paper_id stringlengths 19 21 | paper_title stringlengths 8 170 | paper_abstract stringlengths 8 5.01k | paper_acceptance stringclasses 18
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nips_2022_Tocn9vYMU-o | Approaching Quartic Convergence Rates for Quasi-Stochastic Approximation with Application to Gradient-Free Optimization | Stochastic approximation is a foundation for many algorithms found in machine learning and optimization. It is in general slow to converge: the mean square error vanishes as $O(n^{-1})$. A deterministic counterpart known as quasi-stochastic approximation is a viable alternative in many applications, including gradient-... | Accept | All reviewers are happy with the new ideas and strength of results in this paper. | train | [
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" You may be reassured to learn that we discovered an error in our statement on dependencies: the exponential bound in Baker’s Theorem concerns $K$ (the number of frequencies) and NOT $d$. Origin of error: we took $K=d$ to simplify notation. We apologize for the confusion this caused!\n\nTo avoid assuming $K=d$ we ... | [
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nips_2022_KnCS9390Va | Delving into Out-of-Distribution Detection with Vision-Language Representations | Recognizing out-of-distribution (OOD) samples is critical for machine learning systems deployed in the open world. The vast majority of OOD detection methods are driven by a single modality (e.g., either vision or language), leaving the rich information in multi-modal representations untapped. Inspired by the recent su... | Accept | This paper presents an interesting and novel try at using vision-language multi-modal models for OOD tasks. The experiments are sufficiently validated on diverse OOD datasets. Besides, the theoretical explanations of softmax scaling are quite insightful. All reviewers give positive scores. During the discussion phase, ... | val | [
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" >**Q1: Comparisons with recent works**.\n\nThanks for the suggestion! For the Energy score, please refer to Appendix F.1 for a detailed discussion where we investigate the effectiveness of Energy score based on CLIP. For GradNorm, as suggested, we provide the results as follows. For reference, we also paste the r... | [
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nips_2022_wsnMW0c_Au | Non-convex online learning via algorithmic equivalence | We study an algorithmic equivalence technique between non-convex gradient descent and convex mirror descent. We start by looking at a harder problem of regret minimization in online non-convex optimization. We show that under certain geometric and smoothness conditions, online gradient descent applied to non-convex f... | Accept | The main result of the paper is on establishing an approximate equivalence between online gradient descent (OGD) on non-convex losses with online mirror descent (OMD) on convex reparametrizations of the losses. In a previous result by Amid and Warmuth, which applies to the the continuous-time setting, we have exact con... | train | [
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" I have checked the comments of Reviewer x2pL and Reviewer NwoT. As they bring up some issues that I didn't raise, e.g., the issue of G raised by Reviewer NwoT, I tend to keep the score as is. \n\nIn my opinion, the title of the paper sounds grandiose, but the result (a bit) falls short of it. ",
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nips_2022_ePZsWeGJXyp | VICRegL: Self-Supervised Learning of Local Visual Features | Most recent self-supervised methods for learning image representations focus on either producing a global feature with invariance properties, or producing a set of local features. The former works best for classification tasks while the latter is best for detection and segmentation tasks. This paper explores the fundam... | Accept | This paper proposes to extend the existing VICReg objective to the local features for obtaining good performances on both image-level and dense prediction tasks. In specific, while the global features are obtained by an average pooling on the output feature maps, the local pairs are determined by both of the feature di... | train | [
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nips_2022_C0VKVmhlKgb | Bayesian Clustering of Neural Spiking Activity Using a Mixture of Dynamic Poisson Factor Analyzers | Modern neural recording techniques allow neuroscientists to observe the spiking activity of many neurons simultaneously. Although previous work has illustrated how activity within and between known populations of neurons can be summarized by low-dimensional latent vectors, in many cases what determines a unique populat... | Accept | The authors present a mixture of dynamic Poisson factor analyzers (sometimes called Poisson linear dynamical systems) model. The model itself is not especially novel (it seems closely related to Poisson switching linear dynamical systems) but the authors make up for it with a well-developed approach to Bayesian inferen... | train | [
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nips_2022_hX5Ia-ION8Y | MCVD - Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation | Video prediction is a challenging task. The quality of video frames from current state-of-the-art (SOTA) generative models tends to be poor and generalization beyond the training data is difficult.
Furthermore, existing prediction frameworks are typically not capable of simultaneously handling other video-related task... | Accept | The paper proposes the use of diffusion model for masked video modeling and shows promising results in video generation and completion. All of the reviewers agree that the paper is a good fit for publication at NeurIPS. I appreciate that the authors engaged with the reviewers and improved the paper! | val | [
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nips_2022_IFXTZERXdM7 | Solving Quantitative Reasoning Problems with Language Models | Language models have achieved remarkable performance on a wide range of tasks that require natural language understanding. Nevertheless, state-of-the-art models have generally struggled with tasks that require quantitative reasoning, such as solving mathematics, science, and engineering questions at the college level. ... | Accept | This is a very strong solid work that extracts equations from scientific papers and show strong performance in mathematical reasoning. | train | [
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nips_2022_9t24EBSlZOa | Attention-based Neural Cellular Automata | Recent extensions of Cellular Automata (CA) have incorporated key ideas from modern deep learning, dramatically extending their capabilities and catalyzing a new family of Neural Cellular Automata (NCA) techniques. Inspired by Transformer-based architectures, our work presents a new class of _attention-based_ NCAs form... | Accept | As summarized by reviewer GAh5, this paper proposes a novel combination of vision transformers and Neural Cellular Automata (NCAs), and uses them to create denoising autoencoders. An NCA is essentially a cellular automaton with the node updates being performed by a neural net. The ViTCA proposed here adds to this atten... | train | [
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nips_2022_v9Wjc2OWjz | The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation? | We consider the problem of estimating a rank-$1$ signal corrupted by structured rotationally invariant noise, and address the following question: \emph{how well do inference algorithms perform when the noise statistics is unknown and hence Gaussian noise is assumed?} While the matched Bayes-optimal setting with unstruc... | Accept | This paper studies precise high-dimensional asymptotics in a simple low-rank matrix estimation problem. When there exists a distributional mismatch between the true noise distribution and the Gaussian noise assumption imposed to run the AMP algorithm, the authors observe, and formally quantify, the performance gap bet... | train | [
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nips_2022_48TmED6BvGZ | Biological Learning of Irreducible Representations of Commuting Transformations | A longstanding challenge in neuroscience is to understand neural mechanisms underlying the brain’s remarkable ability to learn and detect transformations of objects due to motion. Translations and rotations of images can be viewed as orthogonal transformations in the space of pixel intensity vectors. Every orthogonal t... | Accept | This manuscript presents novel biologically plausible algorithms for learning representations for Lie groups. The derivation of the algorithms and the networks are based on previously studied biologically plausible networks. Although there are some limitations, the reviewers agree that this work is sound, clearly prese... | train | [
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nips_2022_Bct2f8fRd8S | The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning | Does prompting a large language model (LLM) like GPT-3 with explanations improve in-context learning? We study this question on two NLP tasks that involve reasoning over text, namely question answering and natural language inference. We test the performance of four LLMs on three textual reasoning datasets using prompts... | Accept | The authors perform an analysis that suggests that explanations may not provide reliable signal in few-shot in-context learning, showing that adding explanations yields only minimal gains over raw in-context learning. They then develop an approach to approximate the reliability of predictions automatically using these ... | train | [
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" I think the new framing of the paper \"The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning\" makes more sense. Though the mathematical reasoning chain of tho... | [
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nips_2022_-vXEN5rIABY | Inductive Logical Query Answering in Knowledge Graphs | Formulating and answering logical queries is a standard communication interface for knowledge graphs (KGs).
Alleviating the notorious incompleteness of real-world KGs, neural methods achieved impressive results in link prediction and complex query answering tasks by learning representations of entities, relations, and... | Accept | It is very hard to make a final decision on this paper; the scores are: 4,6,8, and 5. The research problem raised in this paper is interesting and worth further study.
