attacks with Pruning, for generating jailbreak prompts to exploit vulnerabilities in LLMs. TAP uses a tree-of-thought reasoning approach to iteratively refine and prune candidate prompts, significantly reducing the number of queries needed to successfully jailbr... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We take it seriously and respond to your concerns below. We hope that based on our responses you will strengthen your support for the paper.
**“format of this paper needs to be refined.”** Thanks, we will refine the format.
**“The success of TAP heavily depends on th... | Summary: This paper propose Tree of Attacks with Pruning (TAP) to jailbreak blackbox LLMs automatically. TAP has four steps, including branching, pruning, attack and assess, and pruning. TAP leverage an attacker (an LLM) to generate variations of the provided prompt, and the evaluator (another LLM) decide which variati... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. We respond to your questions below and hope that you will strengthen your support for our submission.
**“a typo "branching"”** Thanks, we will correct this.
**“attacker’s system prompt is long and carefully designed…”** Yes, the attacker’s system prompt in Tab... | Summary: This paper presents a novel jailbreaking attack based on the PAIR attack, enhanced with branching and pruning techniques. The attack generates multiple prompts through branching, then applies two pruning steps: removing off-topic prompts and eliminating prompts with low scores after testing them against the vi... | Rebuttal 1:
Rebuttal: Thank you for appreciating our thorough evaluation and clear writing. We take your feedback seriously and, following your suggestion, will shorten Section 1.2 to reduce the overlap with Section 3. We answer your specific questions below and hope you continue to support the paper.
**“how calibrate... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adapting to Unknown Low-Dimensional Structures in Score-Based Diffusion Models | Accept (poster) | Summary: The paper considers the problem of convergence of probability distribution learned by diffusion models to target data distribution when the data distribution lies has some low-dimensional structure. Previous work either provides a convergence rate bound in Wasserstein distance for low-dimensional distribution ... | Rebuttal 1:
Rebuttal: Thank you for your review. We address your comments below.
**Bounded assumption on the target distribution.**
Thank you for raising this point!
- We agree that the bounded support assumption is stronger than, for example, the bounded second moment assumption, and it excludes Gaussian distribution... | Summary: In DDPM-style diffusion models, we need to discretize a continuous
process. Benton et al [3] showed that ~ d/eps^2 steps suffice over d
dimensions. But what if (as is typical) the data lie in a space of
lower intrinsic dimension (e.g. k-sparse or a k-dimensional manifold)?
This paper shows how to replace the... | Rebuttal 1:
Rebuttal: Thank you for your review. We address your comments below.
**Intuition for our coefficient design.**
Thanks for raising this point. We have extended Theorem 2 to a general lower bound that works for arbitrary low-dimensional data distributions. **Please see our response to all reviewers for this ... | Summary: This paper investigates score-based diffusion models when the underlying distribution is near low-dimensional manifolds in a higher-dimensional space. It addresses the gap in theoretical understanding of diffusion models, which are suboptimal in the presence of low-dimensional structures. For the DDPM, the err... | Rebuttal 1:
Rebuttal: Thank you for your review. We address your comments below.
**Bounded assumption on the target distribution.**
- We agree that the bounded support assumption is stronger than, for example, the bounded second moment assumption, and it excludes Gaussian distributions. However, we argue that this con... | Summary: This paper discusses the DDPM sampler's capability to adapt to unknown low-dimensional structures in the target distribution, and studies how this informs the coefficient design.
Strengths: The paper is written clearly and has a nice structure in general. It contributes to an important topic in diffusion mode... | Rebuttal 1:
Rebuttal: Thank you for your review. We address your comments below.
**Comparison with prior works.**
Thank you for the reference. These two works and the current paper approach the diffusion model from different perspectives, making direct comparison challenging. Specifically, the two prior works establis... | Rebuttal 1:
Rebuttal: We thank all reviewers for their feedback. Here we address some common comments and questions.
**Extending the lower bound to arbitrary data distribution.** We agree with several reviewer's comments that the lower bound in Theorem 2 only covers Gaussian distribution, which can be restrictive. Her... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dual Encoder GAN Inversion for High-Fidelity 3D Head Reconstruction from Single Images | Accept (poster) | Summary: The paper proposes an encoder-based approach to GAN-inversion for full 3D head reconstruction from a single image. To address the challenge of reconstructing the entire 360° head, the method employs two encoders: one for high-fidelity reconstruction of the input image (typically near-frontal) and another for g... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their feedback and for highlighting several strengths of our paper.
1. (**W.1**) For the head geometry after editing, we realized that the example we share in the paper does not do justice; the reference images have more flat-looking hairs. We added more ex... | Summary: This paper introduces an encoder-based method to do the GAN inversion task, especially for the full head inversion. The occlusion aware discriminator is interesting and reasonable. The results are good.
Strengths: 1. The idea is interesting, I like the occlusion-aware discriminator. Meanwhile, training the di... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their feedback and finding our paper interesting and technically solid.
In the training process, E1 is trained with visible parts by using input-output paired losses of LPIPS, L2, and identity losses. On the other hand, E2 is trained to learn the occluded ... | Summary: This paper addresses the challenge of 3D GAN inversion from a single image, focusing on a method called PanoHead designed for generating 360 views of human heads. Unlike optimization-based approaches, this work adopts an encoder-based technique to reduce inversion times. The authors observe that varying encod... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their feedback and highlighting our paper’s several strengths.
1. About the claim on lines 168-169: We acknowledge that not receiving any gradients for the visible region may be theoretically incorrect and will make the necessary modifications to avoid con... | Summary: This paper introduces a novel encoder-based method for 3D pano-head GAN inversion. Unlike previous 3D GANs focused on near-frontal views, this work designs a dual-encoder system tailored for both high-fidelity reconstruction and realistic generation from multiple viewpoints. The method incorporates a stitching... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their feedback and suggestions, which further helped us improve the paper.
1. (**W.1**) The reviewer wanted more visual results from extreme viewpoints. We did not notice the visual results from the paper were all more from the frontal view. We apologize ... | Rebuttal 1:
Rebuttal: We want to thank all reviewers for their valuable feedback. We have responded to each reviewer's questions in the rebuttal sections, and attached a PDF file with figures and tables for our additional results.
Pdf: /pdf/f85b902ed1656e416794f9d3cf0de33fde7af889.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents a framework for 3D GAN inversion aimed at reconstructing high-fidelity 3D head models from single 2D images. A dual encoder system that combines one encoder specialized in high-fidelity reconstruction of the input view with another focused on generating realistic representations of invisibl... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their feedback, and we will improve the writing further based on the reviewer’s suggestion. The reviewer mentions that the innovation of the paper may be a little lacking, and there is a lack of in-depth research on the proposed method. However, it is cruci... | null | null | null | null | null | null |
Optimal Hypothesis Selection in (Almost) Linear Time | Accept (poster) | Summary: This paper studies the hypothesis selection problem: Given $n$ distributions $H_1, H_2, \ldots, H_n$ and samples from an unknown distribution $P$, the goal is to output $\hat H$ such that $\mathrm{TV}(P, \hat H) \le \alpha \cdot \mathsf{OPT} + \epsilon$, where $\mathsf{OPT} = \min_{i \in [n]}\mathrm{TV}(P, H_i... | Rebuttal 1:
Rebuttal: Thank you for your feedback.
In response to your question, there is no explicit lower bound for the time complexity of this problem. As you mentioned, it is possible that an algorithm could exist with a time complexity of just $\Theta(n + s)$. However, for algorithms that operate by querying th... | Summary: This paper studies the hypothesis selection problem, where given a set of candidate distributions $\mathcal{H}=\{H_1,\dots,H_n\}$ and samples from a distribution $P$, the learner wants to approximately select a distribution $H_i \in \mathcal{H}$ such that $||H_i-P||_{TV} \le \alpha opt+\epsilon$ for some const... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We will include the motivations and applications of hypothesis selection, as well as an overview of previous algorithms, in our paper. In this rebuttal, some applications of hypothesis selection are discussed in our response to Reviewer fMmw, for your reference.
Regar... | Summary: This paper introduces proper approximation algorithms for optimal hypothesis selection in the context of distribution learning.
The first algorithm achieves an optimal approximation factor of $\alpha=3$ in time approximately linear in the number of hypotheses. The second achieves the slightly looser $\alpha = ... | Rebuttal 1:
Rebuttal: **Presentation:**
Thank you for your feedback regarding the presentation of our paper. Our overview was intended to provide a high-level description of our algorithm to avoid obscuring the main technical ideas with detailed specifics. We will certainly focus on enhancing the clarity and quality of... | Summary: This paper looks at proper distribution learning: given samples from
some distribution p, and a set of n candidate distributions H_i,
output one H_i that is close in TV; in particular, alpha * OPT + eps.
Surprisingly, this is possible with a sample complexity independent of
the domain size (as would be needed ... | Rebuttal 1:
Rebuttal: **Presentation**
Thank you for your feedback regarding the presentation of our paper. Our overview was intended to provide a high-level description of our algorithm to avoid obscuring the main technical ideas with detailed specifics. We will certainly focus on enhancing the clarity and quality of... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for your feedback.
**Presentation:**
Thank you for your comments regarding the presentation of our paper. We will incorporate all your editorial suggestions regarding the presentation of the paper. We will certainly focus on enhancing the cla... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure | Accept (poster) | Summary: This paper compares diffusion models trained on natural image datasets with their Gaussian approximations. It evaluates the quality of this approximation in both the memorization and generalization regime by studying the influence of the training set size, model capacity, and training time.
Strengths: This pa... | Rebuttal 1:
Rebuttal: **Q1. First, most of section 3 is rather obvious for people with a signal/image processing background; e.g., Theorem 1 is well known.**
We sincerely appreciate the review for pointing us to the Wiener filter. We agree that Theorem 1 is well studied (we will add citations on the Wiener filter to a... | Summary: The work analyzes the behavior of a diffusion-based generative model from the perspective of ``Gaussian structures''. In particular, it checks the linearity of scores at single-time steps and compares scores against a Gaussian model. The analysis shows that the Gaussian structure plays a main role in image ge... | Rebuttal 1:
Rebuttal: **Q1. The study is only limited to a few generative models and a face dataset. The observation might be different on different datasets. The face dataset has a more obvious Gaussian distribution. For example, the mean of all faces is a face, and correlations between pixels are relatively consisten... | Summary: The paper investigates the generalization properties of diffusion models by examining the learned score functions, which are denoisers trained on various noise levels. It shows that nonlinear diffusion denoisers exhibit linearity when the model can generalize, leading to the idea of distilling these nonlinear ... | Rebuttal 1:
Rebuttal: **Q1. Although the paper's focus is on generalization in diffusion models, it lacks any quantitative measure of generalization within the experiments presented.**
A1: The memorization and generalization can be clearly observed in our experimental results by comparing the generated image with the ... | null | null | Rebuttal 1:
Rebuttal: To all Reviewers:
We thank all the reviewers for their insightful and constructive comments. Most of the reviewers find our work well written (6yNJ, VCtP, N1bC), well-motivated (6yNJ, N1bC), interesting and novel (6yNJ, N1bC, VCtP) with convincing evidence (VCtP).
