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DivBO: Diversity-aware CASH for Ensemble Learning | https://papers.nips.cc/paper_files/paper/2022/hash/13b2f88be223cd2b4d6be67b56e02fa8-Abstract-Conference.html | Yu Shen, Yupeng Lu, Yang Li, Yaofeng Tu, Wentao Zhang, Bin CUI | https://papers.nips.cc/paper_files/paper/2022/hash/13b2f88be223cd2b4d6be67b56e02fa8-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18216-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/13b2f88be223cd2b4d6be67b56e02fa8-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/13b2f88be223cd2b4d6be67b56e02fa8-Supplemental-Conference.zip | The Combined Algorithm Selection and Hyperparameters optimization (CASH) problem is one of the fundamental problems in Automated Machine Learning (AutoML). Motivated by the success of ensemble learning, recent AutoML systems build post-hoc ensembles to output the final predictions instead of using the best single learn... | null | null |
Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum | https://papers.nips.cc/paper_files/paper/2022/hash/13b45b44e26c353c64cba9529bf4724f-Abstract-Conference.html | Nian Liu, Xiao Wang, Deyu Bo, Chuan Shi, Jian Pei | https://papers.nips.cc/paper_files/paper/2022/hash/13b45b44e26c353c64cba9529bf4724f-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19434-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/13b45b44e26c353c64cba9529bf4724f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/13b45b44e26c353c64cba9529bf4724f-Supplemental-Conference.pdf | Graph Contrastive Learning (GCL), learning the node representations by augmenting graphs, has attracted considerable attentions. Despite the proliferation of various graph augmentation strategies, there are still some fundamental questions unclear: what information is essentially learned by GCL? Are there some general ... | null | null |
Functional Indirection Neural Estimator for Better Out-of-distribution Generalization | https://papers.nips.cc/paper_files/paper/2022/hash/13b8d8fb8d05369480c2c344f2ce3f25-Abstract-Conference.html | Kha Pham, Thai Hung Le, Man Ngo, Truyen Tran | https://papers.nips.cc/paper_files/paper/2022/hash/13b8d8fb8d05369480c2c344f2ce3f25-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17335-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/13b8d8fb8d05369480c2c344f2ce3f25-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/13b8d8fb8d05369480c2c344f2ce3f25-Supplemental-Conference.zip | The capacity to achieve out-of-distribution (OOD) generalization is a hallmark of human intelligence and yet remains out of reach for machines. This remarkable capability has been attributed to our abilities to make conceptual abstraction and analogy, and to a mechanism known as indirection, which binds two representat... | null | null |
Combinatorial Bandits with Linear Constraints: Beyond Knapsacks and Fairness | https://papers.nips.cc/paper_files/paper/2022/hash/13f17f74ec061f1e3e231aca9a43ff23-Abstract-Conference.html | Qingsong Liu, Weihang Xu, Siwei Wang, Zhixuan Fang | https://papers.nips.cc/paper_files/paper/2022/hash/13f17f74ec061f1e3e231aca9a43ff23-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18091-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/13f17f74ec061f1e3e231aca9a43ff23-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/13f17f74ec061f1e3e231aca9a43ff23-Supplemental-Conference.pdf | This paper proposes and studies for the first time the problem of combinatorial multi-armed bandits with linear long-term constraints. Our model generalizes and unifies several prominent lines of work, including bandits with fairness constraints, bandits with knapsacks (BwK), etc. We propose an upper-confidence bound ... | null | null |
Will Bilevel Optimizers Benefit from Loops | https://papers.nips.cc/paper_files/paper/2022/hash/1413947ef79a733e4b839d339e3dffa7-Abstract-Conference.html | Kaiyi Ji, Mingrui Liu, Yingbin Liang, Lei Ying | https://papers.nips.cc/paper_files/paper/2022/hash/1413947ef79a733e4b839d339e3dffa7-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18840-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1413947ef79a733e4b839d339e3dffa7-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1413947ef79a733e4b839d339e3dffa7-Supplemental-Conference.pdf | Bilevel optimization has arisen as a powerful tool for solving a variety of machine learning problems. Two current popular bilevel optimizers AID-BiO and ITD-BiO naturally involve solving one or two sub-problems, and consequently, whether we solve these problems with loops (that take many iterations) or without loops (... | null | null |
Combining Explicit and Implicit Regularization for Efficient Learning in Deep Networks | https://papers.nips.cc/paper_files/paper/2022/hash/1419d8554191a65ea4f2d8e1057973e4-Abstract-Conference.html | Dan Zhao | https://papers.nips.cc/paper_files/paper/2022/hash/1419d8554191a65ea4f2d8e1057973e4-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18086-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1419d8554191a65ea4f2d8e1057973e4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1419d8554191a65ea4f2d8e1057973e4-Supplemental-Conference.zip | Works on implicit regularization have studied gradient trajectories during the optimization process to explain why deep networks favor certain kinds of solutions over others. In deep linear networks, it has been shown that gradient descent implicitly regularizes toward low-rank solutions on matrix completion/factorizat... | null | null |
On A Mallows-type Model For (Ranked) Choices | https://papers.nips.cc/paper_files/paper/2022/hash/145c28cd4b1df9b426990fd68045f4f7-Abstract-Conference.html | Yifan Feng, Yuxuan Tang | https://papers.nips.cc/paper_files/paper/2022/hash/145c28cd4b1df9b426990fd68045f4f7-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17358-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/145c28cd4b1df9b426990fd68045f4f7-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/145c28cd4b1df9b426990fd68045f4f7-Supplemental-Conference.pdf | We consider a preference learning setting where every participant chooses an ordered list of $k$ most preferred items among a displayed set of candidates. (The set can be different for every participant.) We identify a distance-based ranking model for the population's preferences and their (ranked) choice behavior. The... | null | null |
(De-)Randomized Smoothing for Decision Stump Ensembles | https://papers.nips.cc/paper_files/paper/2022/hash/146b4bab3f8536a07905f25d367b4924-Abstract-Conference.html | Miklós Horváth, Mark Müller, Marc Fischer, Martin Vechev | https://papers.nips.cc/paper_files/paper/2022/hash/146b4bab3f8536a07905f25d367b4924-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18820-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/146b4bab3f8536a07905f25d367b4924-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/146b4bab3f8536a07905f25d367b4924-Supplemental-Conference.pdf | Tree-based models are used in many high-stakes application domains such as finance and medicine, where robustness and interpretability are of utmost importance. Yet, methods for improving and certifying their robustness are severely under-explored, in contrast to those focusing on neural networks. Targeting this importa... | null | null |
Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation | https://papers.nips.cc/paper_files/paper/2022/hash/148c0aeea1c5da82f4fa86a09d4190da-Abstract-Conference.html | Jin Xu, Xiaojiang Liu, Jianhao Yan, Deng Cai, Huayang Li, Jian Li | https://papers.nips.cc/paper_files/paper/2022/hash/148c0aeea1c5da82f4fa86a09d4190da-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18967-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/148c0aeea1c5da82f4fa86a09d4190da-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/148c0aeea1c5da82f4fa86a09d4190da-Supplemental-Conference.pdf | While large-scale neural language models, such as GPT2 and BART,have achieved impressive results on various text generation tasks, they tend to get stuck in undesirable sentence-level loops with maximization-based decoding algorithms (\textit{e.g.}, greedy search). This phenomenon is counter-intuitive since there are f... | null | null |
Debiased Machine Learning without Sample-Splitting for Stable Estimators | https://papers.nips.cc/paper_files/paper/2022/hash/1498a03a04f9bcd3a7d44058fc5dc639-Abstract-Conference.html | Qizhao Chen, Vasilis Syrgkanis, Morgane Austern | https://papers.nips.cc/paper_files/paper/2022/hash/1498a03a04f9bcd3a7d44058fc5dc639-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17407-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1498a03a04f9bcd3a7d44058fc5dc639-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1498a03a04f9bcd3a7d44058fc5dc639-Supplemental-Conference.zip | Estimation and inference on causal parameters is typically reduced to a generalized method of moments problem, which involves auxiliary functions that correspond to solutions to a regression or classification problem. Recent line of work on debiased machine learning shows how one can use generic machine learning estima... | null | null |
Near-Optimal Sample Complexity Bounds for Constrained MDPs | https://papers.nips.cc/paper_files/paper/2022/hash/14a5ebc9cd2e507cd811df78c15bf5d7-Abstract-Conference.html | Sharan Vaswani, Lin Yang, Csaba Szepesvari | https://papers.nips.cc/paper_files/paper/2022/hash/14a5ebc9cd2e507cd811df78c15bf5d7-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17645-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/14a5ebc9cd2e507cd811df78c15bf5d7-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/14a5ebc9cd2e507cd811df78c15bf5d7-Supplemental-Conference.pdf | In contrast to the advances in characterizing the sample complexity for solving Markov decision processes (MDPs), the optimal statistical complexity for solving constrained MDPs (CMDPs) remains unknown. We resolve this question by providing minimax upper and lower bounds on the sample complexity for learning near-optim... | null | null |