However, reviewers have raised some concerns about the experimental resuls. In the original paper, they did not compare with any external baselines. In... | val | [
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nips_2022_4v7PSPp-TAe | Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games | We study the problem of finding the Nash equilibrium in a two-player zero-sum Markov game. Due to its formulation as a minimax optimization program, a natural approach to solve the problem is to perform gradient descent/ascent with respect to each player in an alternating fashion. However, due to the non-convexity/non-... | Accept | The paper studies the problem of finding the Nash equilibrium of a two-player zero-sum Markov game. Despite nonconvexity, this min-max optimization satisfies the PL condition and hence it is "easy" to solve. The authors rigorously studied the iteration complexity of this problem. The reviewers found the paper well orga... | train | [
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nips_2022_UqA1mcOxiq | Posted Pricing and Dynamic Prior-independent Mechanisms with Value Maximizers | We study posted price auctions and dynamic prior-independent mechanisms for (ROI-constrained) value maximizers. In contrast to classic (quasi-linear) utility maximizers, these agents aim to maximize their total value subject to a minimum ratio of value per unit of payment made. When personalized posted prices are allow... | Accept | This paper got uniformly positive reviews. That said, reading into the actual text of the reviews, it is evident that the results are not as strong as one might like. The biggest limitation is that the result rely heavily on personalized pricing for reducing the problem to one of utility maximization for utility maximi... | train | [
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" Thanks for the response. Perhaps I was unclear, but it seems to me that the proof of Proposition 7 makes use of the fact that ROI = 1. My question was (1) is this necessary and (2) does the 1/4 b... | [
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nips_2022_pAq8iDy00Oa | Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel | It is challenging to guide neural network (NN) learning with prior knowledge. In contrast, many known properties, such as spatial smoothness or seasonality, are straightforward to model by choosing an appropriate kernel in a Gaussian process (GP). Many deep learning applications could be enhanced by modeling such known... | Reject | The submission considers fusing prior knowledge into neural networks by *modulating* the learnt features. The modulation is either additive or multiplicative using another set of features outputted by a kernel-based mapping with linear/periodic kernels. The method is akin to using composite kernels (or combining kernel... | train | [
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nips_2022_tadPkBL2gHa | Recruitment Strategies That Take a Chance | In academic recruitment settings, including faculty hiring and PhD admissions, committees aim to maximize the overall quality of recruited candidates, but there is uncertainty about whether a candidate would accept an offer if given one. Previous work has considered algorithms that make offers sequentially and are subj... | Accept | Thank the authors for their submission.
The paper studies a hiring problem, arguably a more realistic formulation compared to prior work. An agent has access to a pool of candidates, each with its own value and probability of accepting a hiring offer. The goal is to select a batch of candidates as to maximize the cumu... | train | [
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" ### Questions\n\n> In all experiments presented, xGreedy seems to be equivalent to, if not better than, LowValueL1+. What is the reason behind this result? Is it because LowValueL1+ was run with $\\tau=0$? If so, how sensitive is LowValueL1+ to the choice of $\\tau$ and... | [
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nips_2022_XByg4kotW5 | When does return-conditioned supervised learning work for offline reinforcement learning? | Several recent works have proposed a class of algorithms for the offline reinforcement learning (RL) problem that we will refer to as return-conditioned supervised learning (RCSL). RCSL algorithms learn the distribution of actions conditioned on both the state and the return of the trajectory. Then they define a policy... | Accept | This paper theoretically analyzes a new popular class of RL algorithms, referred to as Return-Conditioned Supervised Learning (RCSL). The paper theoretically shows that this class of method requires stronger assumptions than standard DP-based approaches for learning the optimal policy. The paper shows that RCSL method ... | train | [
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nips_2022_G2kkDEujOw | Detection and Localization of Changes in Conditional Distributions | We study the change point problem that considers alterations in the conditional distribution of an inferential target on a set of covariates. This paired data scenario is in contrast to the standard setting where a sequentially observed variable is analyzed for potential changes in the marginal distribution. We propose... | Accept | This manuscript enjoyed universal recommendation of acceptance from the reviewers after the initial review phase. The reviewers did note several minor issues in these initial reviews, many of which were resolved by insightful responses from the authors. I encourage the authors to edit the manuscript to reflect the insi... | val | [
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nips_2022_8cC2JeUyz9 | Inference and Sampling for Archimax Copulas | Understanding multivariate dependencies in both the bulk and the tails of a distribution is an important problem for many applications, such as ensuring algorithms are robust to observations that are infrequent but have devastating effects. Archimax copulas are a family of distributions endowed with a precise represent... | Accept | The paper proposes a new method for inference and for sampling in archimax copulas. All the reviewers praised the soundess and clarity of the paper, the novetly of the ideas and the experimental results. Copulas might not be one of the core topics of the NeuRIPS community, but the reviewers pointed out that:
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" Thank you for your careful consideration and response to my comments. I have accordingly raised my score and I recommend acceptance of this paper. ",
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nips_2022_4RC_vI0OgIS | Online Deep Equilibrium Learning for Regularization by Denoising | Plug-and-Play Priors (PnP) and Regularization by Denoising (RED) are widely-used frameworks for solving imaging inverse problems by computing fixed-points of operators combining physical measurement models and learned image priors. While traditional PnP/RED formulations have focused on priors specified using image deno... | Accept | The paper proposes a learning method (specifically a deep equilibrium learning approach) for 'regularization by denoising', a plug-and-play method for solving inverse problems.
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" Thank you all again for reviewing our work. An additional thanks to those reviewers that have already read our responses and the area chair for managing the review of our paper. Let us know if there is anything else we can do to improve your evaluation of our work.",
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nips_2022_Ms6QZafNv01 | Optimal algorithms for group distributionally robust optimization and beyond | Distributionally robust optimization (DRO) can improve the robustness and fairness of learning methods. In this paper, we devise stochastic algorithms for a class of DRO problems including group DRO, subpopulation fairness, and empirical conditional value at risk (CVaR) optimization. Our new algorithms achieve faster c... | Reject | The main criticism raised by the reviewers was the unconvincing experiments. The reviewers generally liked the simplicity of the method presented the paper, but were unconvinced by the impact/utility of the results. Even though the paper focuses on the convex regime, the paper may benefit from some DL experiments. This... | val | [
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" I apologize for the late response. \n\n> We believe that our OCO approach is valuable because it yields an algorithm for a wide range of DRO problems in a very simple way. In fact, the previous work on group DRO [Sagawa et al. 2020] analyzed the convergence of their algorithm with more complicated results of conv... | [
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nips_2022_68YyraaeYmc | Exploring through Random Curiosity with General Value Functions | Efficient exploration in reinforcement learning is a challenging problem commonly addressed through intrinsic rewards. Recent prominent approaches are based on state novelty or variants of artificial curiosity. However, directly applying them to partially observable environments can be ineffective and lead to premature... | Accept | This paper proposes an intrinsic motivation method which by and large extends RND, aiming to service the needs of agents in longer horizon exploration problems. There was a bit of a spread of scores amongst reviewers, but based upon reading the extensive discussion, I am recommending acceptance it seems that on the bal... | train | [
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nips_2022_U14PKEu18bK | Unsupervised Multi-View Object Segmentation Using Radiance Field Propagation | We present radiance field propagation (RFP), a novel approach to segmenting objects in 3D during reconstruction given only unlabeled multi-view images of a scene. RFP is derived from emerging neural radiance field-based techniques, which jointly encodes semantics with appearance and geometry. The core of our method is ... | Accept | Reviewers are generally positive about the submission, and all recommend acceptance post rebuttal. They appreciate the new formulation and the strong results. The AC agrees and recommends acceptance. | val | [
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" Dear Reviewer GXm6,\n\nThank you for your response and appreciation of our approach. Please rest assured that we will stress our limitations in our final version.\n\nAnonymous a... | [
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nips_2022_sNcn-E3uPHA | Text Classification with Born's Rule | This paper presents a text classification algorithm inspired by the notion of superposition of states in quantum physics. By regarding text as a superposition of words, we derive the wave function of a document and we compute the transition probability of the document to a target class according to Born's rule. Two com... | Accept | This paper has 2 accepts (7) and 2 borderline accepts (5). The average is 6.
The modification of Reviewer Sqxh does not show in the system, but he stated as follows in our discussion “I tend to modify the score of this paper to five.”
This paper shows an algorithm that delivers outstanding text classification perf... | val | [
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nips_2022_8E8tgnYlmN | SIREN: Shaping Representations for Detecting Out-of-Distribution Objects | Detecting out-of-distribution (OOD) objects is indispensable for safely deploying object detectors in the wild. Although distance-based OOD detection methods have demonstrated promise in image classification, they remain largely unexplored in object-level OOD detection. This paper bridges the gap by proposing a distanc... | Accept | This work proposes a new unified distributional model to address out of distribution detection and improves over the state of the art.