We summarize our main findings... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CoBo: Collaborative Learning via Bilevel Optimization | Accept (poster) | Summary: In this paper, the authors introduce a novel approach to collaborative learning by framing it as a bilevel optimization problem, enhancing the training efficacy of multiple clients. In conventional collaborative learning paradigms, clients engage in mutual model training through information exchange; however, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful questions and comments.
**Regarding Question 1**
We appreciate the reviewer's interest in the step size selection for the projected gradient descent in CoBo. We choose $O(1/LT)$ as the step size, one intuitive explanation is motivated by the similarity ... | Summary: In this paper, the authors model collaborative learning as a bilevel optimization problem, and propose CoBo, an SGD-type alternating optimization algorithm, to solve this problem. Theoretical convergence guarantees are provided, and experiments are conducted to evaluate the performance of CoBo.
Strengths: In ... | Rebuttal 1:
Rebuttal: We would like to thank you for your thoughtful reviews and suggestions on our paper. We appreciate your feedback and are happy to address the concerns raised.
Regarding Weaknesses:
**Theorem I proof:** We agree with the reviewer that using full gradients for the inner problem simplifies the proo... | Summary: The paper proposes bi-level training for heterogeneous federated learning, where heterogeneity is due to underlying clustered clients. The two levels of optimizations are model training and determining the client similarity. The authors prove a convergence result and prove empirical results on training vision ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback on our paper. We address each of the comments below:
1. Sparsity in experiments and ablations:
We have added more experiments to investigate the effect of different settings, such as the number of clusters, data samples per client, and number of ... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful reviews and feedback. **The attached PDF**, contains additional experiments allowing us to gladly address the following concerns:
**1. FedAvg+fine-tuning:** A new baseline is included for all experiments. This baseline is similar to FedAvg for the first... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Inexact Augmented Lagrangian Methods for Conic Optimization: Quadratic Growth and Linear Convergence | Accept (poster) | Summary: This paper presents new theoretical results on the convergence of primal iterates in inexact augmented Lagrangian methods (ALMs) for conic optimization. The idea is to use strict complementarity (which is a standard assumption in conic optimization) to establish quadratic growth and error bound conditions for ... | Rebuttal 1:
Rebuttal: The authors are very thankful to the reviewer for taking the time and effort to review our manuscript. We sincerely appreciate all your valuable comments. All your comments are carefully addressed below.
> **Suggestion focusing on a specific cone, for example, LP. This will make the paper easier ... | Summary: The paper presents the convergence rate of the primal iterates of the augmented Lagrangian Methods (ALMs) which are widely employed in solving constrained optimizations. The authors develop new quadratic growth and error bound properties for primal and dual conic programs under the standard strict complementar... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and effort in evaluating our manuscript. Your comments helped further improve the quality of our work. We provide detailed responses below.
> **The authors claim in Remark 1 that the results in Theorems 1 and 2 are more general and unified compared with known res... | Summary: This paper develops Inexact Augmented Lagrangian methods (ALM) for conic optimization problems with focus on Linear Programming (LP) or the non-negative orthant cone, Second-Order Cone (SOCP) problems and Semidefinite programming (SDP) given a linear objective. Under assumptions of strict complementarity and s... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and effort in evaluating our manuscript. Your comments helped further improve the quality of our work. We provide detailed responses below
> **Lack of characterization of how inexact evaluation of the proximal operator affects convergence.**
Thanks for the valua... | Summary: The paper addresses the convergence of primal and dual iterates in Augmented Lagrangian Methods (ALMs) for conic optimization, particularly under quadratic growth assumptions. The authors establish that both primal and dual iterates of ALMs demonstrate linear convergence solely based on the strict complementar... | Rebuttal 1:
Rebuttal: The authors are very thankful to the reviewer for taking the time and effort to review our manuscript. We sincerely appreciate all your valuable comments. All your comments are carefully addressed below.
We have provided [some general responses](https://openreview.net/forum?id=Sj8G020ADl¬eId=W... | Rebuttal 1:
Rebuttal: The authors would like to thank the four reviewers for their valuable time reading our manuscript and providing helpful comments. While the scores from the reviewers are mixed (3, 5, 8, 3), we find that their comments are generally positive and some are very constructive. In particular, all four r... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Provably Safe Neural Network Controllers via Differential Dynamic Logic | Accept (poster) | Summary: This work addresses the challenge of verifying the safety of NNCSs for CPS, especially for infinite time horizons. To tackle this problem, the author(s) introduce VerSAILLE, a novel approach that leverages differential dynamic logic to derive specifications for NNs, which are then proven using NN verification ... | Rebuttal 1:
Rebuttal: We are glad to see you found our experimental setup to be thorough and consider the paper easy to follow. We are also thankful for your feedback and address your questions and comments below:
## (A) Other architectures & constraints (Weaknesses)
Like many prior works [40,52,54] we focus our impl... | Summary: This paper introduces VerSAILLE, a new method using dL contracts to ensure the safety of Neural Network Controlled Systems with piece-wise Noetherian Neural Networks. Mosaic, implemented in N^{3}V for ReLU NNs, efficiently verifies properties across various case studies, including complex applications like air... | Rebuttal 1:
Rebuttal: Thank you for providing us with the valuable feedback -- we are happy to see you find our approach promising. We address your questions and comments below and are particularly thankful for your feedback on readability:
## (A) Introduction & Related Work (Weaknesses)
We address the readability con... | Summary: This paper tackles the challenge of formal verification of neural-network based control systems. While scalability of the existing methods can still be improved, the authors provide an alternative approach via reusing safety proofs from control theory, open-loop neural-network verification, and differential dy... | Rebuttal 1:
Rebuttal: Thank you for providing us with the valuable feedback -- we are happy to see that you like our proposed approach and found the contents to be well organized.
We address your questions and comments below:
## (A) Evaluation (Weaknesses)
Thank you for the suggestion, we made a proposal for a suitabl... | Summary: Authors introduce VerSAILLE (Verifiably Safe AI via Logically Linked Envelopes), a method of verifying neural network based control systems using a provably safe control envelope in dL. VerSAILLE relied on nondeterministic mirrors, which allows authors to reflect a given neural network and reason within and ou... | Rebuttal 1:
Rebuttal: Thank you for providing us with valuable feedback on readability concerns with the paper's draft. We were pleased to hear, that you nonetheless found our approach novel and comprehensive.
## (A) Strong Definitions
> Readability would be improved from strong definitions and standardized notation.
... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comprehensive reviews and their detailed feedback on the paper. We were excited to hear that the reviewers found our "comprehensive approach" (rNvs) to be "novel" (rNvs,H7zS), "promising" (JF4H) and "well-written" (CSRh). Furthermore we are glad you agree that VerS... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Single Image Reflection Separation via Dual-Stream Interactive Transformers | Accept (poster) | Summary: This work introduces ADI, a new interactive dual-stream transformer framework for single image reflection separation. It incorporates a dual-attention interaction to explicitly model the dual-stream correlation for improved reflection separation, achieving impressive performance compared to other SOTA methods.... | Rebuttal 1:
Rebuttal: Thank you for recognizing the motivation, technical soundness, and state-of-the-art performance of our method. Below, we address the key concerns you raised:
**Q1**: Why the design of inter-patch/intra-patch attention is quite plain?
**A1**: Keeping the design simple is beneficial to validate it... | Summary: The paper proposes a transformer-based network for the single image reflection separation task. It focuses on the network architecture design, proposing several tailored designs, including dual-attention interactive block (DAIB), dual-stream self-attention (DS-SA), and dual-stream cross-attention (DS-CA).
Str... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. We appreciate your comments and try our best to address the issues as follows:
**Q1**: The writing of the method section.
**A1**: We apologize for any difficulties caused by the writing in the method section. In the next version, we will thoroughly proofread... | Summary: The authors propose the Dual-Stream Interactive Transformer (DSIT) for single image reflection separation. DSIT is a dual-stream method designed for complex scenarios. Specifically, the authors design the Dual-Attention Interaction (DAI) to achieve feature aggregation of different dimensions through dual-strea... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback on our work. Below, we address your main concerns:
Q1: (1) Clarify the implementation of CAI, (2) N_T, N_W, and (3) DSLP.
A1: (1) CAI is a general design introduced to enable interaction and fusion of features extracted from different network architectures du... | Summary: This paper introduces a Dual-Stream interactive transformer to tackle the single image reflection removal task. The motivation of the proposed method is based on the drawbacks of existing methods that the dual-stream methods cannot assess the effectiveness of the information flowing between the two streams. Ba... | Rebuttal 1:
Rebuttal: First of all, we sincerely appreciate your comments, which can help us further improve the quality of our paper. We also apologize for any confusion or inconvenience caused by writing issues. Below, we address the main concerns.
**Q1**: What does the superscript symbol $k$ mean? It is not consis... | Rebuttal 1:
Rebuttal: **Common Issue**:
**Q**: Illustration of Explicit Correlation Assessment and the Cross-Attention mechanisms in the proposed method.
**A**: We utilize the Layer-aware Cross-Attention (LaCA) mechanism for explicit correlation assessment between the two streams. A formal derivation of LaCA has alr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling | Accept (spotlight) | Summary: The paper is concerned with sampling trajectories with a terminal condition. For stochastic processes governed by a Brownian Motion, Doob's h-transform gives an posterior SDE that leads to samples with the final condition. However, estimating the h-function that is needed for the posterior SDE usually involves... | Rebuttal 1:
Rebuttal: > Q1. I found the path histograms in Figure 2 to be too cluttered. I would propose adding less samples.
Thank you for your feedback and the concrete suggestion. Our goal was to illustrate the diversity of the ensemble of transition paths. We will revise Figure 2 by reducing the number of paths an... | Summary: This paper proposes a variational formulation of Doob's h-transform, which characterizes the distribution over paths with a given endpoint. Instead of relying on potentially wasteful sampling approaches, the authors propose directly optimizing a tractable variational distribution over transition paths which sa... | Rebuttal 1:
Rebuttal: > Q1. To better understand how the performance of the variational approach improves during training, it would be nice to see a plot that shows the Max Energy as a function of the training epochs.