Integral Probability Metrics PAC-Bayes Bounds | https://papers.nips.cc/paper_files/paper/2022/hash/14da7aea05debb963b3d8d46449d51a0-Abstract-Conference.html | Ron Amit, Baruch Epstein, Shay Moran, Ron Meir | https://papers.nips.cc/paper_files/paper/2022/hash/14da7aea05debb963b3d8d46449d51a0-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18131-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/14da7aea05debb963b3d8d46449d51a0-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/14da7aea05debb963b3d8d46449d51a0-Supplemental-Conference.pdf | We present a PAC-Bayes-style generalization bound which enables the replacement of the KL-divergence with a variety of Integral Probability Metrics (IPM). We provide instances of this bound with the IPM being the total variation metric and the Wasserstein distance. A notable feature of the obtained bounds is that they ... | null | null |
Bellman Residual Orthogonalization for Offline Reinforcement Learning | https://papers.nips.cc/paper_files/paper/2022/hash/14ecbfb2216bab76195b60bfac7efb1f-Abstract-Conference.html | Andrea Zanette, Martin J Wainwright | https://papers.nips.cc/paper_files/paper/2022/hash/14ecbfb2216bab76195b60bfac7efb1f-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19058-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/14ecbfb2216bab76195b60bfac7efb1f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/14ecbfb2216bab76195b60bfac7efb1f-Supplemental-Conference.pdf | We propose and analyze a reinforcement learning principle thatapproximates the Bellman equations by enforcing their validity onlyalong a user-defined space of test functions. Focusing onapplications to model-free offline RL with function approximation, weexploit this principle to derive confidence intervals for off-po... | null | null |
Quantum Speedups of Optimizing Approximately Convex Functions with Applications to Logarithmic Regret Stochastic Convex Bandits | https://papers.nips.cc/paper_files/paper/2022/hash/14f75513f0f1ca01de1e826b52e6b840-Abstract-Conference.html | Tongyang Li, Ruizhe Zhang | https://papers.nips.cc/paper_files/paper/2022/hash/14f75513f0f1ca01de1e826b52e6b840-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18095-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/14f75513f0f1ca01de1e826b52e6b840-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/14f75513f0f1ca01de1e826b52e6b840-Supplemental-Conference.pdf | We initiate the study of quantum algorithms for optimizing approximately convex functions. Given a convex set $\mathcal{K}\subseteq\mathbb{R}^{n}$ and a function $F\colon\mathbb{R}^{n}\to\mathbb{R}$ such that there exists a convex function $f\colon\mathcal{K}\to\mathbb{R}$ satisfying $\sup_{x\in\mathcal{K}}|F(x)-f(x)|\... | null | null |
Learning Neural Acoustic Fields | https://papers.nips.cc/paper_files/paper/2022/hash/151f4dfc71f025ae387e2d7a4ea1639b-Abstract-Conference.html | Andrew Luo, Yilun Du, Michael Tarr, Josh Tenenbaum, Antonio Torralba, Chuang Gan | https://papers.nips.cc/paper_files/paper/2022/hash/151f4dfc71f025ae387e2d7a4ea1639b-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17515-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/151f4dfc71f025ae387e2d7a4ea1639b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/151f4dfc71f025ae387e2d7a4ea1639b-Supplemental-Conference.zip | Our environment is filled with rich and dynamic acoustic information. When we walk into a cathedral, the reverberations as much as appearance inform us of the sanctuary's wide open space. Similarly, as an object moves around us, we expect the sound emitted to also exhibit this movement. While recent advances in learned... | null | null |
A Universal Error Measure for Input Predictions Applied to Online Graph Problems | https://papers.nips.cc/paper_files/paper/2022/hash/15212bd2265c4a3ab0dbc1b1982c1b69-Abstract-Conference.html | Giulia Bernardini, Alexander Lindermayr, Alberto Marchetti-Spaccamela, Nicole Megow, Leen Stougie, Michelle Sweering | https://papers.nips.cc/paper_files/paper/2022/hash/15212bd2265c4a3ab0dbc1b1982c1b69-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18001-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/15212bd2265c4a3ab0dbc1b1982c1b69-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/15212bd2265c4a3ab0dbc1b1982c1b69-Supplemental-Conference.zip | We introduce a novel measure for quantifying the error in input predictions. The error is based on a minimum-cost hyperedge cover in a suitably defined hypergraph and provides a general template which we apply to online graph problems. The measure captures errors due to absent predicted requests as well as unpredicted ... | null | null |
Online Reinforcement Learning for Mixed Policy Scopes | https://papers.nips.cc/paper_files/paper/2022/hash/15349e1c554406b7719d047a498e7117-Abstract-Conference.html | Junzhe Zhang, Elias Bareinboim | https://papers.nips.cc/paper_files/paper/2022/hash/15349e1c554406b7719d047a498e7117-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17642-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/15349e1c554406b7719d047a498e7117-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/15349e1c554406b7719d047a498e7117-Supplemental-Conference.pdf | Combination therapy refers to the use of multiple treatments -- such as surgery, medication, and behavioral therapy - to cure a single disease, and has become a cornerstone for treating various conditions including cancer, HIV, and depression. All possible combinations of treatments lead to a collection of treatment re... | null | null |
Self-explaining deep models with logic rule reasoning | https://papers.nips.cc/paper_files/paper/2022/hash/1548d98b62d3a4382a31ba77d89186cd-Abstract-Conference.html | Seungeon Lee, Xiting Wang, Sungwon Han, Xiaoyuan Yi, Xing Xie, Meeyoung Cha | https://papers.nips.cc/paper_files/paper/2022/hash/1548d98b62d3a4382a31ba77d89186cd-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17824-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1548d98b62d3a4382a31ba77d89186cd-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1548d98b62d3a4382a31ba77d89186cd-Supplemental-Conference.zip | We present SELOR, a framework for integrating self-explaining capabilities into a given deep model to achieve both high prediction performance and human precision. By “human precision”, we refer to the degree to which humans agree with the reasons models provide for their predictions. Human precision affects user trust... | null | null |
XTC: Extreme Compression for Pre-trained Transformers Made Simple and Efficient | https://papers.nips.cc/paper_files/paper/2022/hash/1579d5d8edacd85ac1a86aea28bdf32d-Abstract-Conference.html | Xiaoxia Wu, Zhewei Yao, Minjia Zhang, Conglong Li, Yuxiong He | https://papers.nips.cc/paper_files/paper/2022/hash/1579d5d8edacd85ac1a86aea28bdf32d-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16855-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1579d5d8edacd85ac1a86aea28bdf32d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1579d5d8edacd85ac1a86aea28bdf32d-Supplemental-Conference.pdf | Extreme compression, particularly ultra-low bit precision (binary/ternary) quantization, has been proposed to fit large NLP models on resource-constraint devices. However, to preserve the accuracy for such aggressive compression schemes, cutting-edge methods usually introduce complicated compression pipelines, e.g., mu... | null | null |
S3GC: Scalable Self-Supervised Graph Clustering | https://papers.nips.cc/paper_files/paper/2022/hash/15972a9575e0f03bf82f00aebeb40774-Abstract-Conference.html | Fnu Devvrit, Aditya Sinha, Inderjit Dhillon, Prateek Jain | https://papers.nips.cc/paper_files/paper/2022/hash/15972a9575e0f03bf82f00aebeb40774-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16657-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/15972a9575e0f03bf82f00aebeb40774-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/15972a9575e0f03bf82f00aebeb40774-Supplemental-Conference.pdf | We study the problem of clustering graphs with additional side-information of node features. The problem is extensively studied, and several existing methods exploit Graph Neural Networks to learn node representations. However, most of the existing methods focus on generic representations instead of their cluster-abil... | null | null |
Contrastive Neural Ratio Estimation | https://papers.nips.cc/paper_files/paper/2022/hash/159f7fe5b51ecd663b85337e8e28ce65-Abstract-Conference.html | Benjamin K Miller, Christoph Weniger, Patrick Forré | https://papers.nips.cc/paper_files/paper/2022/hash/159f7fe5b51ecd663b85337e8e28ce65-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18266-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/159f7fe5b51ecd663b85337e8e28ce65-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/159f7fe5b51ecd663b85337e8e28ce65-Supplemental-Conference.pdf | Likelihood-to-evidence ratio estimation is usually cast as either a binary (NRE-A) or a multiclass (NRE-B) classification task. In contrast to the binary classification framework, the current formulation of the multiclass version has an intrinsic and unknown bias term, making otherwise informative diagnostics unreliabl... | null | null |
An Information-Theoretic Framework for Deep Learning | https://papers.nips.cc/paper_files/paper/2022/hash/15cc8e4a46565dab0c1a1220884bd503-Abstract-Conference.html | Hong Jun Jeon, Benjamin Van Roy | https://papers.nips.cc/paper_files/paper/2022/hash/15cc8e4a46565dab0c1a1220884bd503-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16905-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/15cc8e4a46565dab0c1a1220884bd503-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/15cc8e4a46565dab0c1a1220884bd503-Supplemental-Conference.pdf | Each year, deep learning demonstrate new and improved empirical results with deeper and wider neural networks. Meanwhile, with existing theoretical frameworks, it is difficult to analyze networks deeper than two layers without resorting to counting parameters or encountering sample complexity bounds that are exponentia... | null | null |
Uncoupled Learning Dynamics with $O(\log T)$ Swap Regret in Multiplayer Games | https://papers.nips.cc/paper_files/paper/2022/hash/15d45097f9806983f0629a77e93ee60f-Abstract-Conference.html | Ioannis Anagnostides, Gabriele Farina, Christian Kroer, Chung-Wei Lee, Haipeng Luo, Tuomas Sandholm | https://papers.nips.cc/paper_files/paper/2022/hash/15d45097f9806983f0629a77e93ee60f-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17140-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/15d45097f9806983f0629a77e93ee60f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/15d45097f9806983f0629a77e93ee60f-Supplemental-Conference.pdf | In this paper we establish efficient and \emph{uncoupled} learning dynamics so that, when employed by all players in a general-sum multiplayer game, the \emph{swap regret} of each player after $T$ repetitions of the game is bounded by $O(\log T)$, improving over the prior best bounds of $O(\log^4 (T))$. At the same tim... | null | null |