While the approach shares notable similarities with other works in the domain, the idea of creating a unified distributional representation also at the intermediate features, especiall... | train | [
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nips_2022_t3X5yMI_4G2 | Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress | Learning tabula rasa, that is without any prior knowledge, is the prevalent workflow in reinforcement learning (RL) research. However, RL systems, when applied to large-scale settings, rarely operate tabula rasa. Such large-scale systems undergo multiple design or algorithmic changes during their development cycle and ... | Accept | This paper proposes a novel method for transferring prior policies across design and system changes to improve the sample efficiency of RL algorithms, which could ultimately help unlock RL for real-world use cases.
There was an active discussion across the reviewing process in which the authors managed to address the ... | train | [
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nips_2022_fcMd-tuWwiO | A sharp NMF result with applications in network modeling | Given an $n \times n$ non-negative rank-$K$ matrix $\Omega$ where $m$ eigenvalues are negative, when can we write $\Omega = Z P Z'$ for non-negative matrices $Z \in \mathbb{R}^{n, K}$ and $P \in \mathbb{R}^{K, K}$? While most existing works focused on the case of $m = 0$, our primary interest is on the case of gener... | Accept | This was a borderline paper, which fell just above the bar for acceptance. The reviewers felt the work was interesting and original, although perhaps the problem studied is a bit niche for NeurIPS. | train | [
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nips_2022_RP1CtZhEmR | Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN) | Generating multivariate time series is a promising approach for sharing sensitive data in many medical, financial, and IoT applications. A common type of multivariate time series originates from a single source such as the biometric measurements from a medical patient. This leads to complex dynamical patterns between i... | Accept | The authors propose GroupGAN which uses a separate generator and discriminator for generating each data channel and a central discriminator for accurately capturing the correlation structure between different channels. This is a borderline paper and there were extensive discussions among the reviewers about this paper.... | test | [
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" Thank you for your response. My concerns have been addressed. I think this is a good paper and a score of 7 is appropriate. My score remains the same.",
" Thank you for your thorough response.\n\nIn the final version of the paper, we will make sure to clarify any confusion caused by the use of the terms “noise”... | [
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nips_2022_dT0eNsO2YLu | Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks | We propose learnable polyphase sampling (LPS), a pair of learnable down/upsampling layers that enable truly shift-invariant and equivariant convolutional networks. LPS can be trained end-to-end from data and generalizes existing handcrafted downsampling layers. It is widely applicable as it can be integrated into any c... | Accept | The paper proposes an end-to-end learnable polyphase sampling and shows competitive performance and sufficient novelty. Major concerns of the reviewers seem to be addressed during the rebuttal and therefore it can be accepted. | train | [
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nips_2022_1wVBLK1Xuc | Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems | Long-term fairness is an important factor of consideration in designing and deploying learning-based decision systems in high-stake decision-making contexts. Recent work has proposed the use of Markov Decision Processes (MDPs) to formulate decision-making with long-term fairness requirements in dynamically changing env... | Accept | I recommend acceptance due to the positive opinions of the reviewers. This paper proposes a method relevant to fairness in RL, for enforcing constraints during policy optimization. The reviews judge the method (inspired by Lyapunov stability ideas) and its empirical evaluation as technically sound. Many initial points ... | train | [
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nips_2022_5yAmUvdXAve | Cluster and Aggregate: Face Recognition with Large Probe Set | Feature fusion plays a crucial role in unconstrained face recognition where inputs (probes) comprise of a set of $N$ low quality images whose individual qualities vary. Advances in attention and recurrent modules have led to feature fusion that can model the relationship among the images in the input set. However, atte... | Accept | The paper received 4 positive reviewers, and the reviewer increased/remained their scores after the rebuttal.
The paper pursues a useful direction of unconstrained face recognition. All reviewers agree that the results are impressive and the method has novelty. | val | [
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" The rebuttal has solved most of my former concerns and this paper is ok for acceptance based on the positive comments from other reviewers.",
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nips_2022_toleacrf7Hv | Parameter tuning and model selection in Optimal Transport with semi-dual Brenier formulation | Over the past few years, numerous computational models have been developed to solve Optimal Transport (OT) in a stochastic setting, where distributions are represented by samples and where the goal is to find the closest map to the ground truth OT map, unknown in practical settings. So far, no quantitative criterion ha... | Accept | While there were few misunderstandings, the rebuttal successfully convinced all the reviewers that the paper should be accepted. Please take into account the reviewers' comments in preparing the camera-ready, especially the ones concerning the clarity of the paper. | train | [
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" Hi, thank you for your answer. I checked the proposition carefully and I understand my mistake here. I increased the score to 6. ",
" Dear reviewer.\n\nIndeed, the semi-dual $J(f)$ diverges because the Legendre transform... | [
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nips_2022_6yuil2_tn9a | Handcrafted Backdoors in Deep Neural Networks | When machine learning training is outsourced to third parties, $backdoor$ $attacks$ become practical as the third party who trains the model may act maliciously to inject hidden behaviors into the otherwise accurate model. Until now, the mechanism to inject backdoors has been limited to $poisoning$. We argue that a sup... | Accept | This paper proposes a backdoor injection method that directly manipulates model weights after training. The backdoored method can achieve comparable clean accuracy and a high attack success rate through injecting and compromising handcrafted filters, increasing the separation in activations, and increasing the logit of... | val | [
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nips_2022_1xqE9fRZch5 | Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis | Recent self-supervised advances in medical computer vision exploit the global and local anatomical self-similarity for pretraining prior to downstream tasks such as segmentation. However, current methods assume i.i.d. image acquisition, which is invalid in clinical study designs where follow-up longitudinal scans track... | Accept | The paper proposes a self-supervised deep-learning framework for image-to-image translation tasks, such as segmentation, that accommodates and fully exploits longitudinal data. Specifically the method provides a mechanism to impose consistency in output across multiple points from the same individual and simple regular... | val | [
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nips_2022_o8vYKDWMnq1 | Understanding Deep Neural Function Approximation in Reinforcement Learning via $\epsilon$-Greedy Exploration | This paper provides a theoretical study of deep neural function approximation in reinforcement learning (RL) with the $\epsilon$-greedy exploration under the online setting. This problem setting is motivated by the successful deep Q-networks (DQN) framework that falls in this regime. In this work, we provide an initial... | Accept | This paper provides a theoretical study of the successful deep Q-networks framework under an online, episodic Markov decision process (MDP) model with T episodes. All reviewers and the AC believe this is a solid RL theory paper. | train | [
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nips_2022_177GzUAds8U | Compositional generalization through abstract representations in human and artificial neural networks | Humans have a remarkable ability to rapidly generalize to new tasks that is difficult to reproduce in artificial learning systems.
Compositionality has been proposed as a key mechanism supporting generalization in humans, but evidence of its neural implementation and impact on behavior is still scarce. Here we study th... | Accept | The manuscript presents a story about compositionality that ties together neuroscience and models; with a focus on how compositionality enables generalization in a task performed by humans undergoing fMRI. Reviewers were largely happy with the manuscript and authors thoroughly addressed the questions that reviewers had... | test | [
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nips_2022_ZwnPdpCw6d | Robust $\phi$-Divergence MDPs | In recent years, robust Markov decision processes (MDPs) have emerged as a prominent modeling framework for dynamic decision problems affected by uncertainty. In contrast to classical MDPs, which only account for stochasticity by modeling the dynamics through a stochastic process with a known transition kernel, robust ... | Accept | This is a somewhat borderline paper. The reviewers were unanimously positive, but they all had concerns. In reading through the concerns and responses, it seems that many (though perhaps not all) of the concerns could be addressed with additional references and some expository modifications.