Please see the supplementary PDF for the plot of max energy as a function of energy evaluations. We w... | Summary: The Authors of this paper tackle the problem of sampling conditioned SDEs with a specific interest in "transition path sampling", i.e. sampling a Langevin-type SDE undergoing a transition between an initial state (or set of states) $A$ and a final target set $B$. Sampling transition paths efficiently can provi... | Rebuttal 1:
Rebuttal: > Q1. I find the paper overall clearly written, but I had to go through section 3.2 (Computational Approach) several times to grasp how the method can be deployed in practice. Specifically, I think that it can be improved the explanation of the fact that, because of the Gaussian prior, you only ne... | Summary: The submitted manuscript presents a variational formulation of Doob’s h-transform, leading to a novel (simulation-free) computational approach for rare event sampling in transition paths. The task of interest involves conditioning a dynamical system driven by Brownian motion with a known drift term to reach a ... | Rebuttal 1:
Rebuttal: >Q1: The connection to Doob’s h-transform established in Theorem 1 is lost due to the Gaussian parametrization of q_t. Lack of discussion regarding the implications of this parametrization on the accuracy and validity of the method.
We highlight the potential lack of expressivity for the Gaussian... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their diligent review and valuable comments, which helped us to improve the manuscript. In this general response, we would like to address shared concerns raised by more than one reviewer.
To make the implementation and training procedure for our metho... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a variational formulation of the Doob's h-transform to change the problem from the expensive simulations of trajectories to an optimization problem over possible trajectories from given initial point and end point. The model parameterization introduced imposes the desired boundary condition... | Rebuttal 1:
Rebuttal: > Q1: I would have liked to see a bit more discussion on the dimensionality, what are the typical values of D ..
Thank you for your valuable feedback. We will discuss this in the next revision of the manuscript. The dimension $D$ depends on the specific system being modeled. For instance, alanine... | null | null | null | null | null | null |
Time-Varying LoRA: Towards Effective Cross-Domain Fine-Tuning of Diffusion Models | Accept (poster) | Summary: This paper introduces a low-rank adapter, Terra, for effective cross-domain modelling through the construction of a continuous parameter manifold. This approach facilitates knowledge sharing across different domains by training only a single low-rank adaptor. The expressiveness of the model was analyzed theore... | Rebuttal 1:
Rebuttal: #### [W.1] Qualitative Evaluation
> We appreciate the reviewer's suggestion. we have included additional qualitative samples in Fig. r2 of the Rebuttal-PDF. These samples further demonstrate Terra's ability to handle morphing under various scenarios.
#### [W.2] Comparative Analysis
> We thank th... | Summary: The paper presents a variant of LoRA, with additional time variable conditioned low-rank square matrix, for fine-tuning a diffusion model for unsupervised domain adaptation and domain generalization. Besides, the paper also study how to better apply the proposed Terra on UDA and DG tasks. Compared to prior art... | Rebuttal 1:
Rebuttal: #### [W.1] I feel the term "time" and "t" contradicts to the widely used time step t in diffusion model. Although I do see the authors used another symbol for diffusion models' timestep, it would be much better to use another term for the proposed "time" term to avoid confusion.
> We appreciate th... | Summary: This article introduces Terra, a simple time-varying low-rank adapter based on LoRA for domain flow generation. Terra efficiently bridges the source and target domains using a parameter-efficient method. By generating data with smaller domain shifts, Terra effectively improves performance in incorporation, UDA... | Rebuttal 1:
Rebuttal: #### [W.1] The method essentially adopts the LoRA approach and constructs a low-rank parameter manifold through F(W,t)=tW+I. This can be seen as an interpolation version of LoRA with fewer parameters.
> Terra constructs a "LoRA flow" in the parameter space, and is NOT an interpolation version of ... | Summary: This paper proposes Terra, a time-varying low-rank adapter based on the Low-Rank Adapter (LoRA) method, designed to enable continuous domain shifts from one domain to another. The core idea is to incorporate a time-dependent function between the LoRA low-rank matrices, using time t to control the interpolation... | Rebuttal 1:
Rebuttal: #### [W.1] The presentation can be improved. I suggest elaborate and provide more details on sec. 3.3 and to move Fig. 7 in appendix to the main paper. The implementation details of morphing between style and subject should be explained here.
>We will follow your suggestion by (a) relocating Fig.... | Rebuttal 1:
Rebuttal: Dear Reviewers and ACs,
We sincerely thank all the reviewers and ACs for your diligent efforts and high-quality reviews. If you have any additional question or require further clarification, please feel free to let us know. Your insights are highly valued.
We are delighted to note that reviewers... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Removing Length Bias in RLHF is not Enough | Reject | Summary: The authors considers methods for removing bias in RMs, specifically the bias towards long responses and the bias certain prompts might have to generate high rewards (this stems from the BardleyTerry model being underspecified.). For the second problem the authors proposed PBC which adds a linear layer to the ... | Rebuttal 1:
Rebuttal: Thank you for recognizing the impact of our research direction.
### W1
As your suggestion, we have compared our method with other baselines on MT-bench.
The results have been exhibited in the following.
| MT-Bench | Turn 1 | Turn 2 | Average Score |
|----------------------... | Summary: This paper studies the prompt bias in RLHF, especially the reward modeling --- beyond the length bias that might exist.
Alleviating reward hacking is an important topic in RLHF, however, with the current paper, some details or contributions are not very clear. I'll elaborate in the following sections.
Strengt... | Rebuttal 1:
Rebuttal: Thank you for recognizing the impact of our research direction.
### **W1**
In tems of notation, we assume it is correct to perform the averaging operation on the variable $y$ rather than the function symbol $C$.
### **W2**
Thanks for your suggestion. We admit that Fig.1 is used to illustrat... | Summary: This paper introduces the Prompt Bias Calibration (PBC) method to address prompt-template bias in reward training of RLHF. The proposed PBC method is validated through extensive empirical results and mathematical analysis, showing its effectiveness in combination with existing length bias removal methods.
Str... | Rebuttal 1:
Rebuttal: First of all, we greatly appreciate your responsible review of our theoretical analysis on the issue of “prompt-template bias” and also thanks for acknowledging the performance of our method.
We assume that our greatest disagreement lies in the theoretical analysis part, so we will try to address ... | Summary: The paper addresses the issue of reward hacking in RLHF training, superficially, identifying prompt-template bias, defined as when a reward model (RM) develops a preference for responses that adhere to specific formats or templates, even when these formats are not explicitly specified or desired in the prompt ... | Rebuttal 1:
Rebuttal: Thank you for recognizing our work. We believe that you are a reviewer with genuine experience in implementing RLHF and are well aware of the current shortcomings of RLHF.
### **W1**
Thanks for your suggestion. The orginal title aims to emphasize that existing RLHF research mainly focuses only on... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers' efforts and valuable feedback in helping to enhance the quality of our paper.
Here, we would like to highlight the motivation and key contributions of our work in the following:
### **Motivation**
The motivation for our work stems from the process of deployi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need | Accept (poster) | Summary: The paper introduces UMT (Unlearnable Multi-Transformations), the first approach designed to render 3D point cloud data unlearnable for unauthorized deep learning models, by applying class-wise transformations. It also presents a data restoration scheme to enable authorized users to effectively train on the un... | Rebuttal 1:
Rebuttal: > **Q1:** If only part of the training set is UMT data, can the performance still be maintained?
**Re:** We are thankful for your feedback on this matter. To provide a response to this concern, we performed experiments to evaluate the impact of two UMT schemes on final test accuracy using five di... | Summary: This paper studies the protection scheme against unauthorized learning on 3D point cloud data. A simple class-wise transformation method is designed to mislead the model to learn the transformation patterns of points instead of categorical knowledge. The method is evaluated on popular used 3D datasets and mode... | Rebuttal 1:
Rebuttal: **Response to Reviewer Yqki [1/3]**
Due to space limitation, we have divided our response into three parts. Thank you for your understanding.
> **Q1:** Scene understanding for autonomous driving need to be considered.
**Re:** We appreciate your thoughtful and constructive feedback and strongl... | Summary: After reading the author’s response, I would like to increase my evaluation to borderline accept.
—
This paper addresses a critical issue by extending unlearnable strategies to 3D point cloud data, introducing the Unlearnable Multi-Transformations (UMT) approach. The use of a category-adaptive allocation str... | Rebuttal 1:
Rebuttal: Due to space limitation, we have divided our response into three parts (including one rebuttal part and two official comments). Thank you for your understanding.
#### **Response to Reviewer BhZK [1/3]**
> **Q1:** The title is exaggerated, less informative, and not related to the research topic... | Summary: The paper introduces a novel approach called Unlearnable Multi-Transformations (UMT) to make 3D point cloud data unlearnable by unauthorized users. This method employs a category-adaptive allocation strategy to apply class-wise transformations, thereby preventing unauthorized training on the data. Additionally... | Rebuttal 1:
Rebuttal: > **Q1:** Are object categories sensitive to the combination of transformations?
**Re:** Thank you for your thoughtful and valuable comment. In the original paper's Table 7, we have examined the effect of various combinations of transformations on the final performance. It can be seen that emplo... | Rebuttal 1:
Rebuttal: ## **Global Response**
We express our heartfelt thanks to all the reviewers for their valuable time and are encouraged that they found the paper to be:
1. **Clearly written, making complex concepts easy to understand** *(nf2f)*.
2. The studied problem is **interesting** *(BhZK)*, **important** *... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FUGAL: Feature-fortified Unrestricted Graph Alignment | Accept (poster) | Summary: In this paper, the authors propose an algorithm for graph matching. The method is based on the classical relaxation to the set of doubly stochastic matrices, where the authors add two variants to the optimization: (1) an additional term to match node features (computed from the graph as degree, clustering coef... | Rebuttal 1:
Rebuttal: **W1(a). Lack of references: the authors say that FAQ is the only algorithm addressing the graph matching (they say the QAP, actually) problem directly through the adjacency matrices. This is profoundly inaccurate. [1]-[6]**
**Answer:** We apologize for the lack of precision in our original stat... | Summary: The paper proposes FUGAL, a method for graph alignment by using additional features of nodes to guide optimization of a relaxed problem. It combines strengths of 2 lines of methods, using full graph information and structural features enrichment.
---
score raised after rebuttal.
Strengths: I think the paper... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments on our work. Please find below some clarifications on the queries posed.
**W1. Clarification on novelty**
*Answer.* The novelty of our work lies in:
1. crafting a regularizer using network features that makes the QAP potent for network alignment. ... | Summary: The paper presents FUGAL, a method for aligning graphs by finding a permutation matrix. FUGAL is an unrestricted method as it (also) operates on adjacency matrices, unlike most methods that rely only on intermediary graph representations.