Robust Semi-Supervised Learning when Not All Classes have Labels | https://papers.nips.cc/paper_files/paper/2022/hash/15dce910311b9bd82ca24f634148519a-Abstract-Conference.html | Lan-Zhe Guo, Yi-Ge Zhang, Zhi-Fan Wu, Jie-Jing Shao, Yu-Feng Li | https://papers.nips.cc/paper_files/paper/2022/hash/15dce910311b9bd82ca24f634148519a-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19049-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/15dce910311b9bd82ca24f634148519a-Paper-Conference.pdf | null | Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data. Existing SSL typically requires all classes have labels. However, in many real-world applications, there may exist some classes that are difficult to label or newly occurred classes that cannot be labeled in time, resulting in t... | null | null |
Private Multiparty Perception for Navigation | https://papers.nips.cc/paper_files/paper/2022/hash/15ddb1773510075ef44981cdb204330b-Abstract-Conference.html | Hui Lu, Mia Chiquier, Carl Vondrick | https://papers.nips.cc/paper_files/paper/2022/hash/15ddb1773510075ef44981cdb204330b-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17367-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/15ddb1773510075ef44981cdb204330b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/15ddb1773510075ef44981cdb204330b-Supplemental-Conference.pdf | We introduce a framework for navigating through cluttered environments by connecting multiple cameras together while simultanously preserving privacy. Occlusions and obstacles in large environments are often challenging situations for navigation agents because the environment is not fully observable from a single camer... | null | null |
Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization | https://papers.nips.cc/paper_files/paper/2022/hash/16063a1c0f0cddd4894585cf44cebb2c-Abstract-Conference.html | Long-Kai Huang, Ying Wei | https://papers.nips.cc/paper_files/paper/2022/hash/16063a1c0f0cddd4894585cf44cebb2c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18123-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/16063a1c0f0cddd4894585cf44cebb2c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/16063a1c0f0cddd4894585cf44cebb2c-Supplemental-Conference.pdf | Recent years have witnessed the rapid development of meta-learning in improving the meta generalization over tasks in few-shot learning. However, the task-specific level generalization is overlooked in most algorithms. For a novel few-shot learning task where the empirical distribution likely deviates from the true di... | null | null |
C-Mixup: Improving Generalization in Regression | https://papers.nips.cc/paper_files/paper/2022/hash/1626be0ab7f3d7b3c639fbfd5951bc40-Abstract-Conference.html | Huaxiu Yao, Yiping Wang, Linjun Zhang, James Y. Zou, Chelsea Finn | https://papers.nips.cc/paper_files/paper/2022/hash/1626be0ab7f3d7b3c639fbfd5951bc40-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19339-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1626be0ab7f3d7b3c639fbfd5951bc40-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1626be0ab7f3d7b3c639fbfd5951bc40-Supplemental-Conference.pdf | Improving the generalization of deep networks is an important open challenge, particularly in domains without plentiful data. The mixup algorithm improves generalization by linearly interpolating a pair of examples and their corresponding labels. These interpolated examples augment the original training set. Mixup has ... | null | null |
Generalised Mutual Information for Discriminative Clustering | https://papers.nips.cc/paper_files/paper/2022/hash/16294049ed8de15830ac0b569b97f74a-Abstract-Conference.html | Louis Ohl, Pierre-Alexandre Mattei, Charles Bouveyron, Warith HARCHAOUI, Mickaël Leclercq, Arnaud Droit, Frederic Precioso | https://papers.nips.cc/paper_files/paper/2022/hash/16294049ed8de15830ac0b569b97f74a-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19069-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/16294049ed8de15830ac0b569b97f74a-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/16294049ed8de15830ac0b569b97f74a-Supplemental-Conference.pdf | In the last decade, recent successes in deep clustering majorly involved the mutual information (MI) as an unsupervised objective for training neural networks with increasing regularisations. While the quality of the regularisations have been largely discussed for improvements, little attention has been dedicated to th... | null | null |
Consistent Interpolating Ensembles via the Manifold-Hilbert Kernel | https://papers.nips.cc/paper_files/paper/2022/hash/16371a9d5fed65d6d78ca3a7fa6e598c-Abstract-Conference.html | Yutong Wang, Clay Scott | https://papers.nips.cc/paper_files/paper/2022/hash/16371a9d5fed65d6d78ca3a7fa6e598c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17765-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/16371a9d5fed65d6d78ca3a7fa6e598c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/16371a9d5fed65d6d78ca3a7fa6e598c-Supplemental-Conference.pdf | Recent research in the theory of overparametrized learning has sought to establish generalization guarantees in the interpolating regime. Such results have been established for a few common classes of methods, but so far not for ensemble methods. We devise an ensemble classification method that simultaneously interpola... | null | null |
Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction | https://papers.nips.cc/paper_files/paper/2022/hash/16415eed5a0a121bfce79924db05d3fe-Abstract-Conference.html | Qiancheng Fu, Qingshan Xu, Yew Soon Ong, Wenbing Tao | https://papers.nips.cc/paper_files/paper/2022/hash/16415eed5a0a121bfce79924db05d3fe-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19163-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/16415eed5a0a121bfce79924db05d3fe-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/16415eed5a0a121bfce79924db05d3fe-Supplemental-Conference.zip | Recently, neural implicit surfaces learning by volume rendering has become popular for multi-view reconstruction. However, one key challenge remains: existing approaches lack explicit multi-view geometry constraints, hence usually fail to generate geometry-consistent surface reconstruction. To address this challenge, w... | null | null |
Sublinear Algorithms for Hierarchical Clustering | https://papers.nips.cc/paper_files/paper/2022/hash/16466b6c95c5924784486ac5a3feeb65-Abstract-Conference.html | Arpit Agarwal, Sanjeev Khanna, Huan Li, Prathamesh Patil | https://papers.nips.cc/paper_files/paper/2022/hash/16466b6c95c5924784486ac5a3feeb65-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18611-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/16466b6c95c5924784486ac5a3feeb65-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/16466b6c95c5924784486ac5a3feeb65-Supplemental-Conference.pdf | Hierarchical clustering over graphs is a fundamental task in data mining and machine learning with applications in many domains including phylogenetics, social network analysis, and information retrieval. Specifically, we consider the recently popularized objective function for hierarchical clustering due to Dasgupta~\... | null | null |
Is Sortition Both Representative and Fair? | https://papers.nips.cc/paper_files/paper/2022/hash/165bbd0a0a1b9470ec34d5afec582d2e-Abstract-Conference.html | Soroush Ebadian, Gregory Kehne, Evi Micha, Ariel D. Procaccia, Nisarg Shah | https://papers.nips.cc/paper_files/paper/2022/hash/165bbd0a0a1b9470ec34d5afec582d2e-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16949-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/165bbd0a0a1b9470ec34d5afec582d2e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/165bbd0a0a1b9470ec34d5afec582d2e-Supplemental-Conference.zip | Sortition is a form of democracy built on random selection of representatives. Two of the key arguments in favor of sortition are that it provides representation (a random panel reflects the composition of the population) and fairness (everyone has a chance to participate). Uniformly random selection is perfectly fair,... | null | null |
Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis | https://papers.nips.cc/paper_files/paper/2022/hash/1663fba7b56da1e96bed6e30546a07b0-Abstract-Conference.html | Shayegan Omidshafiei, Andrei Kapishnikov, Yannick Assogba, Lucas Dixon, Been Kim | https://papers.nips.cc/paper_files/paper/2022/hash/1663fba7b56da1e96bed6e30546a07b0-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18937-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1663fba7b56da1e96bed6e30546a07b0-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1663fba7b56da1e96bed6e30546a07b0-Supplemental-Conference.pdf | Each year, expert-level performance is attained in increasingly-complex multiagent domains, where notable examples include Go, Poker, and StarCraft II. This rapid progression is accompanied by a commensurate need to better understand how such agents attain this performance, to enable their safe deployment, identify lim... | null | null |
Dynamic pricing and assortment under a contextual MNL demand | https://papers.nips.cc/paper_files/paper/2022/hash/1673a54332b2afc905722048c26f5a4c-Abstract-Conference.html | Noemie Perivier, Vineet Goyal | https://papers.nips.cc/paper_files/paper/2022/hash/1673a54332b2afc905722048c26f5a4c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17247-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1673a54332b2afc905722048c26f5a4c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1673a54332b2afc905722048c26f5a4c-Supplemental-Conference.pdf | We consider dynamic multi-product pricing and assortment problems under an unknown demand over T periods, where in each period, the seller decides on the price for each product or the assortment of products to offer to a customer who chooses according to an unknown Multinomial Logit Model (MNL). Such problems arise in ... | null | null |