If the paper makes the fi... | train | [
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nips_2022_PCZfDUH8fIn | The price of unfairness in linear bandits with biased feedback | In this paper, we study the problem of fair sequential decision making with biased linear bandit feedback. At each round, a player selects an action described by a covariate and by a sensitive attribute. The perceived reward is a linear combination of the covariates of the chosen action, but the player only observes a ... | Accept | The authors study a linear bandit problem with biased feedback, develop an algorithm and bound the corresponding regret. The bandit problem they study is meaningful and highly relevant. I therefore recommend to accept the paper. | train | [
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" Thanks for the response. Please add these details to the paper, so that the paper is clearly placed in the fairness literature.",
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nips_2022_Yo0s4qp_UMR | Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs | Collections of probability distributions arise in a variety of statistical applications ranging from user activity pattern analysis to brain connectomics. In practice these distributions are represented by histograms over diverse domain types including finite intervals, circles, cylinders, spheres, other manifolds, and... | Reject | In this paper, the authors propose the intrinsic sliced Wasserstein distances and a Hypothesis testing framework for the proposed measure. The idea of using eigenfunctions and eigenvalues for sliced Wasserstein is interesting. The authors addressed a part of the concerns raised by the reviewers. However, the advantage ... | train | [
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nips_2022_oQIJsMlyaW_ | SInGE: Sparsity via Integrated Gradients Estimation of Neuron Relevance | The leap in performance in state-of-the-art computer vision methods is attributed to the development of deep neural networks. However it often comes at a computational price which may hinder their deployment. To alleviate this limitation, structured pruning is a well known technique which consists in removing channels,... | Accept | Novel pruning method based on integrated gradients. Reviewers agreed that the method is well-motivated and that the comparisons showcase the potential of this method. There are some concerns regarding fairness of the comparisons in terms of flops and parameter count. I believe some of the rebuttal answers from the auth... | train | [
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nips_2022_SYdg8tcFgdG | Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games | Imperfect-Information Extensive-Form Games (IIEFGs) is a prevalent model for real-world games involving imperfect information and sequential plays. The Extensive-Form Correlated Equilibrium (EFCE) has been proposed as a natural solution concept for multi-player general-sum IIEFGs. However, existing algorithms for findi... | Accept | Reviews on this paper are uniformly positive, and all reviewers feel that it is
an interesting set of results. One minor criticism is that some reviewers felt the presentation
could be improved, and the authors should try to address this for the camera ready.
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" Thanks for your response. I don't have any additional questions. It might make sense to explicitly mention the two weaknesses with the current approach (difficulties in achieving model-freeness and adversarial bandit feedback).",
" Apologies for the late reply.\n\nIn the original EFCE paper by Von Stengel, devi... | [
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nips_2022_a3ymtHbL5p5 | In Differential Privacy, There is Truth: on Vote-Histogram Leakage in Ensemble Private Learning | When learning from sensitive data, care must be taken to ensure that training algorithms address privacy concerns. The canonical Private Aggregation of Teacher Ensembles, or PATE, computes output labels by aggregating the predictions of a (possibly distributed) collection of teacher models via a voting mechanism. The m... | Accept | Although the fact that DP does not protect against population statistics is a widely known fact, the paper weaves this together with PATE (which relies on DP statistics) to demonstrate the danger of mis interpreting the protection guarantees provided by DP. This is a point worth discussing among the privacy and securit... | val | [
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" Thank you for your response. To clarify our response to point 7: the reviewer is right that if the ... | [
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nips_2022_3vmKQUctNy | Washing The Unwashable : On The (Im)possibility of Fairwashing Detection | The use of black-box models (e.g., deep neural networks) in high-stakes decision-making systems, whose internal logic is complex, raises the need for providing explanations about their decisions. Model explanation techniques mitigate this problem by generating an interpretable and high-fidelity surrogate model (e.g., a... | Accept | The reviewers were split about this paper: on one hand they appreciated the motivation and the comprehensive experiments in the paper, on the other they were concerned about the clarity of the paper, even worried about a potential flaw. I have decided to vote to accept given the clear and convincing author response. I ... | val | [
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" Dear reviewer vBkK,\n\nThank you very much for the very positive feedback and insightful suggestions! We would be happy to answer any further questions you may have before the response period ends today.\n\nWarm regards,\nPaper3526 Authors",
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nips_2022_yts7fLpWY9G | Graph Neural Networks with Adaptive Readouts | An effective aggregation of node features into a graph-level representation via readout functions is an essential step in numerous learning tasks involving graph neural networks. Typically, readouts are simple and non-adaptive functions designed such that the resulting hypothesis space is permutation invariant. Prior w... | Accept | The paper proposes the use of an adaptive readout function in GNNs together with extensive empirical work to support it. The reviewers all found the paper interesting and are generally in favor of accepting it (with one marking strong accept with high confidence). Therefore, I recommend the paper be accepted, and encou... | test | [
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nips_2022__N4k45mtnuq | Approximate Euclidean lengths and distances beyond Johnson-Lindenstrauss | A classical result of Johnson and Lindenstrauss states that a set of $n$ high dimensional data points can be projected down to $O(\log n/\epsilon^2)$ dimensions such that the square of their pairwise distances is preserved up to a small distortion $\epsilon\in(0,1)$. It has been proved that the JL lemma is optimal for ... | Accept | All reviews for this paper were positive, albeit with a varying level of enthusiasm. Reviewers found the problem, the results (both theoretical and experimental) and the techniques (very) interesting. The main concerns were whether the paper is a good fit for the conference (given that dimensionality reduction is more ... | val | [
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nips_2022_eF_Mx-3Sm92 | Change-point Detection for Sparse and Dense Functional Data in General Dimensions | We study the problem of change-point detection and localisation for functional data sequentially observed on a general $d$-dimensional space, where we allow the functional curves to be either sparsely or densely sampled. Data of this form naturally arise in a wide range of applications such as biology, neuroscience, cl... | Accept | The paper studies change-point detection and localization for functional data, which is an interesting and timely topic. I agree with some reviewers that the paper might be a better fit with the traditional statistical venue. The authors have done a great job in the rebuttal phase in addressing reviewers’ comments. I b... | val | [
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nips_2022_ADfBF9PoTvw | Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients | Minimizing the inclusive Kullback-Leibler (KL) divergence with stochastic gradient descent (SGD) is challenging since its gradient is defined as an integral over the posterior. Recently, multiple methods have been proposed to run SGD with biased gradient estimates obtained from a Markov chain. This paper provides the f... | Accept | All reviewers recommend accepting the paper. If the authors want to increase the impact of their work, a demonstration on a large-scale problem would help a lot. | test | [
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" Thank you for addressing my questions and suggestions. These answers are helpful and addressed my concerns about mixing rate. The updated Figures 1, 2 are also more informative and highlight the benefits of the proposed approach. The... | [
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nips_2022_rG7HZZtIc- | D^2NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video | Given a monocular video, segmenting and decoupling dynamic objects while recovering the static environment is a widely studied problem in machine intelligence. Existing solutions usually approach this problem in the image domain, limiting their performance and understanding of the environment. We introduce Decoupled Dy... | Accept | This paper attacks an interesting problem with NERF, decoupling moving objects, including their shadows, from the static background. All four reviewers recommend accepting the paper, and the weaknesses identified did not detract from substantive contributions. Therefore I am accepting this paper. | train | [
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nips_2022_pbILUUf_hBN | A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs | We consider online learning with feedback graphs, a sequential decision-making framework where the learner's feedback is determined by a directed graph over the action set. We present a computationally-efficient algorithm for learning in this framework that simultaneously achieves near-optimal regret bounds in both sto... | Accept | The reviewers came to consensus that this paper makes a good progress on the online learning with feedback graphs. I agree with these opinions and please polish the manuscript so that the minor concerns raised by the reviewers become clear in the final version. | train | [
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nips_2022_VpHFHz57fT | Improved Imaging by Invex Regularizers with Global Optima Guarantees | Image reconstruction enhanced by regularizers, e.g., to enforce sparsity, low rank or smoothness priors on images, has many successful applications in vision tasks such as computer photography, biomedical and spectral imaging. It has been well accepted that non-convex regularizers normally perform better than convex on... | Accept | This paper addresses image reconstruction problems exploiting invex regularizers (which are not necessarily convex). For many modern signal processing applications, invexity of the cost is proved. Many examples are considered, and an extensive comparison with state-of-the art methods are provided in an application sect... | train | [
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" We are glad to know that the reviewer found our rebuttal helpful. Thank you for the comments, and the support reconsidering the decision. Here we address your new questions.\n# Questions\n- **Q1.** We thank you for the suggested plot. We will include this new comparative plot between invex and convex regularizers... | [
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nips_2022_K4W92FUXSF9 | Random Normalization Aggregation for Adversarial Defense | The vulnerability of deep neural networks has been widely found in various models as well as tasks where slight perturbations on the inputs could lead to incorrect predictions. These perturbed inputs are known as adversarial examples and one of the intriguing properties of them is Adversarial Transfersability, i.e. the... | Accept | This paper intrdouces the relation between normalizations and adversarial transferability, and proposes a method using random normalization aggregation for enhancing adversarial robustness.