FUGAL combines a Quadratic Assignment Problem (QAP) with a Linear Assign... | Rebuttal 1:
Rebuttal: **Q1. Probably, a paragraph summarizing those cases in which the method works well and when it might perform poorly is missing. Maybe I missed something, but sections 5 and 6 should include the limitations as per your “NeurIPS Paper Checklist,” though they might not be well emphasized.**
*Answer:... | Summary: The current work tackles the problem of graph alignment, where the objective is to find an optimal alignment between two graphs. The current work attempts an unrestricted approach by solving a QAP and augmenting it with a LAP regularizer for tractability. This is in contrast to past work where the matching hap... | Rebuttal 1:
Rebuttal: **W1. I think the QAP problem has been well studied long before since 1990. See for example: https://www.math.cmu.edu/users/af1p/Texfiles/QAP.pdf.
https://link.springer.com/chapter/10.1007/978-1-4757-3155-2_6 https://link.springer.com/article/10.1007/s12652-018-0917-x The authors need to perform c... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their insightful and constructive feedback. Below, we provide a comprehensive point-by-point response to their comments. Additionally, we attach a PDF document containing plots of several new empirical analyses as suggested by the reviewers. The key revisions a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Information Re-Organization Improves Reasoning in Large Language Models | Accept (poster) | Summary: The paper proposes a simple yet effective method to work with most of the current reasoning strategies. It automatically organizes the content into structural form, excluding noises and unused information during this process. The experiments are done using three models across ten datasets. The method consisten... | Rebuttal 1:
Rebuttal: Many thanks to the reviewer for giving these valuable feedback and comments.
**W1**: We supplement the results across all datasets using GPT-35-Turbo-0613, including the main experimental results and ablation results. The results are shown in Tables 1, 2, and 3 in the PDF file under the Author Re... | Summary: This paper proposes a method called information re-organization (InfoRE) to enhance the performance of large language models on some reasoning tasks. Unlike existing approaches that primarily focus on refining the reasoning process, InfoRE emphasizes re-organizing contextual information to identify logical rel... | Rebuttal 1:
Rebuttal: Thanks for reviewing our work and providing these valuable comments.
**W1**: Our paper introduces a strategy specifically designed to enhance multi-hop reasoning capabilities by effectively organizing contextual information before reasoning processes begin. This approach addresses a noticeable ga... | Summary: The paper introduces an "Information Re-organization" (InfoRE) method aimed at improving the reasoning abilities of large language models (LLMs). It highlights the deficiencies of existing approaches that focus on intermediate reasoning steps without adequately addressing the preliminary organization of contex... | Rebuttal 1:
Rebuttal: Many thanks for reviewing our work and providing these valuable feedback and comments.
**W1**: In our method, information extraction is implemented using closed-source or publicly available LLMs with frozen parameters, significantly reducing computational expense.
The primary resource-intensive c... | null | null | Rebuttal 1:
Rebuttal: Thank you very much to the reviewers for reviewing our work and providing valuable feedback and suggestions.
In the PDF file, we supplement the results of our method on the GPT-35-Turbo-0613 version.
Pdf: /pdf/97994460dedb042bc5ed8e0ac11a202e3d72ee73.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DisC-GS: Discontinuity-aware Gaussian Splatting | Accept (poster) | Summary: This paper proposes a novel kernel function to model static scenes, addressing the difficulty of modeling high-frequency boundaries caused by the $r^2$ decay from center to edge of the Gaussian kernel. To tackle the smoothing decay of the Gaussian kernel, the authors first divide the Gaussian in screen space u... | Rebuttal 1:
Rebuttal: >*Q1: Qualitative comparisons and quantitative metrics.*
**A1:** **(1) Qualitative comparisons.** In the PDF uploaded during rebuttal (at the bottom of the "Author Rebuttal by Authors" comment), besides in the 3D scenes in Figs 3 and 4 in paper, we also provide qualitative comparisons in more 3D ... | Summary: This paper introduces DisC-GS, a method that utilizes Bezier curves for discontinuity-aware image rendering on 3DGS. By employing Bezier curves, this approach significantly enhances the rendering results of scene boundaries. A set of experiments and ablation studies substantiate the effectiveness of this propo... | Rebuttal 1:
Rebuttal: >*Q1: 2D parameters to control the position of the control points.*
**A1:** As mentioned in lines 121-125 in the paper, in existing works, both 2D and 3D Gaussian Splattings have been utilized to represent the 3D scene. In this work, we proposed a framework that can be applied to both 2D and 3D G... | Summary: This paper proposes DisC-GS, a technique that enhances the boundary rendering quality for Gaussian splatting. DisC-GS takes into account the discontinuity of shapes and uses Bézier curves to model the boundaries. To enable differentiable rendering, the authors propose a novel discontinuity-aware rendering pipe... | Rebuttal 1:
Rebuttal: >*Q1: Evaluation of the storage.*
**A1:** Below, we show the storage of our framework on the Tanks&Temples dataset on an RTX 3090 GPU. As shown, either applied on 2D or 3D Gaussian Splattings, our framework **does not increase the storage size**.
This can be because, admittedly, for each Gaussi... | Summary: The authors proposed an innovative framework DisC-GS, which enables Gaussian Splatting to represent and render boundaries and discontinuities in an image, they also introduce several designs to make the pipeline is discontinuity-aware and differentiability, and their method achieves superior performance on the... | Rebuttal 1:
Rebuttal: >*Q1: Defines numerous labels. [...] It would be better to include a list of labels and their explanations. [...] label explanations under each equation.*
**A1:** Thanks for your suggestion. Following it, (1) below, we formulate a list of labels (symbols) and their explanations. (2) Meanwhile, un... | Rebuttal 1:
Rebuttal: We thank all reviewers for recognition of our contributions (Reviewer TofM: "an innovative framework", "a robust and sound pipeline"; Reviewer F99C: "a novel discontinuity-aware rendering pipeline", "addresses a very important problem", "the idea of using Bézier curves to define the shape is novel... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning | Accept (poster) | Summary: The paper introduces the GITA framework, which innovatively integrates visual graphs into general graph reasoning tasks for LLMs. By combining visual and textual information of a graph, GITA improves the comprehensibility and flexibility of graph reasoning, outperforming other LLM approaches. The authors also ... | Rebuttal 1:
Rebuttal: Thanks for your thorough comments and insightful suggestions, we here address your concerns and adopt your suggestions as follows:
> **W1**: It is not clear that LLM-based methods like GITA are better than GNNs in terms of generalizability, flexibility, and user-friendliness, and the motivation ... | Summary: To fill the gap that LLM overlook the rich vision modality with graph structure, this paper proposes GITA to incorporates visual graphs into general graph reasoning. A large graph vision-language dataset called GVLQA is designed to boost the general graph reasoning capabilities.
Strengths: 1. This paper may i... | Rebuttal 1:
Rebuttal: We acknowledge and appreciate your insightful review. Below, you can find our responses addressing your concerns point by point. If you have any additional questions or require further clarification, please feel free to let us know.
> **W1**: This paper appears to be quite technical or engineerin... | Summary: The paper introduces an end-to-end framework called Graph to Visual and Textual Integration (GITA) to visualize graphs in order to improve LLMs’ reasoning capabilities on graph tasks. GITA consists of three main components: a Graph Describer to translate a graph into a natural language description, a Graph Vis... | Rebuttal 1:
Rebuttal: We acknowledge and appreciate your insightful review. Below, you can find our responses addressing your concerns point by point. If you have any additional question or require further clarification, please feel free to let us know.
> **W1**: The applications are limited. The proposed method is on... | Summary: This paper introduces Graph to Visual and Textual Integration (GITA), a novel framework that enhances graph reasoning by integrating visual representations with traditional text-based processing. GITA's innovation lies in using both visual and textual inputs to address graph-structured data, a significant devi... | Rebuttal 1:
Rebuttal: We acknowledge and appreciate your insightful review. Below, you can find our responses addressing your concerns point by point. If you have any additional questions or require further clarification, please feel free to let us know.
**Note: Some tables mentioned in this rebuttal are contained in... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for your valuable feedback and thoughtful comments on our manuscript. We have carefully reviewed each of your concerns and have addressed them individually in the responses below. We believe that these revisions and clarifications have strengthened our manuscript, and we... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bayesian Strategic Classification | Accept (poster) | Summary: The paper studies partial information release in strategic classification. Roughly speaking, the learner publishes a set of classifiers containing the actual one being used, and the agents update their beliefs accordingly and best respond. The authors show that the agents' problem of best responding is gener... | Rebuttal 1:
Rebuttal: Thank you for these questions, we will now respond in detail.
**The model (while the model in the paper makes sense, I'm also curious about other natural ways to model the problem, e.g., the learner could commit to a (possibly restricted) distribution over classifiers and hide the realization fro... | Summary: The paper studies strategic classification problems where agents with partial information about the classifier can strategically report themselves at a cost. The problem is modeled as a Stackelberg game: The principal can first reveal partial information of the classifier, then the agents choose their strategy... | Rebuttal 1:
Rebuttal: Thank you for this review, we will now respond in detail.
**Positive results seem to be under strong constraints. This may imply that the problem itself is very difficult:**
A challenging aspect of work in strategic classification theory, along with other fields in ML is that with minimal assum... | Summary: * This paper investigates strategic classification in a partial-information setting using a Bayesian common-prior framework.
* The setting extends standard strategic classification (Hardt et al. 2016) to a partial-information setting by assuming that the deployed classifier $h\\in\\mathcal{H}$ is not fully kno... | Rebuttal 1:
Rebuttal: Thank you for these detailed comments which we shall now do our best to answer!
Part 1
**Interaction model, true relation between features and labels is known to the learner:**
This is an assumption made in prior work on strategic classification (e.g. see [2,4]). That being said, extending our ... | Summary: The paper introduces a novel framework for strategic classification using a Bayesian setting for agents' beliefs about the classifiers. This framework departs from the traditional assumption that agents have complete knowledge of the deployed classifier and instead assumes that agents have a prior distribution... | Rebuttal 1:
Rebuttal: We thank the reviewer for suggesting interesting questions that can be natural future directions of this work such as other utility functions and dynamic environments.
- Regarding the performance of partial information release versus full information release, while we have examples where one out... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LFME: A Simple Framework for Learning from Multiple Experts in Domain Generalization | Accept (poster) | Summary: This paper proposes a simple framework called LFME for learning from multiple experts in domain generalization. LFME introduces a logarithmic regularization term to enforce similarity between the target model and expert models, allowing the target model to acquire expertise from all source domains and perform ... | Rebuttal 1:
Rebuttal: > W1: List more computational cost as a limitation.
A1: We thank the reviewer for the suggestion, we will include this limitation in our revised paper.
> W2: Experiments with ViTs.
A2: We conducted further experiments by evaluating our method, the baseline ERM, and some leading methods in Tab. ... | Summary: This paper focuses on improving domain generalization by utilizing multiple experts. Particularly, a simple yet effective framework is proposed whereby a target (student) model is learned from multiple expert (teacher) models through logit regularization. After learning, the target model can grasp knowledge fr... | Rebuttal 1:
Rebuttal: > W1: The performance does not show much improvements in Tab. 5
A1: We want to clarify that the in-domain comparisons in Tab. 5 are only used to verify our claim that the target model has evolved to be an expert in all source domains. In the DG setting, out-of-domain performance is often regarded... | Summary: This paper addresses the problem of domain generalization from the perspective of ''learning multiple experts''. In particular, they propose to train multiple experts specialized in different domains, whose output probabilities provide professional guidance by simply regularizing the logit of the target model... | Rebuttal 1:
Rebuttal: > W1: Performance in the Meta-DMoE paper is higher than that in Tab. 1, and highlight the resource usage differences.