DGD^2: A Linearly Convergent Distributed Algorithm For High-dimensional Statistical Recovery | https://papers.nips.cc/paper_files/paper/2022/hash/1687466683649e8bdcdec0e3f5c8de64-Abstract-Conference.html | Marie Maros, Gesualdo Scutari | https://papers.nips.cc/paper_files/paper/2022/hash/1687466683649e8bdcdec0e3f5c8de64-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17436-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1687466683649e8bdcdec0e3f5c8de64-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1687466683649e8bdcdec0e3f5c8de64-Supplemental-Conference.pdf | We study linear regression from data distributed over a network of agents (with no master node) under high-dimensional scaling, which allows the ambient dimension to grow faster than the sample size. We propose a novel decentralization of the projected gradient algorithm whereby agents iteratively update their local es... | null | null |
Pseudo-Riemannian Graph Convolutional Networks | https://papers.nips.cc/paper_files/paper/2022/hash/16c628ab12dc4caca8e7712affa6c767-Abstract-Conference.html | Bo Xiong, Shichao Zhu, Nico Potyka, Shirui Pan, Chuan Zhou, Steffen Staab | https://papers.nips.cc/paper_files/paper/2022/hash/16c628ab12dc4caca8e7712affa6c767-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17698-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/16c628ab12dc4caca8e7712affa6c767-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/16c628ab12dc4caca8e7712affa6c767-Supplemental-Conference.pdf | Graph Convolutional Networks (GCNs) are powerful frameworks for learning embeddings of graph-structured data. GCNs are traditionally studied through the lens of Euclidean geometry. Recent works find that non-Euclidean Riemannian manifolds provide specific inductive biases for embedding hierarchical or spherical data. H... | null | null |
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion | https://papers.nips.cc/paper_files/paper/2022/hash/16e71d1a24b98a02c17b1be1f634f979-Abstract-Conference.html | Philippe Weinzaepfel, Vincent Leroy, Thomas Lucas, Romain BRÉGIER, Yohann Cabon, Vaibhav ARORA, Leonid Antsfeld, Boris Chidlovskii, Gabriela Csurka, Jerome Revaud | https://papers.nips.cc/paper_files/paper/2022/hash/16e71d1a24b98a02c17b1be1f634f979-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17679-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/16e71d1a24b98a02c17b1be1f634f979-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/16e71d1a24b98a02c17b1be1f634f979-Supplemental-Conference.zip | Masked Image Modeling (MIM) has recently been established as a potent pre-training paradigm. A pretext task is constructed by masking patches in an input image, and this masked content is then predicted by a neural network using visible patches as sole input. This pre-training leads to state-of-the-art performance when... | null | null |
Sound and Complete Verification of Polynomial Networks | https://papers.nips.cc/paper_files/paper/2022/hash/1700ad4e6252e8f2955909f96367b34d-Abstract-Conference.html | Elias Abad Rocamora, Mehmet Fatih Sahin, Fanghui Liu, Grigorios Chrysos, Volkan Cevher | https://papers.nips.cc/paper_files/paper/2022/hash/1700ad4e6252e8f2955909f96367b34d-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19349-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1700ad4e6252e8f2955909f96367b34d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1700ad4e6252e8f2955909f96367b34d-Supplemental-Conference.zip | Polynomial Networks (PNs) have demonstrated promising performance on face and image recognition recently. However, robustness of PNs is unclear and thus obtaining certificates becomes imperative for enabling their adoption in real-world applications. Existing verification algorithms on ReLU neural networks (NNs) based ... | null | null |
Better SGD using Second-order Momentum | https://papers.nips.cc/paper_files/paper/2022/hash/1704fe7aaff33a54802b83a016050ab8-Abstract-Conference.html | Hoang Tran, Ashok Cutkosky | https://papers.nips.cc/paper_files/paper/2022/hash/1704fe7aaff33a54802b83a016050ab8-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18976-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1704fe7aaff33a54802b83a016050ab8-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1704fe7aaff33a54802b83a016050ab8-Supplemental-Conference.pdf | We develop a new algorithm for non-convex stochastic optimization that finds an $\epsilon$-critical point in the optimal $O(\epsilon^{-3})$ stochastic gradient and Hessian-vector product computations. Our algorithm uses Hessian-vector products to "correct'' a bias term in the momentum of SGD with momentum. This leads ... | null | null |
Learning Predictions for Algorithms with Predictions | https://papers.nips.cc/paper_files/paper/2022/hash/17061a94c3c7fda5fa24bbdd1832fa99-Abstract-Conference.html | Misha Khodak, Maria-Florina F. Balcan, Ameet Talwalkar, Sergei Vassilvitskii | https://papers.nips.cc/paper_files/paper/2022/hash/17061a94c3c7fda5fa24bbdd1832fa99-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16950-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/17061a94c3c7fda5fa24bbdd1832fa99-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/17061a94c3c7fda5fa24bbdd1832fa99-Supplemental-Conference.pdf | A burgeoning paradigm in algorithm design is the field of algorithms with predictions, in which algorithms can take advantage of a possibly-imperfect prediction of some aspect of the problem. While much work has focused on using predictions to improve competitive ratios, running times, or other performance measures, le... | null | null |
Unsupervised Point Cloud Completion and Segmentation by Generative Adversarial Autoencoding Network | https://papers.nips.cc/paper_files/paper/2022/hash/171846d7af5ea91e63db508154eaffe8-Abstract-Conference.html | Changfeng Ma, Yang Yang, Jie Guo, Fei Pan, Chongjun Wang, Yanwen Guo | https://papers.nips.cc/paper_files/paper/2022/hash/171846d7af5ea91e63db508154eaffe8-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18686-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/171846d7af5ea91e63db508154eaffe8-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/171846d7af5ea91e63db508154eaffe8-Supplemental-Conference.pdf | Most existing point cloud completion methods assume the input partial point cloud is clean, which is not practical in practice, and are Most existing point cloud completion methods assume the input partial point cloud is clean, which is not the case in practice, and are generally based on supervised learning. In this p... | null | null |
CalFAT: Calibrated Federated Adversarial Training with Label Skewness | https://papers.nips.cc/paper_files/paper/2022/hash/171c3678c36e39fc0074f3e7332a9a66-Abstract-Conference.html | Chen Chen, Yuchen Liu, Xingjun Ma, Lingjuan Lyu | https://papers.nips.cc/paper_files/paper/2022/hash/171c3678c36e39fc0074f3e7332a9a66-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17543-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/171c3678c36e39fc0074f3e7332a9a66-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/171c3678c36e39fc0074f3e7332a9a66-Supplemental-Conference.pdf | Recent studies have shown that, like traditional machine learning, federated learning (FL) is also vulnerable to adversarial attacks.To improve the adversarial robustness of FL, federated adversarial training (FAT) methods have been proposed to apply adversarial training locally before global aggregation. Although thes... | null | null |
Rethinking Generalization in Few-Shot Classification | https://papers.nips.cc/paper_files/paper/2022/hash/1734365bbf243480dbc491a327497cf1-Abstract-Conference.html | Markus Hiller, Rongkai Ma, Mehrtash Harandi, Tom Drummond | https://papers.nips.cc/paper_files/paper/2022/hash/1734365bbf243480dbc491a327497cf1-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17571-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1734365bbf243480dbc491a327497cf1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1734365bbf243480dbc491a327497cf1-Supplemental-Conference.pdf | Single image-level annotations only correctly describe an often small subset of an image’s content, particularly when complex real-world scenes are depicted. While this might be acceptable in many classification scenarios, it poses a significant challenge for applications where the set of classes differs significantly ... | null | null |
Stimulative Training of Residual Networks: A Social Psychology Perspective of Loafing | https://papers.nips.cc/paper_files/paper/2022/hash/1757af1fe1429801bdf3abf5600f8bba-Abstract-Conference.html | Peng Ye, Shengji Tang, Baopu Li, Tao Chen, Wanli Ouyang | https://papers.nips.cc/paper_files/paper/2022/hash/1757af1fe1429801bdf3abf5600f8bba-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19190-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1757af1fe1429801bdf3abf5600f8bba-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1757af1fe1429801bdf3abf5600f8bba-Supplemental-Conference.pdf | Residual networks have shown great success and become indispensable in today’s deep models. In this work, we aim to re-investigate the training process of residual networks from a novel social psychology perspective of loafing, and further propose a new training strategy to strengthen the performance of residual networ... | null | null |
EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations | https://papers.nips.cc/paper_files/paper/2022/hash/177d68f4adef163b7b123b5c5adb3c60-Abstract-Conference.html | Min Zhao, Fan Bao, Chongxuan LI, Jun Zhu | https://papers.nips.cc/paper_files/paper/2022/hash/177d68f4adef163b7b123b5c5adb3c60-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17041-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/177d68f4adef163b7b123b5c5adb3c60-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/177d68f4adef163b7b123b5c5adb3c60-Supplemental-Conference.pdf | Score-based diffusion models (SBDMs) have achieved the SOTA FID results in unpaired image-to-image translation (I2I). However, we notice that existing methods totally ignore the training data in the source domain, leading to sub-optimal solutions for unpaired I2I. To this end, we propose energy-guided stochastic diffe... | null | null |