Three reviewers agreed with the interesing idea, thorough expreiments, theroetical analysis, and the effectivess, so they gave ac... | val | [
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" We sincerely appreciate all the reviewers for their valuable comments and suggestions. We have revised the manuscript according to the comments. The updated contents are highlighted by blue text in the revised manuscript.",
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nips_2022_8hs7qlWcnGs | ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery | In computer-aided drug discovery (CADD), virtual screening (VS) is used for comparing a library of compounds against known active ligands to identify the drug candidates that are most likely to bind to a molecular target. Most VS methods to date have focused on using canonical compound representations (e.g., SMILES str... | Accept | The reviewers mostly liked the paper. They mentioned the sound theoretical foundations, stability, and strong empirical performance. The rebuttal was able to convince most reviewers that the paper should be accepted for NeurIPS. | train | [
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nips_2022_9cU2iW3bz0 | Score-Based Diffusion meets Annealed Importance Sampling | More than twenty years after its introduction, Annealed Importance Sampling (AIS) remains one of the most effective methods for marginal likelihood estimation. It relies on a sequence of distributions interpolating between a tractable initial distribution and the target distribution of interest which we simulate from a... | Accept | The submission presents a novel and interesting method using recent advances in score-based diffusion to improve the recently proposed differentiable AIS log marginal likelihood estimates. The experiments clearly show the benefit of using Monte Carlo diffusion. The writing is clear and of high quality. For these reason... | val | [
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nips_2022_0oQv1Ftt_gK | Rethinking Counterfactual Explanations as Local and Regional Counterfactual Policies | Among the challenges not yet resolved for Counterfactual Explanations (CE), there are stability, synthesis of the various CE and the lack of plausibility/sparsity guarantees. From a more practical point of view, recent studies show that the prescribed counterfactual recourses are often not implemented exactly by the ... | Reject | This paper highlights a series of limitations of existing methods in the literature on algorithmic recourse (e.g., recourses are not implemented exactly and are often noisy), and posits new definitions for Local and Regional Counterfactual Rules and proposes a novel algorithmic framework to learn them. All the reviewer... | train | [
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nips_2022_HuiLIB6EaOk | VTC-LFC: Vision Transformer Compression with Low-Frequency Components | Although Vision transformers (ViTs) have recently dominated many vision tasks, deploying ViT models on resource-limited devices remains a challenging problem. To address such a challenge, several methods have been proposed to compress ViTs. Most of them borrow experience in convolutional neural networks (CNNs) and main... | Accept | The authors present a method to improve ViT efficiency by pruning channels and tokens using a selection mechanism that emphasizes low spatial frequency information. In particular they propose two measures: Low Frequency Sensitivity (LFS) and Low Frequency Energy (LFE).
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nips_2022_YiFQqYAk1xH | Dynamic Fair Division with Partial Information | We consider the fundamental problem of fairly and efficiently allocating $T$ indivisible items among $n$ agents with additive preferences. The items become available over a sequence of rounds, and every item must be allocated immediately and irrevocably before the next one arrives. Previous work shows that when the age... | Accept | All reviewers are positive about the paper and found that the problem of sequential/online fair allocation of indivisible items interesting (and relatively new), and the theoretical results significant and sometimes surprising. Technically, the paper also has a "bandit" flavor, which makes it a good fit for NeurIPS. | train | [
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nips_2022_78T4K99jvbE | Set-based Meta-Interpolation for Few-Task Meta-Learning | Meta-learning approaches enable machine learning systems to adapt to new tasks given few examples by leveraging knowledge from related tasks. However, a large number of meta-training tasks are still required for generalization to unseen tasks during meta-testing, which introduces a critical bottleneck for real-world p... | Accept | This paper uses a set transformer to create new tasks at meta training time when the amount of data for meta training is scarce. This approach seems to be highly effective and will make a worthwhile contribution to the few-shot learning toolbox. Many of the reviewer concerns were addressed through additional ablations ... | train | [
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nips_2022_WyiM4lDJOcK | How To Design Stable Machine Learned Solvers For Scalar Hyperbolic PDEs | Machine learned partial differential equation (PDE) solvers trade the robustness of classical numerical methods for potential gains in accuracy and/or speed. A key challenge for machine learned PDE solvers is to maintain physical constraints that will improve robustness while still retaining the flexibility that allows... | Reject | Thank you for your submission to NeurIPS. All four reviewers authors are enthusiastic of this work, though three of the four reviewers had major concerns with the actual submission. In response, the authors carried out a major revision within a very short turn-around, completely rewriting most of the paper. All four re... | train | [
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nips_2022_pl279jU4GOu | Convergence beyond the over-parameterized regime using Rayleigh quotients | In this paper, we present a new strategy to prove the convergence of Deep Learning architectures to a zero training (or even testing) loss by gradient flow. Our analysis is centered on the notion of Rayleigh quotients in order to prove Kurdyka-Lojasiewicz inequalities for a broader set of neural network architectures a... | Accept | This paper proposes a new method for proving the convergence of gradient flow to zero loss by leveraging Rayleigh quotients to establish KL inequalities, a strategy that can apply even without overparameterization. The reviewers found the paper to be well written and generally easy to follow, despite a few concerns abo... | train | [
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nips_2022_M_WuaKoaEfQ | A Quadrature Rule combining Control Variates and Adaptive Importance Sampling | Driven by several successful applications such as in stochastic gradient descent or in Bayesian computation, control variates have become a major tool for Monte Carlo integration. However, standard methods do not allow the distribution of the particles to evolve during the algorithm, as is the case in sequential simul... | Accept | This paper proposes a novel method to perform Monte Carlo integration combining control variates and annealed importance sampling. All reviewers agreed the algorithm was of interest, the theoretical evidence was strong, and the experimental results were sufficiently convincing, so there was a consensus on acceptance.
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nips_2022_s0AgNH86p8 | TransBoost: Improving the Best ImageNet Performance using Deep Transduction | This paper deals with deep transductive learning, and proposes TransBoost as a procedure for fine-tuning any deep neural model to improve its performance on any (unlabeled) test set provided at training time. TransBoost is inspired by a large margin principle and is efficient and simple to use. Our method significantly... | Accept | This paper initially received mixed opinions. After intensive author-reviewer and reviewer-reviewer discussions, all reviewers converged and recommended acceptance. AC recommends accepting the paper. | train | [
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nips_2022_lHj-q9BSRjF | Data Distributional Properties Drive Emergent In-Context Learning in Transformers | Large transformer-based models are able to perform in-context few-shot learning, without being explicitly trained for it. This observation raises the question: what aspects of the training regime lead to this emergent behavior? Here, we show that this behavior is driven by the distributions of the training data itself.... | Accept | This paper poses and analyses an interesting question -- do statistical properties of the training data affect emergent behavior (e.g. in-context learning) in Transformers? The study is novel since it probes the properties of the data itself, as opposed to most existing work that has studied the effect of model archite... | train | [
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nips_2022_WUMH5xloWn | Automatic differentiation of nonsmooth iterative algorithms | Differentiation along algorithms, i.e., piggyback propagation of derivatives, is now routinely used to differentiate iterative solvers in differentiable programming. Asymptotics is well understood for many smooth problems but the nondifferentiable case is hardly considered. Is there a limiting object for nonsmooth pigg... | Accept | The reviewers agreed that the paper has solid and novel technical contributions. Nevertheless, please consider elaborating more on the background of the techniques used in the revision, so that the paper is more self-contained. | train | [
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nips_2022_QW98XBAqNRa | Truncated proposals for scalable and hassle-free simulation-based inference | Simulation-based inference (SBI) solves statistical inverse problems by repeatedly running a stochastic simulator and inferring posterior distributions from model-simulations. To improve simulation efficiency, several inference methods take a sequential approach and iteratively adapt the proposal distributions from whi... | Accept | This paper received generally positive reviews, with one reviewer originally weakly backing rejection but ultimately backing acceptance after substantial discussions, and the other three confidently backing acceptance from the off. Based on the reviewer's comments and my own assessments, I see the positives and negati... | train | [
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nips_2022_M12autRxeeS | Extracting computational mechanisms from neural data using low-rank RNNs | An influential framework within systems neuroscience posits that neural computations can be understood in terms of low-dimensional dynamics in recurrent circuits. A number of methods have thus been developed to extract latent dynamical systems from neural recordings, but inferring models that are both predictive and in... | Accept | Building on recent theoretical work on the dynamics of low-rank recurrent neural networks, the authors present a method called LINT for learning low-rank network models directly from data. As the reviewers point out, from a purely technical perspective, the idea is straightforward: simply optimize a low-rank parameteri... | train | [
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" I thank the authors for their thoughtful responses and revision. I’m still in favor of this work, although I think my main point (3) has not really been addressed. That is, the K-back task is perhaps helpful in further confirming that the authors’ method works well. But it doesn’t give me more reason to believe t... | [
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nips_2022_nC8VC8gVGPo | Training Spiking Neural Networks with Local Tandem Learning | Spiking neural networks (SNNs) are shown to be more biologically plausible and energy efficient over their predecessors. However, there is a lack of an efficient and generalized training method for deep SNNs, especially for deployment on analog computing substrates. In this paper, we put forward a generalized learning ... | Accept | This paper proposes a novel method of training spiking neural networks (SNNs) by matching the intermediate feature representations of SNNs with pre-trained ANNs. The method is on-chip and local, allowing SNNs to be learned directly on neuromorphic hardware.