A1: Note that we mainly use the ResNet18 model for evaluating Meta-DMoE in Tab. 1, and the experiments are conducted for a total of 3x20 trials using the default hyper-parameter s... | Summary: The computer-vision paper introduces a strategy of learning from multiple experts (LFME), which performs knowledge distillation from models specially trained on data from different domains. In particular, the experts are trained jointly with the target model, and a specific form of logit regularization is chos... | Rebuttal 1:
Rebuttal: > W1. Novelty
A1: Differences between Meta-DMoE and LFME are as follow.
1. ### Domain experts in these two works serve different purposes
Meta-DMoE aims to adapt the trained target model to a new domain in test. To facilitate adaptation, their target model should be capable of identifying domai... | Rebuttal 1:
Rebuttal: We thank all reviewers for their hard work and insightful suggestions. We are inspired that Reviewer xxxE and h8LM find our work simple and Reviewer xxxE, h8LM, and oULp think the performance is strong. We are also glad that our in-depth theoretical insights are appreciated by Reviewer xxxE, h8LM,... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution | Accept (poster) | Summary: The paper thoroughly assesses the issue of identifiability of both the neural and physics components in hybrid deep generative models (hybrid-DGMs), and proposes a novel approach to formulate such models using meta-learning. The authors show the performance of the model in comparison to other hybrid-DGMs and t... | Rebuttal 1:
Rebuttal: We thank Reviewer 5WN4 for the supportive and constructive comments.
1. Resources: On the same device and dataset, Hybrid-VAE takes 250.24s for 50 epochs and Meta-Hybrid-VAE takes 291.68s. In all experiments, Meta-Hybrid-VAE requires approximately 0.5 times more epochs to converge.
2. Perform... | Summary: This paper discusses the identifiability of Hybrid Deep Generative Models and proposes a Meta-Learning-based approach to address the (un)identifiability issue of current DGMs. Theoretical discussions are provided, and experiments are conducted to verify the proposed method.
Strengths: This paper adopts the re... | Rebuttal 1:
Rebuttal: We thank Reviewer HAHc for the constructive comments.
1. Contribution: Please see the overall response 2 for the significance for the (un)identifiability of the neural component that we focused on, and response 3 for the theoretical contribution of our meta-formulation of identifiable-DGMs in g... | Summary: A method and a theory of learning hybrid deep generative models (esp., VAEs) are proposed. The method is based on meta-learning, and the theory is about the identifiability of the neural component's latent variable. The method is also empirically compared to baseline methods of hybrid DGMs.
Strengths: - In th... | Rebuttal 1:
Rebuttal: We thank Reviewer 1hrQ for the constructive comments.
1. Contribution: Please see the overall response 1 for our rationale for not focusing on the identifiability of the physics-component, response 2 for the significance for the (un)identifiability of the neural component that we focused on, an... | Summary: The original contribution for his manuscript is a metalearning framework to identify the physical parameters in a hybrid deep generative models.
Strengths: This article starts with a good question about the identifiability of physical parameters in hybrid deep generative models (that include physics as an ind... | Rebuttal 1:
Rebuttal: We thank Reviewer 1aic for the critical comments. Please see the overall response 4 (and the corresponding results in the attached pdf) for clarification regarding the effect of the number of parameters to be identified, and the overall response 1-3 (and the corresponding results in the attached p... | Rebuttal 1:
Rebuttal: We clarify the reviewers’ main questions about the theoretical contribution of this work as follow:
1. **Identifiability of the physics-based component in hybrid-DGMs**: The reviewers questioned our motivation for focusing on the un-identifiability of the neural (instead of the physics) component.... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GrounDiT: Grounding Diffusion Transformers via Noisy Patch Transplantation | Accept (poster) | Summary: The paper introduces a zero-shot layout technique for spatially-grounded text-to-image generation with the power of transformer-based diffusion architecture. Prior methods tend to manipulate the latent image during reverse process for grounding, solely relying on cross-attention maps that show the alignment be... | Rebuttal 1:
Rebuttal: We sincerely appreciate your review, acknowledging our work to be “well-written” and possess “superior efficacy to other methods”. Here, we address the concerns and questions that have been raised.
**(1) Clarification on the Main Method**
We would like to draw your attention to our general respo... | Summary: The paper targets exploiting a pre-trained PixArt-alpha (a text-to-image multi-aspect diffusion transformer) to generate images conditioned on a set of text-labeled bounding boxes.
The authors start from the idea that, at inference time, a transformer can be given an arbitrary number of tokens, and that these... | Rebuttal 1:
Rebuttal: We greatly appreciate your review, recognizing our method to be “very interesting” and “show a lot of promise”. Here, we address the concerns and questions that have been raised.
**(1) Clarifications on the Main Method**
**Please find the detailed clarification of the method in our general respo... | Summary: This paper explores the zero-shot layout-to-image generation problem using bounding boxes and texts as conditional inputs. The authors propose a method called GrounDiT, which builds upon the recent Diffusion Transformers (DiT) model. Leveraging DiT's emergent property of semantic sharing, where two noisy image... | Rebuttal 1:
Rebuttal: We greatly appreciate your review, acknowledging that our paper is “well-written” and the proposed method is “simple and intuitive”. Here, we address the concerns and questions that have been raised.
**(1) Additional Comparisons on Prompt Fidelity**
Here we provide further quantitative compariso... | Summary: This paper proposes a method to use a pre-trained text-to-image
diffusion model to guide the generation into placing objects at given
locations determined by bounding boxes. The challenge is to develop
this capability without requiring fine-tuning of the model.
Strengths: - The problem of guided generation wi... | Rebuttal 1:
Rebuttal: We sincerely appreciate your review, acknowledging that our work is solving a “relevant” problem of zero-shot guided generation through an “effective” method. Here, we address the concerns and questions that have been raised.
**(1) Clarifications on the Main Method**
We would like to draw your a... | Rebuttal 1:
Rebuttal: Here we clarify our problem definition and method. For clarity, we have slightly modified the notations to improve the description of the entire framework and readability. We will continue to improve the presentation of our paper in the revision.
**[Notations]**
- Let $P$ be the input global tex... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis | Accept (spotlight) | Summary: This paper addresses the issue of deep networks' sensitivity to domain shifts in medical image analysis, particularly for chest X-rays and skin lesion images. The authors propose Knowledge-enhanced Bottlenecks (KnoBo), a concept bottleneck model that incorporates explicit medical knowledge from resources like ... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback on our work! Here, we address your concern about applications in 3D images.
We agree with you that 3D modalities are important. However, we scoped our study to 2D modalities as these are cheaper in practice. Part of the motivation is to transfer to less-resour... | Summary: The authors noticed the domain-shift issue of current medical dataset, and after taking inspiration from medical training, they propose giving deep networks a prior grounded in explicit medical knowledge communicated in natural language. The proposed network can incorporate medical knowledge priors to help mod... | Rebuttal 1:
Rebuttal: Thanks for the feedback! We will try to include more examples in future versions. We hope the answers below clarify:
**Q1. What are the tasks?**
We studied the medical image classification tasks in a confounded setting (the medical class names are found in Tables 5 and 6). As explained in the se... | Summary: The presented paper addresses the challenge of domain shifts in medical image classification, where conventional neural networks often lack effective priors for medical datasets. The authors introduce KnoBo, a novel class of concept bottleneck networks that integrate medical knowledge priors to enhance neural ... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback on our work! Here is our reply to your questions:
**Q1. Hallucinations of GPT-4**
This is a valid concern, but the way we use GPT-4 highly encourages it to directly copy information from documents instead of inventing it. Our concept generation is conditioned... | Summary: This paper proposed Knowledge-enhanced Bottlenecks (KnoBo) to leverage the concept bottleneck model (CBM) to improve model robustness to various domain shifts and confounders. KnoBo explores using retrieval augmented generation to generate concept space with a large number of concepts using a large language mo... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. Here we address your comments:
**Q1. The concept grounding seems pretty data-hungry.**
This is a valid concern, but while we sample from a large dataset like MIMIC, in reality, we only use 22k total examples. Training the concept grounding component is j... | Rebuttal 1:
Rebuttal: We deeply appreciate the time and effort all reviewers have contributed. We feel very encouraged to see that all the reviewers have overall positive attitudes towards our work. Reviewers found our work interesting (f9Nf) and praised the **novelty of our method** (T8n7, tMbd) with **comprehensive e... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GIC: Gaussian-Informed Continuum for Physical Property Identification and Simulation | Accept (oral) | Summary: The paper proposes an improvement of PAC-NeRF for the task of estimating material properties from multiview video using 3D Gaussian Splatting (3DGS). Instead of estimating geometry solely based on the first frame like PAC-NeRF, the proposed method uses 4D Gaussian Splatting (4DGS) with reduced order modeling t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed reading of our paper and constructive suggestions! We hope our responses adequately address the following questions about our work. Please let us know if there’s anything we can clarify further.
---
> 1. Some symbols are not defined clearly, making it hard ... | Summary: This paper introduces a novel hybrid method that leverages 3D Gaussian representation and continuum to estimate physical properties of deformable objects. From multi-view video, the Gaussian- informed continuum can be extracted and then combined with material point method (MPM) simulation to train the whole pi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed reading of our paper and constructive suggestions! We hope our responses adequately address the following questions about our work. Please let us know if there’s anything we can clarify further.
---
> 1. The authors stated that such a lightweight architectu... | Summary: The manuscript proposes a novel hybrid framework that leverages 3D Gaussian representations for system identification from visual observations. The framework captures both explicit and implicit shapes using dynamic 3D Gaussian reconstruction and a coarse-to-fine filling strategy to generate density fields. The... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed reading of our paper and constructive suggestions! We hope our responses adequately address the following questions about our work. Please let us know if there’s anything we can clarify further.
---
> 1. In the real-world experiment, I found the authors swi... | Summary: This paper presents an approach for estimating the geometry and physical properties of objects through visual observations using 3D Gaussian representations. The method employs a dynamic 3D Gaussian framework to reconstruct objects as point sets over time and a coarse-to-fine filling strategy to generate densi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed reading of our paper and constructive suggestions! We hope our responses adequately address the following questions raised about our work. Please let us know if there’s anything we can clarify further.