Cryptographic Hardness of Learning Halfspaces with Massart Noise | https://papers.nips.cc/paper_files/paper/2022/hash/17826a22eb8b58494dfdfca61e772c39-Abstract-Conference.html | Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren | https://papers.nips.cc/paper_files/paper/2022/hash/17826a22eb8b58494dfdfca61e772c39-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18677-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/17826a22eb8b58494dfdfca61e772c39-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/17826a22eb8b58494dfdfca61e772c39-Supplemental-Conference.pdf | We study the complexity of PAC learning halfspaces in the presence of Massart noise. In this problem, we are given i.i.d. labeled examples $(\mathbf{x}, y) \in \mathbb{R}^N \times \{ \pm 1\}$, where the distribution of $\mathbf{x}$ is arbitrary and the label $y$ is a Massart corruption of $f(\mathbf{x})$, for an unknow... | null | null |
Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator | https://papers.nips.cc/paper_files/paper/2022/hash/1787533e171dcc8549cc2eb5a4840eec-Abstract-Conference.html | Lior Danon, Dan Garber | https://papers.nips.cc/paper_files/paper/2022/hash/1787533e171dcc8549cc2eb5a4840eec-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19389-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1787533e171dcc8549cc2eb5a4840eec-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1787533e171dcc8549cc2eb5a4840eec-Supplemental-Conference.pdf | Tyler's M-estimator is a well known procedure for robust and heavy-tailed covariance estimation. Tyler himself suggested an iterative fixed-point algorithm for computing his estimator however, it requires super-linear (in the size of the data) runtime per iteration, which maybe prohibitive in large scale. In this work... | null | null |
Reinforcement Learning with Non-Exponential Discounting | https://papers.nips.cc/paper_files/paper/2022/hash/178b306c7ee66a66db2171646e17da36-Abstract-Conference.html | Matthias Schultheis, Constantin A. Rothkopf, Heinz Koeppl | https://papers.nips.cc/paper_files/paper/2022/hash/178b306c7ee66a66db2171646e17da36-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17907-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/178b306c7ee66a66db2171646e17da36-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/178b306c7ee66a66db2171646e17da36-Supplemental-Conference.zip | Commonly in reinforcement learning (RL), rewards are discounted over time using an exponential function to model time preference, thereby bounding the expected long-term reward. In contrast, in economics and psychology, it has been shown that humans often adopt a hyperbolic discounting scheme, which is optimal when a s... | null | null |
Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization? | https://papers.nips.cc/paper_files/paper/2022/hash/17a234c91f746d9625a75cf8a8731ee2-Abstract-Conference.html | Rishi Bommasani, Kathleen A. Creel, Ananya Kumar, Dan Jurafsky, Percy S. Liang | https://papers.nips.cc/paper_files/paper/2022/hash/17a234c91f746d9625a75cf8a8731ee2-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17123-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/17a234c91f746d9625a75cf8a8731ee2-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/17a234c91f746d9625a75cf8a8731ee2-Supplemental-Conference.zip | As the scope of machine learning broadens, we observe a recurring theme of algorithmic monoculture: the same systems, or systems that share components (e.g. datasets, models), are deployed by multiple decision-makers. While sharing offers advantages like amortizing effort, it also has risks. We introduce and formaliz... | null | null |
Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness | https://papers.nips.cc/paper_files/paper/2022/hash/17a9ab4190289f0e1504bbb98d1d111a-Abstract-Conference.html | Amin Jaber, Adele Ribeiro, Jiji Zhang, Elias Bareinboim | https://papers.nips.cc/paper_files/paper/2022/hash/17a9ab4190289f0e1504bbb98d1d111a-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18251-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/17a9ab4190289f0e1504bbb98d1d111a-Paper-Conference.pdf | null | 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... | null | null |
Dynamic Fair Division with Partial Information | https://papers.nips.cc/paper_files/paper/2022/hash/17bb0edcc02bd1f74e771e23b2aa1501-Abstract-Conference.html | Gerdus Benade, Daniel Halpern, Alexandros Psomas | https://papers.nips.cc/paper_files/paper/2022/hash/17bb0edcc02bd1f74e771e23b2aa1501-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17662-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/17bb0edcc02bd1f74e771e23b2aa1501-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/17bb0edcc02bd1f74e771e23b2aa1501-Supplemental-Conference.pdf | 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... | null | null |
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning | https://papers.nips.cc/paper_files/paper/2022/hash/18210aa6209b9adfc97b8c17c3741d95-Abstract-Conference.html | Veit David Wild, Robert Hu, Dino Sejdinovic | https://papers.nips.cc/paper_files/paper/2022/hash/18210aa6209b9adfc97b8c17c3741d95-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18870-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/18210aa6209b9adfc97b8c17c3741d95-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/18210aa6209b9adfc97b8c17c3741d95-Supplemental-Conference.pdf | We develop a framework for generalized variational inference in infinite-dimensional function spaces and use it to construct a method termed Gaussian Wasserstein inference (GWI). GWI leverages the Wasserstein distance between Gaussian measures on the Hilbert space of square-integrable functions in order to determine a ... | null | null |
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases | https://papers.nips.cc/paper_files/paper/2022/hash/184c1e18d00d7752805324da48ad25be-Abstract-Conference.html | James Harrison, Luke Metz, Jascha Sohl-Dickstein | https://papers.nips.cc/paper_files/paper/2022/hash/184c1e18d00d7752805324da48ad25be-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17411-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/184c1e18d00d7752805324da48ad25be-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/184c1e18d00d7752805324da48ad25be-Supplemental-Conference.pdf | Learned optimizers---neural networks that are trained to act as optimizers---have the potential to dramatically accelerate training of machine learning models. However, even when meta-trained across thousands of tasks at huge computational expense, blackbox learned optimizers often struggle with stability and generaliz... | null | null |
"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach | https://papers.nips.cc/paper_files/paper/2022/hash/185087ea328b4f03ea8fd0c8aa96f747-Abstract-Conference.html | lingyu gu, Yongqi Du, yuan zhang, Di Xie, Shiliang Pu, Robert Qiu, Zhenyu Liao | https://papers.nips.cc/paper_files/paper/2022/hash/185087ea328b4f03ea8fd0c8aa96f747-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16866-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/185087ea328b4f03ea8fd0c8aa96f747-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/185087ea328b4f03ea8fd0c8aa96f747-Supplemental-Conference.pdf | Modern deep neural networks (DNNs) are extremely powerful; however, this comes at the price of increased depth and having more parameters per layer, making their training and inference more computationally challenging. In an attempt to address this key limitation, efforts have been devoted to the compression (e.g., spa... | null | null |
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss | https://papers.nips.cc/paper_files/paper/2022/hash/18561617ca0b4ffa293166b3186e04b0-Abstract-Conference.html | Jason Altschuler, Kunal Talwar | https://papers.nips.cc/paper_files/paper/2022/hash/18561617ca0b4ffa293166b3186e04b0-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17309-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/18561617ca0b4ffa293166b3186e04b0-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/18561617ca0b4ffa293166b3186e04b0-Supplemental-Conference.pdf | A central issue in machine learning is how to train models on sensitive user data. Industry has widely adopted a simple algorithm: Stochastic Gradient Descent with noise (a.k.a. Stochastic Gradient Langevin Dynamics). However, foundational theoretical questions about this algorithm's privacy loss remain open---even in ... | null | null |
Theseus: A Library for Differentiable Nonlinear Optimization | https://papers.nips.cc/paper_files/paper/2022/hash/185969291540b3cd86e70c51e8af5d08-Abstract-Conference.html | Luis Pineda, Taosha Fan, Maurizio Monge, Shobha Venkataraman, Paloma Sodhi, Ricky T. Q. Chen, Joseph Ortiz, Daniel DeTone, Austin Wang, Stuart Anderson, Jing Dong, Brandon Amos, Mustafa Mukadam | https://papers.nips.cc/paper_files/paper/2022/hash/185969291540b3cd86e70c51e8af5d08-Abstract-Conference.html | NIPS 2022 | null | null | null | null | null | null |
Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again | https://papers.nips.cc/paper_files/paper/2022/hash/187d94b3c93343f0e925b5cf729eadd5-Abstract-Conference.html | Xin-Chun Li, Wen-shu Fan, Shaoming Song, Yinchuan Li, bingshuai Li, Shao Yunfeng, De-Chuan Zhan | https://papers.nips.cc/paper_files/paper/2022/hash/187d94b3c93343f0e925b5cf729eadd5-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18637-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/187d94b3c93343f0e925b5cf729eadd5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/187d94b3c93343f0e925b5cf729eadd5-Supplemental-Conference.pdf | Knowledge Distillation (KD) aims at transferring the knowledge of a well-performed neural network (the {\it teacher}) to a weaker one (the {\it student}). A peculiar phenomenon is that a more accurate model doesn't necessarily teach better, and temperature adjustment can neither alleviate the mismatched capacity. To ex... | null | null |
Solving Quantitative Reasoning Problems with Language Models | https://papers.nips.cc/paper_files/paper/2022/hash/18abbeef8cfe9203fdf9053c9c4fe191-Abstract-Conference.html | Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, Vedant Misra | https://papers.nips.cc/paper_files/paper/2022/hash/18abbeef8cfe9203fdf9053c9c4fe191-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18305-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/18abbeef8cfe9203fdf9053c9c4fe191-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/18abbeef8cfe9203fdf9053c9c4fe191-Supplemental-Conference.zip | 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. ... | null | null |