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nips_2022_OxHn1Yz_Kl3 | Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness | One common task in many data sciences applications is to answer questions about the effect of new interventions, like: `what would happen to $Y$ if we make $X$ equal to $x$ while observing covariates $Z=z$?'. Formally, this is known as conditional effect identification, where the goal is to determine whether a post-int... | Accept | The reviewers are all in agreeement that the paper constitutes a fundamental advance in the theory of causal inference. The authors responded to the reviewers' remaining questions in a detailed way, and there is no further issue with the paper being accepted. | val | [
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nips_2022_JO9o3DgV9l2 | Shield Decentralization for Safe Multi-Agent Reinforcement Learning | Learning safe solutions is an important but challenging problem in multi-agent reinforcement learning (MARL). Shielded reinforcement learning is one approach for preventing agents from choosing unsafe actions. Current shielded reinforcement learning methods for MARL make strong assumptions about communication and full ... | Accept | It is agreed among reviewers that the paper should be accepted. Hope the authors can address the comments from the reviewers in the final version as promised. | train | [
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nips_2022_ONB4RdP2GX | Hardness in Markov Decision Processes: Theory and Practice | Meticulously analysing the empirical strengths and weaknesses of reinforcement learning methods in hard (challenging) environments is essential to inspire innovations and assess progress in the field. In tabular reinforcement learning, there is no well-established standard selection of environments to conduct such anal... | Accept | The reviewers' opinions are quite consistent towards a weak accept.
I'm not confident with the big title "Hardness in Markov Decision Processes: Theory and Practice". This paper is more like a survey + benchmark review instead of a research article. Neither the theory part or the practice part is novel enough as a res... | val | [
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nips_2022_eXggxYNbQi | On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity | Most complex machine learning and modelling techniques are prone to over-fitting and may subsequently generalise poorly to future data. Artificial neural networks are no different in this regard and, despite having a level of implicit regularisation when trained with gradient descent, often require the aid of explicit ... | Accept | This paper is controversial among the reviewers. On the positive side, reviewers like the novelty of the concept, the derivations and the clear presentation. The negative review wonders why the proposed method performs much better than dropout, similarity to Lipschitz constraints, and whether proper early stopping was ... | train | [
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nips_2022_U_hOegGGglw | A Closer Look at Prototype Classifier for Few-shot Image Classification | The prototypical network is a prototype classifier based on meta-learning and is widely used for few-shot learning because it classifies unseen examples by constructing class-specific prototypes without adjusting hyper-parameters during meta-testing.
Interestingly, recent research has attracted a lot of attention, show... | Accept | It has been shown that linear classifier heads on top of pre-trained models can outperform meta-learning approaches. However, this is less adaptable than prototypical classifier heads and requires retraining with each new set of classes. This paper theoretically investigates the generalization of prototypical classifie... | val | [
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nips_2022__2-r5UurHp | ALIFE: Adaptive Logit Regularizer and Feature Replay for Incremental Semantic Segmentation | We address the problem of incremental semantic segmentation (ISS) recognizing novel object/stuff categories continually without forgetting previous ones that have been learned. The catastrophic forgetting problem is particularly severe in ISS, since pixel-level ground-truth labels are available only for the novel categ... | Accept | This work deals with incremental semantic segmentation. The authors propose a three-step incremental learning approach. They provide an in-depth analysis of the probability calibration methods widely used for the ISS, and introduce an interesting proposal for incrementally adapting the memorized features using global a... | train | [
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nips_2022_RQ385yD9dqR | When are Local Queries Useful for Robust Learning? | Distributional assumptions have been shown to be necessary for the robust learnability of concept classes when considering the exact-in-the-ball robust risk and access to random examples by Gourdeau et al. (2019). In this paper, we study learning models where the learner is given more power through the use of local que... | Accept | Solid contribution to NTK theory | train | [
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" We reply to the remaining concerns below. Please let us know if you have any questions. \n\n**For the LEQ**, if one believes that the exact-in-the-ball notion of robust risk is worth investigating, then our lower bound for the LMQ model and our impossibility result for $\\lambda<\\rho$-LEQ give ample justificatio... | [
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nips_2022_vGQiU5sqUe3 | Contrastive Learning as Goal-Conditioned Reinforcement Learning | In reinforcement learning (RL), it is easier to solve a task if given a good representation. While deep RL should automatically acquire such good representations, prior work often finds that learning representations in an end-to-end fashion is unstable and instead equip RL algorithms with additional representation lear... | Accept | How to design RL algorithms that directly acquire good representations? This paper gives an answer that contrastive representation learning can be cast as a goal-conditioned RL using the inner product of learned representations.
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" I would like to thank the authors for their detailed response and for running the additional experiments. I find the additional experiment and video with the hand-tracking camera interesting, thank you for running that.",
" I appreciate the authors taking the time to respond to my comments and making appropriat... | [
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nips_2022_fSfcEYQP_qc | A Neural Corpus Indexer for Document Retrieval | Current state-of-the-art document retrieval solutions mainly follow an index-retrieve paradigm, where the index is hard to be directly optimized for the final retrieval target. In this paper, we aim to show that an end-to-end deep neural network unifying training and indexing stages can significantly improve the recall... | Accept | This paper proposes a new framework for neural IR: given query, directly predict a document ID. The document IDs are obtained by hierarchical clustering of documents beforehand. This is a novel formulation of the problem, and is very distinct from current two-stage methods that have a high-recall sparse retrieval stag... | train | [
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" Thanks for the answers. Most of my questions are answered and I support for the acceptance of the paper.",
" Dear reviewers, \n\nThank you for taking time in reading our paper and providing valuable comments. We briefly address common questions here. \n \n1. Regarding the concern that query generation dominate... | [
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nips_2022_USoYIT4IQz | Invertible Monotone Operators for Normalizing Flows | Normalizing flows model probability distributions by learning invertible transformations that transfer a simple distribution into complex distributions. Since the architecture of ResNet-based normalizing flows is more flexible than that of coupling-based models, ResNet-based normalizing flows have been widely studied i... | Accept | The paper proposes a new type of ResNet-based Normalizing Flows that, unlike previous versions of these flows, do not require the Lipschitz constant of each layer to be less than 1. The authors use monotone operators, which they show to be strictly more expressive and propose a new activation function called Concatenat... | train | [
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" Thank you for your reply. I'm happy with all the answers.\n\nI would suggest to incorporate the reply to Q4 into the paper (i.e. that the notation is overloaded and that $F(x) = \\\\{y \\; | \\; (x, y) \\in F\\\\}$). This would help to avoid confusing authors without a background in monotone operator theory.\n\nA... | [
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nips_2022_Ya9lATuQ3gg | Large-Scale Retrieval for Reinforcement Learning | Effective decision making involves flexibly relating past experiences and relevant contextual information to a novel situation. In deep reinforcement learning (RL), the dominant paradigm is for an agent to amortise information that helps decision-making into its network weights via gradient descent on training losses. ... | Accept | This paper uses nearest neighbor methods to retrieve and exploit information from similar games during planning, whilst playing the game of go (although the method is extensible to other environments which support muzero-style agents). The reviewers found this approach interesting and ultimately worth publishing, altho... | val | [
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nips_2022_88_wNI6ZBDZ | Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach | Deep neural networks often suffer from poor generalization caused by complex and non-convex loss landscapes. One of the popular solutions is Sharpness-Aware Minimization (SAM), which smooths the loss landscape via minimizing the maximized change of training loss when adding a perturbation to the weight. However, we fin... | Accept | This paper proposes a new scheme to improve computational efficiency of the SAM optimizer. The original SAM requires to asses the loss value at a perturbed point. The perturbation lives in the full parameter space. This paper shows that computing the perturbation in every direction is not necessary. By only selecting a... | train | [
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nips_2022_bk8vkdQfBS | Explainability Via Causal Self-Talk | Explaining the behavior of AI systems is an important problem that, in practice, is generally avoided. While the XAI community has been developing an abundance of techniques, most incur a set of costs that the wider deep learning community has been unwilling to pay in most situations. We take a pragmatic view of the is... | Accept | This paper proposes causal self-talk (CST) as a means to obtain more explainable AI systems. The work lists a set of desiderata for explainable AI and argues that CST satisfies this set. The paper is well written and the experimental results, although in a toy setting called "DaxDucks" are reasonably convincing. The re... | train | [
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nips_2022_t0VbBTw-o8 | Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks | Randomized smoothing is one of the most promising frameworks for certifying the adversarial robustness of machine learning models, including Graph Neural Networks (GNNs). Yet, existing randomized smoothing certificates for GNNs are overly pessimistic since they treat the model as a black box, ignoring the underlying ar... | Accept | The paper proposes a novel approach to certify the robustness of graph neural networks via randomized smoothing. It does so by treating the networks as ``gray-box'' models and leveraging message passing routines. This yields an improved lower bound on probabilistic certification.