---
> 0. Some of the technical terms are misused, makin... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to all the reviewers for their time and their valuable feedback. We deeply appreciate their recognition of our work, such as
"The experiments in this paper demonstrate improvements over prior works" (FGDU),
"I am happy to see the proposed method als... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
U-DiTs: Downsample Tokens in U-Shaped Diffusion Transformers | Accept (poster) | Summary: The paper presents a novel approach to enhancing diffusion models for image generation using Transformers. It introduces U-shaped Diffusion Transformers (U-DiTs) that employ token downsampling within the self-attention mechanism of U-Net architectures, aiming to reduce computational cost while maintaining or i... | Rebuttal 1:
Rebuttal: Dear reviewer JEXU,
Thank you very much for your review. Here are our responses:
**W1: Fail to compare with other efficient DiT models, like PixArt-Alpha.**
Thanks for your advice. Here we provide a comparison with powerful baselines: PixArt-Alpha [1], as well as U-ViT [2] and DiffiT-XL [3]. We... | Summary: This paper introduces a U-shaped diffusion Transformer (U-DiT) model, inspired by the departure from U-Net in DiT. The authors aim to combine the strengths of U-Net and DiT to determine if the inductive bias of U-Net can enhance DiTs. Initially, a simple DiT-UNet model was developed, but it showed minimal impr... | Rebuttal 1:
Rebuttal: Dear reviewer GsTx,
Thank you very much for your comments. Here are our responses:
**W1:** Statement "the latent feature space is not downsampled" is misleading.
**A1**: Thanks for your suggestions. We will add qualifiers to this statement and limit it to the intermediate features.
**W2&Q1:** ... | Summary: This paper proposes a transformer architecture as backbone for
diffusion modeling that is based on the UNet. The paper shows that a
variation of the transformer with downsampling layers and skip
connections achieves better results than the DiT at a lower
compoutational cost.
Strengths: - Experimental results ... | Rebuttal 1:
Rebuttal: Dear reviewer AGQh,
Thank you very much for your comments. Here are our responses:
W1.. **Novelty is limited.**
Our work is not an improvement of U-ViT. The architecture of our U-DiT model is **completely different from U-ViT**: we adopt a U-Net architecture, while U-ViT is an isotropic archit... | Summary: The authors conduct a simple toy experiment by comparing a U-Net architectured DiT with an isotropic one. They find that the U-Net architecture only gains a slight advantage, indicating potential redundancies within the U-Net-style DiT. Inspired by the discovery that U-Net backbone features are low-frequency-... | Rebuttal 1:
Rebuttal: Dear reviewer Df8i,
Thank you very much for your suggestions. Here are our responses:
**W1.** We omitted U-ViT and Hourglass DiT previously, because these models are mainly targeted at pixel-space generation, while our work is focused on latent space generation. To demonstrate the advantage of o... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
State-free Reinforcement Learning | Accept (poster) | Summary: The paper proposes a black-box approach to make any no-regret algorithm to a state-free algorithm. The topic this paper works on is a very important topic. The theoretical results in this paper are significant, if correct. That said, the algorithm description is hard to follow, and hence, I could not verify if... | Rebuttal 1:
Rebuttal: Thank you for the comment! We believe there are some misunderstandings, and have clarified them below. We hope the reviewer could reevaluate our paper, and are very happy to respond more questions during the reviewer-author discussion period.
**Q1**: What is $\pi^\bot$?
**R1**: $\pi^\bot... | Summary: This paper proposes a kind of parameter-free reinforcement learning where the algorithm does not need to have the information about states before interacting with the environment. To achieve this, the authors design a black-box reduction framework which can transform any existing RL algorithm for stochastic or... | Rebuttal 1:
Rebuttal: Thank you for the comment! We would like to respectfully ask the reviewer to reassess our paper in light of the rebuttal. We believe the reviewer's concern was peripheral to our main contributions and would like to receive a fair assessment of our work.
**Q1**:
While I understand that the focus... | Summary: The paper studies the problem of online reinforcement learning in the tabular setting when no prior knowledge about the size of the state space is available. Unlike existing algorithms, that usually require the size S as input parameter, the proposed algorithm is fully adaptive and its final performance scales... | Rebuttal 1:
Rebuttal: Thank you for the instructive feedback! Below we address some of the questions raised by the reviewer.
**Q1**: I would encourage the authors to provide a clean comparison of the final bounds in the stochastic setting with the best available bounds. In particular, I'm wondering whether the restart... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TextCtrl: Diffusion-based Scene Text Editing with Prior Guidance Control | Accept (spotlight) | Summary: Considering the limitations of current GAN-based and Diffusion-based Scene Text Editing (STE) methods, this paper introduces TextCtrl, a diffusion-based method that edits text with prior guidance control. Specifically, TextCtrl incorporates text style disentanglement and text glyph structure representation to ... | Rebuttal 1:
Rebuttal: Thank you for the detailed review. We are encouraged by the positive comments on novelty of the method and the contribution of the proposed dataset. The concerns are taken care to address point by point in the following.
> **[W1]: Discussion about the hyperparameters in TextCtrl.**
[A1]: Thanks... | Summary: This paper aims to enhance scene text editing performance using a conditional prior-guidance-control diffusion model. It decomposes text style into background, foreground, font glyph, and color features. A text glyph structure representation improves the correlation between the text prompt and glyph structure.... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments. Your detailed review will certainly help improve the revised paper. The remaining concerns are taken care to address point by point in the following.
> **[W1]: For the ablation study of text style disentanglement, why replace the style encoder with ControlNet... | Summary: This paper proposes TextCtrl, which is a new method for high-fidelity scene text editing. The authors identify the primary factor hindering previous methods from achieving accurate and faithful scene text editing to be the absence of prior guidance on stylistic elements and textual organization. By leveraging ... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments. We are encouraged by the positive response to the paper presentation and ScenePair dataset. The remaining concerns are taken care to address point by point in the following.
> **[Q1]: A big picture of why all the components working together in style pretra... | Summary: This manuscript proposes a diffusion-based scene text editing method with prior guidance control. It incorporates style-structure guidance into the model to enhance the text style consistency and rendering accuracy. A Glyph-adaptive mutual self-attention mechanism is designed to deconstruct the implicit fine-g... | Rebuttal 1:
Rebuttal: Thank you for the detailed review. We are encouraged by the comments that TextCtrl serves as an innovative approach and ScenePair provides a more holistic assessment to STE methods. The concerns are taken care to address point by point in the following.
> **[W1/Q1]: Limitation in task scope to o... | Rebuttal 1:
Rebuttal: We thank all reviewers for their thoughtful and constructive feedback. It's encouraged to hear from the reviewers that
- The Model TextCtrl: *"Innovative Approach; addresses weak correlation between text prompts and glyph structures; generate edited image with both high style fidelity and high re... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper addresses the challenges of Scene Text Editing (STE) by introducing a diffusion-based STE method, TextCtrl. Traditional GAN-based STE methods struggle with generalization, and existing diffusion-based methods face issues with style deviation. TextCtrl overcomes these limitations through two main comp... | Rebuttal 1:
Rebuttal: Thank you for the comprehensive review, which will certainly help improve the revised paper. The concerns are taken care to address point by point in the following.
> **[W1]: Discussion of more style prior information from unmasked region.**
[A1]: We agree that surrounding unmasked region could... | null | null | null | null | null | null |
Efficient Algorithms for Lipschitz Bandits | Reject | Summary: The paper proposes two algorithms for Lipschitz bandit problems, with improved time complexity and memory requirements.
Strengths: If the algorithms and proofs are sound, then this is an excellent contribution. Developing these sort of streaming/sketching methods for key bandit problems (such as Lipschitz ban... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable time and effort in reviewing this paper. We thank you for the thoughtful feedback you provided, which has significantly improved the quality of this paper. We also appreciate your recognition of the contributions our paper makes.
In Section 2.2, we primarily ... | Summary: The paper considers regret minimization for Lipschitz bandits with time horizont $T$ and proposes an algorithm that provably achieves nearly optimal regret while having strictly smaller (by a factor of $T$) time (of order $O(T)$) and memory complexity (of order $O(1)$). This is achieved by considering a tree-l... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable time and effort in reviewing this paper. We thank you for the thoughtful feedback you provided, which has significantly improved the quality of this paper. For the potential concerns you bring up, we would like to address them here.
**Q1: It is not simple to ... | Summary: The paper investigates a multi-armed bandit problem where the action space is a metric space a stochastic Lipschitz rewards. The authors present algorithms that use a constant amount of memory and achieve a near-optimal regret. This improves on previous results that had heavy memory usage.
Strengths: The pape... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable time and efforts in reviewing this paper. We thank the thoughtful feedback you provided, which significantly improved the quality of this paper. For the potential concerns you bring up, we would like to answer/address them here.
**Q1: "While the result is gre... | Summary: This paper studies the Lipschitz bandit problem with a memory constraint. There are two algorithms proposed by the authors. The Memory Bounded Uniform Discretization (MBUD) algorithm uses a fixed discretization over the metric space and implements a strategy which explores first and then commits to an exploita... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable time and efforts in reviewing this paper. We thank the thoughtful feedback you provided, which significantly improved the quality of this paper. For the potential concerns you bring up, we would like to answer/address them here.
**Q1: I am unclear about the n... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Automatically Learning Hybrid Digital Twins of Dynamical Systems | Accept (spotlight) | Summary: The paper presents a neurosymbolic approach to model dynamical systems based on the usage of LLMs and gradient-based optimization. The model perform competitively against the reported baselines.
Firstly, the human modeller is required to define the problem, priors and target metrics in text. Then the first L... | Rebuttal 1:
Rebuttal: *We appreciate the reviewer’s thorough evaluation and positive feedback.*
---
## [P1] Clarifying computational budget
Thank you for raising this question. To clarify:
1. All neural baselines and each HDTwinGen model use identical training settings (2000 epochs, 1000 batch size), consuming the sam... | Summary: This paper presents a LLM-powered evolutionary multi-agent algorithm to automate the composition of hybrid digital twins of dynamical systems. Experiments were conducted on several datasets including variants of PKPD model synthesized data, simulated covid-19 data, simulated plankton miscrocosm data, and a rea... | Rebuttal 1:
Rebuttal: *We thank the reviewer for their thoughtful and helpful review. We’re glad that the reviewer finds our approach to be both highly novel and of high potential impact, though we agree that the addition of existing hybrid models improves the paper significantly.*
---
## [P1] Extending the literature... | Summary: This paper describes a method to improve digital twins (DTs) with an evolutionary and human driven dynamics, aided by an LLM. The process is meant to optimize hybrid models with both human-driven and computer search-based optimization.The empirical study uses six datasets from the medicine domain. Suitable bas... | Rebuttal 1:
Rebuttal: *We thank the reviewer for their constructive feedback. We’re glad the reviewer finds the approach general and of high potential.*
---
## [P1] Clarifying "Multi-agent" terminology
We appreciate your comment on our system's classification as "multi-agent". Upon reflection, we agree that our framew... | Summary: An evolutionary search framework for building hybrid dynamics models, especially for so-called digital twins, is proposed. Its notable feature is the use of LLMs for the model proposal and model evaluation in the search. The effectiveness of the method is validated with multiple datasets.