Structural Knowledge Distillation for Object Detection | https://papers.nips.cc/paper_files/paper/2022/hash/18c0102cb7f1a02c14f0929089b2e576-Abstract-Conference.html | Philip de Rijk, Lukas Schneider, Marius Cordts, Dariu Gavrila | https://papers.nips.cc/paper_files/paper/2022/hash/18c0102cb7f1a02c14f0929089b2e576-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18398-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/18c0102cb7f1a02c14f0929089b2e576-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/18c0102cb7f1a02c14f0929089b2e576-Supplemental-Conference.zip | Knowledge Distillation (KD) is a well-known training paradigm in deep neural networks where knowledge acquired by a large teacher model is transferred to a small student.KD has proven to be an effective technique to significantly improve the student's performance for various tasks including object detection. As such, K... | null | null |
Thompson Sampling Efficiently Learns to Control Diffusion Processes | https://papers.nips.cc/paper_files/paper/2022/hash/18c54ed6e0cc390d750f64927dbc4e93-Abstract-Conference.html | Mohamad Kazem Shirani Faradonbeh, Mohamad Sadegh Shirani Faradonbeh, Mohsen Bayati | https://papers.nips.cc/paper_files/paper/2022/hash/18c54ed6e0cc390d750f64927dbc4e93-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18200-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/18c54ed6e0cc390d750f64927dbc4e93-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/18c54ed6e0cc390d750f64927dbc4e93-Supplemental-Conference.pdf | Diffusion processes that evolve according to linear stochastic differential equations are an important family of continuous-time dynamic decision-making models. Optimal policies are well-studied for them, under full certainty about the drift matrices. However, little is known about data-driven control of diffusion proc... | null | null |
Discrete Compositional Representations as an Abstraction for Goal Conditioned Reinforcement Learning | https://papers.nips.cc/paper_files/paper/2022/hash/18ddfb199d71a8a24f83abc1ced077b7-Abstract-Conference.html | Riashat Islam, Hongyu Zang, Anirudh Goyal, Alex M. Lamb, Kenji Kawaguchi, Xin Li, Romain Laroche, Yoshua Bengio, Remi Tachet des Combes | https://papers.nips.cc/paper_files/paper/2022/hash/18ddfb199d71a8a24f83abc1ced077b7-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17570-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/18ddfb199d71a8a24f83abc1ced077b7-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/18ddfb199d71a8a24f83abc1ced077b7-Supplemental-Conference.pdf | Goal-conditioned reinforcement learning (RL) is a promising direction for training agents that are capable of solving multiple tasks and reach a diverse set of objectives. How to \textit{specify} and \textit{ground} these goals in such a way that we can both reliably reach goals during training as well as generalize t... | null | null |
Graph Convolution Network based Recommender Systems: Learning Guarantee and Item Mixture Powered Strategy | https://papers.nips.cc/paper_files/paper/2022/hash/18fd48d9cbbf9a20e434c9d3db6973c5-Abstract-Conference.html | Leyan Deng, Defu Lian, Chenwang Wu, Enhong Chen | https://papers.nips.cc/paper_files/paper/2022/hash/18fd48d9cbbf9a20e434c9d3db6973c5-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18445-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/18fd48d9cbbf9a20e434c9d3db6973c5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/18fd48d9cbbf9a20e434c9d3db6973c5-Supplemental-Conference.zip | Inspired by their powerful representation ability on graph-structured data, Graph Convolution Networks (GCNs) have been widely applied to recommender systems, and have shown superior performance. Despite their empirical success, there is a lack of theoretical explorations such as generalization properties. In this pape... | null | null |
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions | https://papers.nips.cc/paper_files/paper/2022/hash/18fee39e2666f43cf44425138bae9def-Abstract-Conference.html | Haanvid Lee, Jongmin Lee, Yunseon Choi, Wonseok Jeon, Byung-Jun Lee, Yung-Kyun Noh, Kee-Eung Kim | https://papers.nips.cc/paper_files/paper/2022/hash/18fee39e2666f43cf44425138bae9def-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19107-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/18fee39e2666f43cf44425138bae9def-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/18fee39e2666f43cf44425138bae9def-Supplemental-Conference.pdf | We consider local kernel metric learning for off-policy evaluation (OPE) of deterministic policies in contextual bandits with continuous action spaces. Our work is motivated by practical scenarios where the target policy needs to be deterministic due to domain requirements, such as prescription of treatment dosage and ... | null | null |
Quasi-Newton Methods for Saddle Point Problems | https://papers.nips.cc/paper_files/paper/2022/hash/191ebdfc96f43928e278fcf5902be405-Abstract-Conference.html | Chengchang Liu, Luo Luo | https://papers.nips.cc/paper_files/paper/2022/hash/191ebdfc96f43928e278fcf5902be405-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16872-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/191ebdfc96f43928e278fcf5902be405-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/191ebdfc96f43928e278fcf5902be405-Supplemental-Conference.pdf | This paper studies quasi-Newton methods for strongly-convex-strongly-concave saddle point problems. We propose random Broyden family updates, which have explicit local superlinear convergence rate of ${\mathcal O}\big(\big(1-1/(d\varkappa^2)\big)^{k(k-1)/2}\big)$, where $d$ is the dimension of the problem, $\varkappa$... | null | null |
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency | https://papers.nips.cc/paper_files/paper/2022/hash/194b8dac525581c346e30a2cebe9a369-Abstract-Conference.html | Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, Marinka Zitnik | https://papers.nips.cc/paper_files/paper/2022/hash/194b8dac525581c346e30a2cebe9a369-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19189-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/194b8dac525581c346e30a2cebe9a369-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/194b8dac525581c346e30a2cebe9a369-Supplemental-Conference.pdf | Pre-training on time series poses a unique challenge due to the potential mismatch between pre-training and target domains, such as shifts in temporal dynamics, fast-evolving trends, and long-range and short-cyclic effects, which can lead to poor downstream performance. While domain adaptation methods can mitigate thes... | null | null |
Uncalibrated Models Can Improve Human-AI Collaboration | https://papers.nips.cc/paper_files/paper/2022/hash/1968ea7d985aa377e3a610b05fc79be0-Abstract-Conference.html | Kailas Vodrahalli, Tobias Gerstenberg, James Y. Zou | https://papers.nips.cc/paper_files/paper/2022/hash/1968ea7d985aa377e3a610b05fc79be0-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18173-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1968ea7d985aa377e3a610b05fc79be0-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1968ea7d985aa377e3a610b05fc79be0-Supplemental-Conference.zip | In many practical applications of AI, an AI model is used as a decision aid for human users. The AI provides advice that a human (sometimes) incorporates into their decision-making process. The AI advice is often presented with some measure of "confidence" that the human can use to calibrate how much they depend on or ... | null | null |
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces | https://papers.nips.cc/paper_files/paper/2022/hash/196c4e02b7464c554f0f5646af5d502e-Abstract-Conference.html | Vladimir Kostic, Pietro Novelli, Andreas Maurer, Carlo Ciliberto, Lorenzo Rosasco, Massimiliano Pontil | https://papers.nips.cc/paper_files/paper/2022/hash/196c4e02b7464c554f0f5646af5d502e-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17354-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/196c4e02b7464c554f0f5646af5d502e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/196c4e02b7464c554f0f5646af5d502e-Supplemental-Conference.pdf | We study a class of dynamical systems modelled as stationary Markov chains that admit an invariant distribution via the corresponding transfer or Koopman operator. While data-driven algorithms to reconstruct such operators are well known, their relationship with statistical learning is largely unexplored. We formalize ... | null | null |
Self-supervised surround-view depth estimation with volumetric feature fusion | https://papers.nips.cc/paper_files/paper/2022/hash/19a0a55fcb8fc0c31db093941fccd707-Abstract-Conference.html | Jung-Hee Kim, Junhwa Hur, Tien Phuoc Nguyen, Seong-Gyun Jeong | https://papers.nips.cc/paper_files/paper/2022/hash/19a0a55fcb8fc0c31db093941fccd707-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17761-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/19a0a55fcb8fc0c31db093941fccd707-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/19a0a55fcb8fc0c31db093941fccd707-Supplemental-Conference.pdf | We present a self-supervised depth estimation approach using a unified volumetric feature fusion for surround-view images. Given a set of surround-view images, our method constructs a volumetric feature map by extracting image feature maps from surround-view images and fuse the feature maps into a shared, unified 3D vo... | null | null |
On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation | https://papers.nips.cc/paper_files/paper/2022/hash/1a000ee0f122d0bbd3edb9bf55170ea3-Abstract-Conference.html | Markus Hiller, Mehrtash Harandi, Tom Drummond | https://papers.nips.cc/paper_files/paper/2022/hash/1a000ee0f122d0bbd3edb9bf55170ea3-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16684-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1a000ee0f122d0bbd3edb9bf55170ea3-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1a000ee0f122d0bbd3edb9bf55170ea3-Supplemental-Conference.pdf | Inspired by the concept of preconditioning, we propose a novel method to increase adaptation speed for gradient-based meta-learning methods without incurring extra parameters. We demonstrate that recasting the optimisation problem to a non-linear least-squares formulation provides a principled way to actively enforce a... | null | null |