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" Thank you for the clarification and updating the paper. I am satisfied with the response. Therefore, I decided to keep my support of acceptance of this paper.",
" Thank you for your response!\n\nAl... | [
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nips_2022_djOANbV2zSu | UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs | We present an approach to quantifying both aleatoric and epistemic uncertainty for deep neural networks in image classification, based on generative adversarial networks (GANs). While most works in the literature that use GANs to generate out-of-distribution (OoD) examples only focus on the evaluation of OoD detection,... | Accept | The authors propose a new approach for training image classifiers with complete uncertainty quantification based on generative adversarial networks. The main idea is to use GANs to "shield" each class separately from the out-of-class (OoC) regime. This is done in combination with a one-vs-all classifier in the final DN... | train | [
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" Thanks for the response. My questions have been answered, and the additional experiments do reinforce that the setting is likely to be robust. My ratings remain unchanged.",
" Thanks for the response. \nMost of m... | [
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nips_2022_WOuGTb9QswS | Oscillatory Tracking of Continuous Attractor Neural Networks Account for Phase Precession and Procession of Hippocampal Place Cells | Hippocampal place cells of freely moving rodents display an intriguing temporal organization in their responses known as `theta phase precession', in which individual neurons fire at progressively earlier phases in successive theta cycles as the animal traverses the place fields. Recent experimental studies found that ... | Accept | This paper presents non-trivial and novel theoretical and computational modeling that accounts for experimentally observed phenomena: the theta phase procession and precession. These phenomena are implicated in the neural representation and learning of neuronal networks involving hippocampus. Although the current manus... | train | [
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" Dear All:\n\nWe appreciate the reviewers for their questions/comments and their time and effort in reviewing the paper. Since we haven't got any feedback from the reviewers (the deadline is Aug 09 '22 08:00 PM UTC), we are wondering if the updates to the manuscript and replies to the corresponding questions resol... | [
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nips_2022_gd7ZI0X7Q-h | ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler | Numerical reasoning over text is a challenging task of Artificial Intelligence (AI), requiring reading comprehension and numerical reasoning abilities. Previous approaches use numerical reasoning programs to represent the reasoning process. However, most works do not separate the generation of operators and operands, w... | Accept | The paper presents a new model called Numerical Reasoning with Adaptive Symbolic Compiler that is able to perform numerical reasoning tasks. One of the key ideas in this model is to separate the generation of operators and operands and to include a memory register to remember intermediate values. The method is compared... | train | [
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" The writing has been improved in the revised version and most of the questions have been resolved. Although the authors' response still does not convince me of an excellent technical novelty, it's reasonable to raise the overall rating (borderline accept).",
" We thank the R_2nDD for providing two references a... | [
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nips_2022_Y0Bm5tL92lg | Adaptation Accelerating Sampling-based Bayesian Inference in Attractor Neural Networks | The brain performs probabilistic Bayesian inference to interpret the external world. The sampling-based view assumes that the brain represents the stimulus posterior distribution via samples of stochastic neuronal responses. Although the idea of sampling-based inference is appealing, it faces a critical challenge of wh... | Accept | The reviewers agree that the paper makes an interesting contribution, connecting inference in probabilistic models with network models from computation neuroscience. | test | [
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nips_2022_hYx-xr1wdo | Subspace clustering in high-dimensions: Phase transitions \& Statistical-to-Computational gap | A simple model to study subspace clustering is the high-dimensional $k$-Gaussian mixture model where the cluster means are sparse vectors. Here we provide an exact asymptotic characterization of the statistically optimal reconstruction error in this model in the high-dimensional regime with extensive sparsity, i.e. whe... | Accept | The reviewers appreciate the solid theoretical results concerning statistical-computational tradeoff in subspace clustering. The exact asymptotics and clear presentation make the paper stand out. Therefore, I recommend acceptance. Meanwhile, please carefully revise the paper according to the reviews to highlight its st... | train | [
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nips_2022_K-A4tDJ6HHf | Diagnosing failures of fairness transfer across distribution shift in real-world medical settings | Diagnosing and mitigating changes in model fairness under distribution shift is an important component of the safe deployment of machine learning in healthcare settings. Importantly, the success of any mitigation strategy strongly depends on the \textit{structure} of the shift. Despite this, there has been little discu... | Accept | This is a compelling work characterizing some forms of model (non-)robustness to drift through a causal lens, with a focus on performance metrics including group-level fairness. The methodological novelty to the work is a method for discovering structure for that drift, then using that structure to (i) estimate impact... | train | [
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nips_2022_AYQI3rlp9tW | Efficient identification of informative features in simulation-based inference | Simulation-based Bayesian inference (SBI) can be used to estimate the parameters of complex mechanistic models given observed model outputs without requiring access to explicit likelihood evaluations. A prime example for the application of SBI in neuroscience involves estimating the parameters governing the response dy... | Accept | The paper presents a method for feature selection in simulation-based inference, that is, for quantifying to what extent each feature (or summary statistic) contributes to reducing posterior uncertainty. As this can be accomplished naively by re-estimating the posterior after systematically omitting features, the focus... | train | [
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" We thank the reviewer for their reply and for appreciating our efforts.",
" Thanks for your reply. I think it all makes sense and I'm glad you added the discussion of MI and how a single observation can be important in neuroscience. I... | [
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nips_2022_S8-duMv77W3 | Sound and Complete Causal Identification with Latent Variables Given Local Background Knowledge | Great efforts have been devoted to causal discovery from observational data, and it is well known that introducing some background knowledge attained from experiments or human expertise can be very helpful. However, it remains unknown that \emph{what causal relations are identifiable given background knowledge in the p... | Accept | This paper presents sound and complete orientation rules to incorporate local causal background knowledge along with algorithms implementing these rules. Reviewers were universally appreciative of the contributions and in favor of acceptance. | train | [
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" Thank you for your clarification. All my concerns have been addressed.",
" Thanks for the response regarding the comparison to Jaber et al. I will keep my current score.",
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nips_2022_aJ5xc1QB7EX | Deep Active Learning by Leveraging Training Dynamics | Active learning theories and methods have been extensively studied in classical statistical learning settings. However, deep active learning, i.e., active learning with deep learning models, is usually based on empirical criteria without solid theoretical justification, thus suffering from heavy doubts when some of tho... | Accept | It is the consensus of the reviewers that this paper makes a worthwhile contribution to active deep learning. The author(s)' idea of optimizing for convergence speed is interesting and of potential significance. The meta-reviewer would recommend acceptance of the paper as a poster. | train | [
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" Thanks for the responses.",
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nips_2022_vdxOesWgbyN | Model Extraction Attacks on Split Federated Learning | Federated learning (FL) is a popular collaborative learning scheme involving multiple clients and a server. FL focuses on client's data privacy but exposes interfaces for Model Extraction (ME) attacks. As FL periodically collects and shares model parameters, a malicious client can download the latest model and thus ste... | Reject | The paper studies the vulnerability of split federated learning with model extraction attacks. The paper provides five attacks and evaluates them experimentally. The authors also provided additional experimental results during the author rebuttal. While the topic and techniques are interesting, reviewers raise concern... | train | [
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nips_2022_kUnHCGiILeU | CageNeRF: Cage-based Neural Radiance Field for Generalized 3D Deformation and Animation | While implicit representations have achieved high-fidelity results in 3D rendering, deforming and animating the implicit field remains challenging. Existing works typically leverage data-dependent models as deformation priors, such as SMPL for human body animation. However, this dependency on category-specific priors l... | Accept | All reviewers consider the central idea of the paper to be novel and interesting, and that deformable NeRFs are a valuable research area.