Strengths: - Learnin... | Rebuttal 1:
Rebuttal: *We thank the reviewer for their thorough review. We are pleased that the reviewer finds our approach interesting, addressing an important research topic, with careful experimental analysis.*
---
## [P1] Incorporating additional baselines
Thank you for this suggestion. While we included SINDy fo... | Rebuttal 1:
Rebuttal: *We are grateful to the reviewers for their insightful feedback and constructive comments that have improved the paper.*
We are encouraged by the reviewers' recognition of our work's novelty and potential impact. Reviewers highlighted our approach as "highly novel and of high potential impact" (*... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Ensemble sampling for linear bandits: small ensembles suffice | Accept (poster) | Summary: This paper presents theoretical results for an exploration algorithm using ensemble sampling in the stochastic linear bandit setting. The proposed algorithm creates $2m = O(d \log T)$ ensemble models, selects one model uniformly at random, and then chooses the greedy action based on the parameters of that mode... | Rebuttal 1:
Rebuttal: Thank you for your review. There is an unfortunate, but crucial, misunderstanding, in the relation between our work and algorithms such as Thompson sampling and Perturbed History Exploration (PHE), which we believe is responsible for your low scoring of our work. We have clarified these relations ... | Summary: The paper presents a regret analysis of ensemble sampling within the stochastic linear bandit framework. It demonstrates that an ensemble size scaling logarithmically with time and linearly with the number of features suffices, marking a theoretical advancement. The paper shows that under standard assumptions ... | Rebuttal 1:
Rebuttal: We must clarify two important misunderstandings:
1. Our upper bound scales superlinearly with m, but that does not mean that the regret of the algorithm scales superlinearly with m. Our bound is simply not tight when m goes to infinity (we will adjust the wording of Remark 2 to make this clear). I... | Summary: The authors study an upper confidence bound (UCB) type of ensemble sampling method specific to the stochastic linear bandits. Based on previous work, it improves the analysis by introducing Rademacher variables for symmetrising the ensemble. The authors are thus able to obtain $\sqrt{T}$ dependency in the regr... | Rebuttal 1:
Rebuttal: We would ask that the reviewer confirms that they have read our main rebuttal, and to acknowledge that they understand and agree that our paper solves an open problem in the theory community that has attracted two previous failed attempts—both published at NeurIPS. With that in mind, we would ask ... | Summary: This paper studies the ensemble sampling in the stochastic linear bandit setting, and shows that the ensemble sampling can achieve $\mathcal{O}( (d\log T)^{5/2} \sqrt{T})$ regret with an ensemble size of order $\mathcal{O}(d log T)$. The authors claim this is the first meaningful result of ensemble sampling a... | Rebuttal 1:
Rebuttal: While our focus is on the novel proof ideas and techniques needed to get a bound for ensemble sampling, we would be happy to use the additional page available at the camera ready stage to provide more context on ensemble sampling and its relation to other methods.
To answer the reviewer’s two “we... | Rebuttal 1:
Rebuttal: We respectfully disagree with the current assessment of our work and would like to encourage the reviewers to reconsider the following: We believe that the successful resolution of a long-standing open problem, one that has attracted multiple prior attempts, holds significant value within the Neur... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
4Real: Towards Photorealistic 4D Scene Generation via Video Diffusion Models | Accept (poster) | Summary: 4Real presents a novel framework for dynamic 3D scene generation. Given an input video, the framework selects a frame and prompts a text-to-video diffusion model to generate a "freeze-time video" that reconstructs the canonical 3D scene. Then, video-SDS-based optimization is used to optimize a dynamic 3DGS wit... | Rebuttal 1:
Rebuttal: **Context embedding.** First, the dataset for “static real-world objects with circular camera trajectories”, consists of data similar to the MV-ImagNet, with real-world backgrounds. Second, the use of context embedding is critical, as it controls the video model to generate freeze-time-like video.... | Summary: The paper introduces a pipeline designed for photorealistic text-to-4D scene generation, discarding the dependency on multi-view generative models and instead fully utilizing video generative models trained on diverse real-world datasets. The method begins by generating a reference video using the video genera... | Rebuttal 1:
Rebuttal: **Latency comparison.** In Ln. 64, we mentioned that our overall runtime is approximately 1.5 hours on a single A100 GPU, compared to over 10 hours for 4Dfy and Dream-in-4D. To provide additional latency analysis, we break down our runtime as follows: approximately 10 minutes for reference and fre... | Summary: This paper proposes a straightforward method to distill a 4D dynamic scene from a video diffusion model. Given a reference video (only generated videos are shown in the paper, no real video), they first generate a freeze-time video as a multi-view to reconstruct a reference 3DGS. Because they don't have a good... | Rebuttal 1:
Rebuttal: **Baseline by 3D scene generation + 4D object-centric generation.**
Although this is a straightforward baseline, implementing it with high quality is challenging.
- First, inserting 3D objects with realistic and physically correct placement is not trivial. Common issues include floating objects,... | Summary: This paper proposes a new pipeline called 4Real, aiming for more photorealistic text-to-4D generation than prior work. The method first generates a reference video, then learn a canonical 3D representation from a freeze-time video. Afterwards, per-frame and temporal deformation are learned to model the gap bet... | Rebuttal 1:
Rebuttal: **Interaction between objects and environments.** Our approach aims for a generalizable approach to generate realistic interaction between objects and environments, which includes natural multi-object placement, realistic environmental lighting effects, and relative motions between objects and env... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for the positive feedback and insightful comments. We appreciate all the suggestions and will revise the paper accordingly.
To address major concerns from the reviewers, we include the following additional results in the PDF:
- **Additional baseline results... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents a method for 3D scene generation using video diffusion models. The main contribution is to present a method that does not require a multi-view generative model trained on synthetic data. The paper identifies the use of multi-view models as a strong limitation limiting existing works to gener... | Rebuttal 1:
Rebuttal: **Ablation of finetuning with synthetic data.** This is a great suggestion to rigorously prove the benefit of using models trained with real world data. However, conducting such experiments requires significant GPU hours to reach a meaningful conclusion, which we are unable to fulfill for rebuttal... | null | null | null | null | null | null |
Mixtures of Experts for Audio-Visual Learning | Accept (poster) | Summary: They propose a novel and simple method based on Mixture of Experts (MoE) for audio-visual learning.
Strengths: - Simple yet effective method.
- State-of-the-art results.
- Overall, is well written.
Weaknesses: - Does not include demos with audio to listen in a website.
- Does not include code.
Minor notes o... | Rebuttal 1:
Rebuttal: Dear reviewer usTX,
Thank you for the positive feedback and valuable suggestions! Please see the following for our point-by-point reply.
---
**Weakness - Demos**
Thanks for your advice! We totally agree with the importance of providing audio-visual demos for a more comprehensive understanding o... | Summary: This paper introduces an interesting observation i.e. for the off-screen sound cases or some mixed audio cases, ``injecting cross-modal adapters only brings disturbing information``. To solve the problem. this paper proposes a Mixture of expert strategy that can let the model focus on different aspects of the... | Rebuttal 1:
Rebuttal: Dear reviewer k9Vn,
Thank you for taking the time to read our paper. We hope our responses adequately addresses your concerns.
---
**Summary - The improvements this approach brings to downstream tasks are limited.**
Thanks for your comments! However, we argue that focusing only on some of the e... | Summary: The manuscript proposes the Audio-Visual Mixture of Experts (AVMoE) approach for multiple audio-visual tasks, aiming to explore parameter-efficient transfer learning for audio-visual learning. Specifically, AVMoE introduces both unimodal and cross-modal adapters as multiple experts, specializing in intra-modal... | Rebuttal 1:
Rebuttal: Dear reviewer YVYv,
Thank you so much for these very valuable and constructive suggestions! Please kindly find the point-to-point responses below.
---
**Differences between AVMoE and LAVisH**
Thanks for your feedback! The similarity between AVMoE and LAVisH is the bottleneck architecture of ada... | Summary: This paper proposed an integrated adaptor architecture that makes use of cross-modal attention mechanism and Mixture-of-Experts. The architecture connects (separately) trained audio and visual encoder to do audio-visual tasks, including audio-visual event localization, audio-visual segmentation, audio-visual q... | Rebuttal 1:
Rebuttal: Dear Reviewer jXV1,
Thanks for your careful review and kindly comments on this paper. Please see our point-by-point responses below.
---
**Weakness - The inference cost, training time, and flops are not reported.**
Thanks for your suggestion! Following LAVisH and DG-SCT, we only report the tr... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for your valuable and constructive comments. Our motivation is to propose a general, efficient, and flexible method for audio-visual learning, and your suggestions will definitely help us strengthen our work. Please kindly find the point-to-point responses below.
P... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes AVMoE, a new way of adding adapters to pretrained audio-visual models.
AVMoE extends the LAVisH approach in two ways:
(1) Using unimodal adapters in addition to cross-modal LAVisH adapters.
(2) Adding a router network to dynamically combine the output of multiple unimodal/cross-modal ad... | Rebuttal 1:
Rebuttal: Dear reviewer pdXh,
Thank you so much for these very insightful and constructive comments, please see the following for our point-by-point responses.
---
**Weakness - Experimental Design Ablation**
Following your great advice, we conducted more experiments to analyze the contribution of each co... | null | null | null | null | null | null |
Achieving Tractable Minimax Optimal Regret in Average Reward MDPs | Accept (poster) | Summary: The paper proposes the first tractable algorithm that achieves minimax optimal regret for average reward tabular MDPs. The algorithm does not require prior information on the span of the optimal bias function.
Strengths: The paper proposes the first tractable algorithm that achieves minimax optimal regret for... | Rebuttal 1:
Rebuttal: We address your concerns below.
> 1. The minimax lower bound in [4] is $\sqrt{DSAT}$. Because $H=\mathrm{span}(h^*) \le D$, I wonder why $\sqrt{HSAT}$ is even achievable by the algorithm developed in the paper.
The lower bound of [4] is indeed $\sqrt{DSAT}$, but like pointed out by [14], the "h... | Summary: The authors propose a novel algorithm for weakly communicating Markov Decision Processes (MDPs) with an average-reward objective. This algorithm, at the same time, is a) tractable since it does not rely on the exact solution to a high-dimensional non-convex optimization problem as prior work, and b) achieves m... | Rebuttal 1:
Rebuttal: ## Concerning the weaknesses
> The algorithm requires solving the linear programs for each doubling epoch, which is tractable but non-generalizable beyond the tabular setting;
Yes, the algorithm needs to solve many linear programs at every epoch for the vanilla version of `PMEVI`. However, solvi... | Summary: This paper studies learning in average reward MDPs, and presents the first minimax optimal algorithm (in terms of $sp(h^*)$) which is computationally tractable, and also simultaneously the first which does not require prior knowledge of $sp(h^*)$.
Strengths: The main theorem resolves a longstanding open probl... | Rebuttal 1:
Rebuttal: We have numbered your questions to gain a few characters and address all of them.