Oracle-Efficient Online Learning for Smoothed Adversaries | https://papers.nips.cc/paper_files/paper/2022/hash/1a04df6a405210aab4986994b873db9b-Abstract-Conference.html | Nika Haghtalab, Yanjun Han, Abhishek Shetty, Kunhe Yang | https://papers.nips.cc/paper_files/paper/2022/hash/1a04df6a405210aab4986994b873db9b-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19178-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1a04df6a405210aab4986994b873db9b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1a04df6a405210aab4986994b873db9b-Supplemental-Conference.pdf | We study the design of computationally efficient online learning algorithms under smoothed analysis. In this setting, at every step, an adversary generates a sample from an adaptively chosen distribution whose density is upper bounded by $1/\sigma$ times the uniform density. Given access to an offline optimization (ERM... | null | null |
A Policy-Guided Imitation Approach for Offline Reinforcement Learning | https://papers.nips.cc/paper_files/paper/2022/hash/1a0755b249b772ed5529796b0a7cc9bd-Abstract-Conference.html | Haoran Xu, Li Jiang, Li Jianxiong, Xianyuan Zhan | https://papers.nips.cc/paper_files/paper/2022/hash/1a0755b249b772ed5529796b0a7cc9bd-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17683-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1a0755b249b772ed5529796b0a7cc9bd-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1a0755b249b772ed5529796b0a7cc9bd-Supplemental-Conference.pdf | Offline reinforcement learning (RL) methods can generally be categorized into two types: RL-based and Imitation-based. RL-based methods could in principle enjoy out-of-distribution generalization but suffer from erroneous off-policy evaluation. Imitation-based methods avoid off-policy evaluation but are too conservativ... | null | null |
Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games | https://papers.nips.cc/paper_files/paper/2022/hash/1a17a06de88cf77f25cda0da91615a54-Abstract-Conference.html | Ziang Song, Song Mei, Yu Bai | https://papers.nips.cc/paper_files/paper/2022/hash/1a17a06de88cf77f25cda0da91615a54-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19166-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1a17a06de88cf77f25cda0da91615a54-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1a17a06de88cf77f25cda0da91615a54-Supplemental-Conference.pdf | 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... | null | null |
VectorAdam for Rotation Equivariant Geometry Optimization | https://papers.nips.cc/paper_files/paper/2022/hash/1a774f3555593986d7d95e4780d9e4f4-Abstract-Conference.html | Selena Zihan Ling, Nicholas Sharp, Alec Jacobson | https://papers.nips.cc/paper_files/paper/2022/hash/1a774f3555593986d7d95e4780d9e4f4-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18164-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1a774f3555593986d7d95e4780d9e4f4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1a774f3555593986d7d95e4780d9e4f4-Supplemental-Conference.zip | The Adam optimization algorithm has proven remarkably effective for optimization problems across machine learning and even traditional tasks in geometry processing. At the same time, the development of equivariant methods, which preserve their output under the action of rotation or some other transformation, has proven... | null | null |
Matrix Multiplicative Weights Updates in Quantum Zero-Sum Games: Conservation Laws & Recurrence | https://papers.nips.cc/paper_files/paper/2022/hash/1a78459dbbcdc90783d183999e72176c-Abstract-Conference.html | Rahul Jain, Georgios Piliouras, Ryann Sim | https://papers.nips.cc/paper_files/paper/2022/hash/1a78459dbbcdc90783d183999e72176c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16670-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1a78459dbbcdc90783d183999e72176c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1a78459dbbcdc90783d183999e72176c-Supplemental-Conference.zip | Recent advances in quantum computing and in particular, the introduction of quantum GANs, have led to increased interest in quantum zero-sum game theory, extending the scope of learning algorithms for classical games into the quantum realm. In this paper, we focus on learning in quantum zero-sum games under Matrix Mult... | null | null |
On the Convergence Theory for Hessian-Free Bilevel Algorithms | https://papers.nips.cc/paper_files/paper/2022/hash/1a82986c9f321217f2ed407a14dcfa0b-Abstract-Conference.html | Daouda Sow, Kaiyi Ji, Yingbin Liang | https://papers.nips.cc/paper_files/paper/2022/hash/1a82986c9f321217f2ed407a14dcfa0b-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16655-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1a82986c9f321217f2ed407a14dcfa0b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1a82986c9f321217f2ed407a14dcfa0b-Supplemental-Conference.pdf | Bilevel optimization has arisen as a powerful tool in modern machine learning. However, due to the nested structure of bilevel optimization, even gradient-based methods require second-order derivative approximations via Jacobian- or/and Hessian-vector computations, which can be costly and unscalable in practice. Recent... | null | null |
Equivariant Networks for Crystal Structures | https://papers.nips.cc/paper_files/paper/2022/hash/1abed6ee581b9ceb4e2ddf37822c7fcb-Abstract-Conference.html | Oumar Kaba, Siamak Ravanbakhsh | https://papers.nips.cc/paper_files/paper/2022/hash/1abed6ee581b9ceb4e2ddf37822c7fcb-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16628-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1abed6ee581b9ceb4e2ddf37822c7fcb-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1abed6ee581b9ceb4e2ddf37822c7fcb-Supplemental-Conference.pdf | Supervised learning with deep models has tremendous potential for applications in materials science. Recently, graph neural networks have been used in this context, drawing direct inspiration from models for molecules. However, materials are typically much more structured than molecules, which is a feature that these m... | null | null |
A General Framework for Auditing Differentially Private Machine Learning | https://papers.nips.cc/paper_files/paper/2022/hash/1add3bbdbc20c403a383482a665eb5a4-Abstract-Conference.html | Fred Lu, Joseph Munoz, Maya Fuchs, Tyler LeBlond, Elliott Zaresky-Williams, Edward Raff, Francis Ferraro, Brian Testa | https://papers.nips.cc/paper_files/paper/2022/hash/1add3bbdbc20c403a383482a665eb5a4-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17922-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1add3bbdbc20c403a383482a665eb5a4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1add3bbdbc20c403a383482a665eb5a4-Supplemental-Conference.zip | We present a framework to statistically audit the privacy guarantee conferred by a differentially private machine learner in practice. While previous works have taken steps toward evaluating privacy loss through poisoning attacks or membership inference, they have been tailored to specific models or have demonstrated l... | null | null |
Generalization Analysis on Learning with a Concurrent Verifier | https://papers.nips.cc/paper_files/paper/2022/hash/1af83ab66b4f07a3f55788e67dab5782-Abstract-Conference.html | Masaaki Nishino, Kengo Nakamura, Norihito Yasuda | https://papers.nips.cc/paper_files/paper/2022/hash/1af83ab66b4f07a3f55788e67dab5782-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19439-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1af83ab66b4f07a3f55788e67dab5782-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1af83ab66b4f07a3f55788e67dab5782-Supplemental-Conference.zip | Machine learning technologies have been used in a wide range of practical systems.In practical situations, it is natural to expect the input-output pairs of a machine learning model to satisfy some requirements.However, it is difficult to obtain a model that satisfies requirements by just learning from examples.A simpl... | null | null |
Spartan: Differentiable Sparsity via Regularized Transportation | https://papers.nips.cc/paper_files/paper/2022/hash/1afb9ca4adf1d9cb3c87ff3e22a29049-Abstract-Conference.html | Kai Sheng Tai, Taipeng Tian, Ser Nam Lim | https://papers.nips.cc/paper_files/paper/2022/hash/1afb9ca4adf1d9cb3c87ff3e22a29049-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18052-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1afb9ca4adf1d9cb3c87ff3e22a29049-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1afb9ca4adf1d9cb3c87ff3e22a29049-Supplemental-Conference.pdf | We present Spartan, a method for training sparse neural network models with a predetermined level of sparsity. Spartan is based on a combination of two techniques: (1) soft top-k masking of low-magnitude parameters via a regularized optimal transportation problem and (2) dual averaging-based parameter updates with hard... | null | null |
Focal Modulation Networks | https://papers.nips.cc/paper_files/paper/2022/hash/1b08f585b0171b74d1401a5195e986f1-Abstract-Conference.html | Jianwei Yang, Chunyuan Li, Xiyang Dai, Jianfeng Gao | https://papers.nips.cc/paper_files/paper/2022/hash/1b08f585b0171b74d1401a5195e986f1-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17838-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1b08f585b0171b74d1401a5195e986f1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1b08f585b0171b74d1401a5195e986f1-Supplemental-Conference.zip | We propose focal modulation networks (FocalNets in short), where self-attention (SA) is completely replaced by a focal modulation module for modeling token interactions in vision. Focal modulation comprises three components: $(i)$ hierarchical contextualization, implemented using a stack of depth-wise convolutional lay... | null | null |
HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces | https://papers.nips.cc/paper_files/paper/2022/hash/1b115b1feab2198dd0881c57b869ddb7-Abstract-Conference.html | Qing Li, Yu-Shen Liu, Jin-San Cheng, Cheng Wang, Yi Fang, Zhizhong Han | https://papers.nips.cc/paper_files/paper/2022/hash/1b115b1feab2198dd0881c57b869ddb7-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17775-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1b115b1feab2198dd0881c57b869ddb7-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1b115b1feab2198dd0881c57b869ddb7-Supplemental-Conference.pdf | We propose a novel normal estimation method called HSurf-Net, which can accurately predict normals from point clouds with noise and density variations. Previous methods focus on learning point weights to fit neighborhoods into a geometric surface approximated by a polynomial function with a predefined order, based on w... | null | null |