All reviewers agree that the initial paper was poorly presented, and that the revised version is considerably improved.
While accepting the authors' rebuttal regarding the lack of ... | train | [
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nips_2022_k5uFiFLWv3X | Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation | Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples, which can produce erroneous predictions by injecting imperceptible perturbations. In this work, we study the transferability of adversarial examples, which is significant due to its threat to real-world applications where model archit... | Accept | This paper studies the transferability of adversarial examples. In general, the reviewers found the paper is well motivated, and the proposed method is simple and effective. Most initial concerns were about missing comparisons and ablations.
All these concerns are well addressed in the rebuttal. As a result, all revi... | train | [
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" Dear Reviewer pUE8, \n\nConsidering the discussion stage is close to the end, we are looking forward to your further feedback about our latest response (posted one day ago, see below), whether your remaining concern has been addressed. We would like to discuss with you in more details. \nGreatly appreciate your h... | [
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nips_2022_OoN6TVb4Vkq | Contextual Bandits with Knapsacks for a Conversion Model | We consider contextual bandits with knapsacks, with an underlying structure between rewards generated and cost vectors suffered. We do so motivated by sales with commercial discounts. At each round, given the stochastic i.i.d.\ context $\mathbf{x}_t$ and the arm picked $a_t$ (corresponding, e.g., to a discount level), ... | Accept | As acknowledged in the reviews (and I concur), this is a well written paper that introduces a relevant and interesting contextual-bandits model and gives solid technical results. The paper certainly has its limitations, but overall the technical contributions are novel and well executed. The authors have effectively ... | train | [
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" I thank the authors for their detailed response and they have addressed most of my questions on the technical results. In terms of future directions, I think the main limitations of the current work (e.g., finiteness of contextual space, lack of specific lower bound results, etc.) remain to be further looked at. ... | [
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nips_2022_buXZ7nIqiwE | Using natural language and program abstractions to instill human inductive biases in machines | Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks. Although meta-learning is a method to endow neural networks with useful inductive biases, agents trained by meta-learning may sometimes acquire very different strategies from humans. We show that co-training these agents on ... | Accept | The submission explores differences in human and machine inductive biases. Using the task of generating a pattern in a grid, the authors first show that models trained on human inputs generalize better to machine generated inputs than other human inputs, suggesting that models lack the correct inductive bias. However, ... | train | [
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" Thanks a lot for the detailed responses, all my points are adequately addressed. I agree with the authors that the weaknesses I mentioned in my initial review are better seen as avenues for future work. Very interesting to learn about this meta-learning approach for RL, I will read more about that. I will keep my... | [
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nips_2022_5vVSA_cdRqe | FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning | Vertical federated learning (VFL) is a privacy-preserving machine learning paradigm that can learn models from features distributed on different platforms in a privacy-preserving way. Since in real-world applications the data may contain bias on fairness-sensitive features (e.g., gender), VFL models may inherit bias fr... | Accept | This paper presents a fair vertical federated learning framework (FairVFL), by learning a set of unified fair representations of data/features distributed across decentralized platforms, i.e., these representations do not reflect sensitive attributes such as age/gender. This is accomplished by having platforms learn l... | train | [
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" I appreciate the authors' response on my questions. They more or less answer my questions. However, as I am not an expert in fairness-aware ML, I maintain my score at 6 at the present stage. ",
" Thank the reviewer for the insightful comments and constructive suggestions. Our detailed responses to your comments... | [
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nips_2022_U-RsnLYHcKa | Wasserstein Logistic Regression with Mixed Features | Recent work has leveraged the popular distributionally robust optimization paradigm to combat overfitting in classical logistic regression. While the resulting classification scheme displays a promising performance in numerical experiments, it is inherently limited to numerical features. In this paper, we show that dis... | Accept | The focus of the submission is distributionally robust logistic regression when the discrepancy used in the ambiguity set is the Wasserstein distance and the features are mixed (i.e., they can contain both numerical and categorical variables). After showing that the resulting optimization problem (1) with the log-loss ... | test | [
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" Thank you for your detailed explanations. I have no more questions and would tend to raise my rating from 5 to 6 (weak accept).",
" We thank you again for your comments! We agree with you on the statement of Theorem 2 and will clarify that in our next revision of the paper.",
" I thank the authors for their r... | [
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nips_2022_RW-OOBU11xl | Forecasting Human Trajectory from Scene History | Predicting the future trajectory of a person remains a challenging problem, due to randomness and subjectivity. However, the moving patterns of human in constrained scenario typically conform to a limited number of regularities to a certain extent, because of the scenario restrictions (\eg, floor plan, roads and obstac... | Accept | Initially, the paper received mixed reviews (3456). The major concerns raised by the reviews were:
1. What is the contribution of the PAV dataset? (XkVC)
2. There should be experiments on existing datasets, e.g. SDD or inDD. (XkVC, tAHR)
3. Is it fair to use curve smoothing on the GT during evaluation? To be fair, ot... | train | [
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" Thank you for uploading the revised the paper. I've checked the manuscript and increased my rating. I highly encourage authors to include several more lateset works in this year CVPR, since there are some highly relevant papers in the proceedings. i.e. [1][2][3] etc.\n\nAlso, there are some typos in the manuscrip... | [
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nips_2022_-QHUWgkh1OY | DOGE-Train: Discrete Optimization on GPU with End-to-end Training | We present a fast, scalable, data-driven approach for solving linear relaxations of 0-1 integer linear programs using a graph neural network.
Our solver is based on the Lagrange decomposition based algorithm of Abbas et al. (2022).
We make the algorithm differentiable and perform backpropagation through the dual update... | Reject | The presented paper introduces DOGE-Train method that targets discrete optimization problems. It allows finding solutions for discrete problems utilizing GPUs. This is achieved by pre-training on smaller size instances and then hoping it would also generalize for larger instances that are coming from a similar family o... | train | [
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" We thank the reviewer for detailed feedback. We will add more context and discussion about specialized solvers in the final version.\n\n_Regards,_\n\n_Paper 3276 authors_",
" Thanks for answering questions! Now, the proposed approach becomes technically more clear and the presentation is more consistent. The pr... | [
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nips_2022_Mn4IkuWamy | The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning | We study the multi-step off-policy learning approach to distributional RL. Despite the apparent similarity between value-based RL and distributional RL, our study reveals intriguing and fundamental differences between the two cases in the multi-step setting. We identify a novel notion of path-dependent distributional T... | Accept | The reviewers carefully analyzed this work and agreed that the topics investigated in this paper are important and relevant to the field. Although the reviewers generally expressed positive views on the proposed method, they also pointed out many possible limitations of this paper. On the one hand, one reviewer acknowl... | train | [
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" Thank you very much for your reply. We are glad that the explanation above has clarified things. We will make sure to include such discussions and incorporate your suggestions in our revision.",
" Thank you very much for the informative response, your argument is now clear to me! Could you please add a small no... | [
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nips_2022_VwRFJi9crEH | Personalized Subgraph Federated Learning | In real-world scenarios, subgraphs of a larger global graph may be distributed across multiple devices or institutions, and only locally accessible due to privacy restrictions, although there may be links between them. Recently proposed subgraph Federated Learning (FL) methods deal with those missing links across priva... | Reject | The author(s) present(s) a new subgraph federated learning approach to learn a single GNN model that computes embeddings based on the relationship between local graphs. This approach goes beyond the previous approaches that consider the local subgraphs separately.
The paper is interesting and present some novel ideas... | train | [
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" We sincerely thank you for your helpful comments, as well as your time and effort in reviewing our paper. We are happy to hear that your concerns are mostly resolved. \n\n**NOTE:** I know you must be very busy, but could you please reflect your updated score in your original comment? You promised to increase your... | [
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