## Concerning the weaknesses
> The algorithm is rather complicated and has many components (...) While it is interesting that this method may incorporate different confidence regions, I wonder if it might be more c... | Summary: The paper shows that by replacing the extended value iteration in optimistic methods, like UCRL2, it is possible to obtain regret that scales optimally with the number of states, actions, the time horizon and the span of the optimal value function, instead of the diameter, despite not knowing the diameter, ass... | Rebuttal 1:
Rebuttal: ## About weaknesses
> The presentation is not great; the paper feels rushed, the paper is full of (minor) grammatical and typographical errors (starting with the abstract: "encounter suffer")
We will do our best to correct as many typos as possible, that can make understanding the paper difficul... | Rebuttal 1:
Rebuttal: We appreciate the reviewers for their constructive suggestions. Please find our responses to the reviews below.
**From your collective feedback, it emerges a shared concern about the numerical experiments.** Indeed, the left part of Figure 2 shows that the projection-mitigation operations of `PME... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ID-to-3D: Expressive ID-guided 3D Heads via Score Distillation Sampling | Accept (poster) | Summary: The paper introduces a novel method for generating 3D human heads guided by both identity and textual descriptions. The proposed method employs face image embeddings and textual descriptions to optimize a neural representation for each subject. By leveraging task-specific 2D diffusion models as priors and a ne... | Rebuttal 1:
Rebuttal: # 5 Response to reviewer `fD9E`
We thank the reviewer `fD9E` for recognizing our work as "novel and promising", acknowledging its "superiority [...] over existing methods" and appreciating its "versatility". Below we address the proposed concerns about the limitations of our method and its potent... | Summary: The paper presents a novel technique for creating 3D human head models from a single real-world image, guided by identity and text. This method is based on compositionality and uses task-specific 2D diffusion models as optimization priors. The authors extend a base model and fine-tune only a small training par... | Rebuttal 1:
Rebuttal: # 4 Response to reviewer `G3ux`
We appreciate the reviewer's thorough feedback and the definition of our work as a "significant innovation in the field of 3D head generation". We provide all the requested clarification and experiments below. **We regularly refer to the general response above and t... | Summary: This work proposes a new approach for the generation of 3D human heads, which enables guidance with identity, facial expressions, and text descriptions. The approach is structured around two principal components: 1) the authors fine-tune a previously established text-to-image diffusion model through LORA on a ... | Rebuttal 1:
Rebuttal: # 3 Response to reviewer `vm8B`
We thank the reviewer for the feedback. We thoroughly clarify and respond to all the concerns raised. **We regularly refer to the general response above and the one-page pdf provided with the figures.**
## 3.1 Discussion and comparisons on the quality of the genera... | Summary: **Summary
This paper focuses on the task of 3D head generation. Specifically, the authors first extend a traditional diffusion model to a text-to-normal version and a text-to-albedo version with ID-aware and expression-aware cross-attention layers. Then, with the trained diffusion models, the authors optimize ... | Rebuttal 1:
Rebuttal: # 2 Response to reviewer `HnyJ`
We thank the reviewer for recognizing the novelty of our method and appreciating the experimental section. Below we respond to the doubts put forward by the reviewer. **We regularly refer to the general response above and the one-page pdf provided with the figures.*... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers’ insightful feedback, which acknowledges the novelty of our method (G3ux, HnyJ, fD9E) and the quality of our experimental results (G3ux, HnyJ, fD9E, vm8B). The reviewers recommended additional experiments and visualizations to emphasize strengths, address limi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction | Accept (poster) | Summary: The authors propose 'diffusion plug-and-play' (DPnP), a plug-and-play diffusion framework that alternatively calls what amounts to a consistency sampler based on the likelihood of the forward model, followed by (essentially) and unconditional diffusion step using the score function. The authors provide many su... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and insightful comments. We will make sure to take your invaluable suggestions into account in revising the paper and in our future work. Below we address the two questions you raise.
**Connections with projection-type approaches.**
- Thank you for your though... | Summary: This paper introduces a diffusion plug-and-play method (DPnP) that uses score-based diffusion models as expressive data priors for nonlinear inverse problems with general forward models. By combining a proximal consistency sampler and a denoising diffusion sampler, the method offers provably robust posterior s... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback on our theoretical contribution and for your insightful comments. It seems that the reference items [1-2] in the review are unfortunately missing, so this rebuttal will be based on our understanding of general related literatures. We would really appreciate it ... | Summary: This paper introduces a diffusion-based sampling framework closely related to plug-and-play methods for solving general inverse problems. The technique alternates between two steps: calling a proximal consistency sampler that enforces data-fidelity, and regularization via a denoising diffusion sampler leveragi... | Rebuttal 1:
Rebuttal: Thank you for your careful review and insightful suggestions. Below we address your concerns in a point-to-point manner.
**Comparison with ReSample [SKZ+23].**
- Thank you for your suggestion. There are a few recent algorithms that included a stochastic correction step for better robustness. The ... | Summary: This paper focuses on developing a plug-and-play algorithm for using score-based diffusion models as an expressive prior for solving nonlinear inverse problems with general forward models. While going from current state $x_{k+1}$ to $x_{k}$, this paper makes two gradient updates: (1) from $x_{k+1}$ to $x_{k+\f... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and valuable comments. Below we address your comments in a point-to-point manner.
**Examples for Assumption 1 on the forward model and the likelihood (point 1,2).**
- A typical scenario is where the measurement noise $\xi$ in the measurement model $y=\mathcal{A... | Rebuttal 1:
Rebuttal: # General Response to All Reviewers
We would like to express our cordial thanks to all the reviewers for their careful review and constructive feedback. Below we address some common concerns raised by the reviewers. Our point-to-point response can be found in the separate rebuttal to each reviewe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
End-to-End Autonomous Driving without Costly Modularization and 3D Manual Annotation | Reject | Summary: This paper handles the costly modularization and 3D manual annotation in current end-to-end autonomous driving, which proposes an unsupervised pretext task to provide necessary environmental information, as well as a direction-aware training strategy to enhance the robustness in safety-critical steering scenar... | Rebuttal 1:
Rebuttal: ## Response to Reviewer YXrs:
We appreciate your careful review and thoughtful comments. We are encouraged and grateful that the reviewer found our approach to be well-motivated and insightful. Below, we address the concerns that were raised, and remain committed to clarifying further questions th... | Summary: This paper addresses the limitations of current end-to-end autonomous driving models that still rely on modular architectures with manually annotated 3D data. The authors propose an unsupervised pretext task that eliminates the need for manual 3D annotations by predicting angular-wise spatial objectness and te... | Rebuttal 1:
Rebuttal: ## Response to Reviewer 3W73:
We appreciate your careful review and thoughtful comments. We are encouraged and grateful that the reviewer found our approach to be well-motivated and innovative. Below, we address the concerns that were raised, and remain committed to clarifying further questions th... | Summary: This paper aims to discard the requirement of 3D manual annotation in end-to-end autonomous driving by the proposed angular perception pretext task. Besides, this paper proposes a direction-aware learning strategy consisting of directional augmentation and directional consistency loss. Finally, the proposed me... | Rebuttal 1:
Rebuttal: ## Response to Reviewer UZRG:
We thank the reviewer for providing helpful comments on our work. We provide our responses below to address reviewer’s concerns, and remain committed to clarifying further questions that may arise during discussion period.
***
***Q1: Lacking explanation on how to use ... | Summary: The article proposes an end-to-end (E2EAD) autonomous driving method called UAD (Unsupervised Autonomous Driving), which achieves autonomous driving on a visual basis without the need for expensive modular design and 3D manual annotation. UAD aims to overcome the limitations of existing E2EAD models that mimic... | Rebuttal 1:
Rebuttal: ## Response to Reviewer XcsQ:
We thank the reviewer for providing thoughtful comments on our work. We provide our responses below to address reviewer’s concerns, and remain committed to clarifying further questions that may arise during discussion period.
***
***Q1: The paper lacks sufficient com... | Rebuttal 1:
Rebuttal: **Dear Reviewers,**
We thank all reviewers for their very careful review, valuable comments and suggestions on our manuscript. We have worked our best to address all concerns with analyses and experiments according to the comments from reviewers.
In specific, for **Reviewer d489**'s comments, we... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes a new e2e driving model named UAD. In this paper, the authors propose an unsupervised method for effective training and inference of the e2e model. The paper mainly has two contributions: 1. It designs an angular-wise perception module. In this module, the authors directly project 2D GT labe... | Rebuttal 1:
Rebuttal: ## Response to Reviewer d489:
We thank the reviewer for providing valuable and thoughtful comments on our work. We provide our responses below to address the reviewer's concerns, and remain committed to clarifying further questions that may arise during the discussion period.
***
***Q1: The worki... | null | null | null | null | null | null |
Diffusing Differentiable Representations | Accept (poster) | Summary: This paper introduces a zero-shot method to sample neural representations with pre-trained diffusion models. By pulling back the measure over the data space through the representation, the authors express the PF ODE in the parameter space. Solving this ODE can directly provide parameter samples of the represen... | Rebuttal 1:
Rebuttal: Thank you for your comments and review. We will address your concerns in turn.
Experiment details: As mentioned in the paper, generating a NeRF with our method takes around 24 minutes for 199 NFEs. We used as many samples as possible in the 40GB VRAM of an NVIDIA A6000; our experiments were eigh... | Summary: The paper introduces a method for sampling a differential representation using a pre-trained diffusion model. Instead of sampling in the image space, the authors propose sampling in the parameter space of differential representations by 'pulling back' the probability distribution from the image space to the pa... | Rebuttal 1:
Rebuttal: Thank you for your comments and review. We will address your concerns in turn.
SJC and Dreamfusion (SDS) are extremely similar in methodology when accounting for the conversion between the denoising model and the score function. The key differences are in the weighting function that the expectat... | Summary: This paper introduces a novel, training-free method to sample through differentiable functions using pretrained diffusion models.
Strengths: It sounds a general method that could apply to many different scenarios.
Weaknesses: More systematic evaluation of the method in addition to image examples would be gre... | Rebuttal 1:
Rebuttal: Thank you for your positive review. Please see the common response for additional SIREN panoramas. Let us know if there are any remaining concerns we might be able to address. | Summary: The present paper introduces a training-free method to sample differentiable representations using pre-trained diffusion models. This is achieved by pulling back the dynamics of the reverse-time process from the image to the parameter space. Moreover, training-free methods for conditional sampling are employed... | Rebuttal 1:
Rebuttal: Thank you for your comments and review. We will address your concerns in turn.
We have added a discussion of additional 3D asset generation from image diffusion methods like Zero-1-to-3, HiFA, Magic123, LatentNeRF, and Fantasia3D, which you mentioned. These works build off the Dreamfusion (SDS) ... | Rebuttal 1:
Rebuttal: Firstly, we would like to thank all the reviewers for their thoughtful comments and feedback on our submission. We appreciate the opportunity to address your concerns and clarify aspects of our work.
We want to reiterate the contribution of our work as a superior method to perform true sampling o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.