Robust Streaming PCA | https://papers.nips.cc/paper_files/paper/2022/hash/1b11d918b08f781a6c194c6c522edfd6-Abstract-Conference.html | Daniel Bienstock, Minchan Jeong, Apurv Shukla, Se-Young Yun | https://papers.nips.cc/paper_files/paper/2022/hash/1b11d918b08f781a6c194c6c522edfd6-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16973-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1b11d918b08f781a6c194c6c522edfd6-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1b11d918b08f781a6c194c6c522edfd6-Supplemental-Conference.zip | We consider streaming principal component analysis when the stochastic data-generating model is subject to perturbations. While existing models assume a fixed covariance, we adopt a robust perspective where the covariance matrix belongs to a temporal uncertainty set. Under this setting, we provide fundamental limits on... | null | null |
NeMF: Neural Motion Fields for Kinematic Animation | https://papers.nips.cc/paper_files/paper/2022/hash/1b3750390ca8b931fb9ca988647940cb-Abstract-Conference.html | Chengan He, Jun Saito, James Zachary, Holly Rushmeier, Yi Zhou | https://papers.nips.cc/paper_files/paper/2022/hash/1b3750390ca8b931fb9ca988647940cb-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18876-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1b3750390ca8b931fb9ca988647940cb-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1b3750390ca8b931fb9ca988647940cb-Supplemental-Conference.zip | We present an implicit neural representation to learn the spatio-temporal space of kinematic motions. Unlike previous work that represents motion as discrete sequential samples, we propose to express the vast motion space as a continuous function over time, hence the name Neural Motion Fields (NeMF). Specifically, we u... | null | null |
Global Normalization for Streaming Speech Recognition in a Modular Framework | https://papers.nips.cc/paper_files/paper/2022/hash/1b4839ff1f843b6be059bd0e8437e975-Abstract-Conference.html | Ehsan Variani, Ke Wu, Michael D Riley, David Rybach, Matt Shannon, Cyril Allauzen | https://papers.nips.cc/paper_files/paper/2022/hash/1b4839ff1f843b6be059bd0e8437e975-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17135-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1b4839ff1f843b6be059bd0e8437e975-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1b4839ff1f843b6be059bd0e8437e975-Supplemental-Conference.pdf | We introduce the Globally Normalized Autoregressive Transducer (GNAT) for addressing the label bias problem in streaming speech recognition. Our solution admits a tractable exact computation of the denominator for the sequence-level normalization. Through theoretical and empirical results, we demonstrate that by switch... | null | null |
Resource-Adaptive Federated Learning with All-In-One Neural Composition | https://papers.nips.cc/paper_files/paper/2022/hash/1b61ad02f2da8450e08bb015638a9007-Abstract-Conference.html | Yiqun Mei, Pengfei Guo, Mo Zhou, Vishal Patel | https://papers.nips.cc/paper_files/paper/2022/hash/1b61ad02f2da8450e08bb015638a9007-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19136-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1b61ad02f2da8450e08bb015638a9007-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1b61ad02f2da8450e08bb015638a9007-Supplemental-Conference.pdf | Conventional Federated Learning (FL) systems inherently assume a uniform processing capacity among clients for deployed models. However, diverse client hardware often leads to varying computation resources in practice. Such system heterogeneity results in an inevitable trade-off between model complexity and data acces... | null | null |
SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression | https://papers.nips.cc/paper_files/paper/2022/hash/1b645a77cf48821afc3ee7e5b5d42617-Abstract-Conference.html | Zhize Li, Haoyu Zhao, Boyue Li, Yuejie Chi | https://papers.nips.cc/paper_files/paper/2022/hash/1b645a77cf48821afc3ee7e5b5d42617-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17742-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1b645a77cf48821afc3ee7e5b5d42617-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1b645a77cf48821afc3ee7e5b5d42617-Supplemental-Conference.pdf | To enable large-scale machine learning in bandwidth-hungry environments such as wireless networks, significant progress has been made recently in designing communication-efficient federated learning algorithms with the aid of communication compression. On the other end, privacy preserving, especially at the client leve... | null | null |
Your Transformer May Not be as Powerful as You Expect | https://papers.nips.cc/paper_files/paper/2022/hash/1ba5f64159d67775a251cf9ce386a2b9-Abstract-Conference.html | Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He | https://papers.nips.cc/paper_files/paper/2022/hash/1ba5f64159d67775a251cf9ce386a2b9-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17885-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1ba5f64159d67775a251cf9ce386a2b9-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1ba5f64159d67775a251cf9ce386a2b9-Supplemental-Conference.zip | Relative Positional Encoding (RPE), which encodes the relative distance between any pair of tokens, is one of the most successful modifications to the original Transformer. As far as we know, theoretical understanding of the RPE-based Transformers is largely unexplored. In this work, we mathematically analyze the power... | null | null |
Redundancy-Free Message Passing for Graph Neural Networks | https://papers.nips.cc/paper_files/paper/2022/hash/1bd6f17639876b4856026744932ec76f-Abstract-Conference.html | Rongqin Chen, Shenghui Zhang, Leong Hou U, Ye Li | https://papers.nips.cc/paper_files/paper/2022/hash/1bd6f17639876b4856026744932ec76f-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19416-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1bd6f17639876b4856026744932ec76f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1bd6f17639876b4856026744932ec76f-Supplemental-Conference.zip | Graph Neural Networks (GNNs) resemble the Weisfeiler-Lehman (1-WL) test, which iteratively update the representation of each node by aggregating information from WL-tree. However, despite the computational superiority of the iterative aggregation scheme, it introduces redundant message flows to encode nodes. We found t... | null | null |
Diffusion-LM Improves Controllable Text Generation | https://papers.nips.cc/paper_files/paper/2022/hash/1be5bc25d50895ee656b8c2d9eb89d6a-Abstract-Conference.html | Xiang Li, John Thickstun, Ishaan Gulrajani, Percy S. Liang, Tatsunori B. Hashimoto | https://papers.nips.cc/paper_files/paper/2022/hash/1be5bc25d50895ee656b8c2d9eb89d6a-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18733-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1be5bc25d50895ee656b8c2d9eb89d6a-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1be5bc25d50895ee656b8c2d9eb89d6a-Supplemental-Conference.pdf | Controlling the behavior of language models (LMs) without re-training is a major open problem in natural language generation. While recent works have demonstrated successes on controlling simple sentence attributes (e.g., sentiment), there has been little progress on complex, fine-grained controls (e.g., syntactic stru... | null | null |
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure | https://papers.nips.cc/paper_files/paper/2022/hash/1bed04feb85e5f02a7407fa3b191630b-Abstract-Conference.html | Paul Novello, Thomas FEL, David Vigouroux | https://papers.nips.cc/paper_files/paper/2022/hash/1bed04feb85e5f02a7407fa3b191630b-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19440-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1bed04feb85e5f02a7407fa3b191630b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1bed04feb85e5f02a7407fa3b191630b-Supplemental-Conference.pdf | This paper presents a new efficient black-box attribution method built on Hilbert-Schmidt Independence Criterion (HSIC). Based on Reproducing Kernel Hilbert Spaces (RKHS), HSIC measures the dependence between regions of an input image and the output of a model using the kernel embedding of their distributions. It thus ... | null | null |
Energy-Based Contrastive Learning of Visual Representations | https://papers.nips.cc/paper_files/paper/2022/hash/1bf03a03ca8fc5918fdcacb22e14c374-Abstract-Conference.html | Beomsu Kim, Jong Chul Ye | https://papers.nips.cc/paper_files/paper/2022/hash/1bf03a03ca8fc5918fdcacb22e14c374-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18566-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1bf03a03ca8fc5918fdcacb22e14c374-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1bf03a03ca8fc5918fdcacb22e14c374-Supplemental-Conference.zip | Contrastive learning is a method of learning visual representations by training Deep Neural Networks (DNNs) to increase the similarity between representations of positive pairs (transformations of the same image) and reduce the similarity between representations of negative pairs (transformations of different images). ... | null | null |
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power | https://papers.nips.cc/paper_files/paper/2022/hash/1c0d1b0734b0b94eff0acf0bbedfc671-Abstract-Conference.html | Binghui Li, Jikai Jin, Han Zhong, John Hopcroft, Liwei Wang | https://papers.nips.cc/paper_files/paper/2022/hash/1c0d1b0734b0b94eff0acf0bbedfc671-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18016-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1c0d1b0734b0b94eff0acf0bbedfc671-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1c0d1b0734b0b94eff0acf0bbedfc671-Supplemental-Conference.zip | It is well-known that modern neural networks are vulnerable to adversarial examples. To mitigate this problem, a series of robust learning algorithms have been proposed. However, although the robust training error can be near zero via some methods, all existing algorithms lead to a high robust generalization error. In ... | null | null |
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning | https://papers.nips.cc/paper_files/paper/2022/hash/1c153788756d35559c22d105d1182c30-Abstract-Conference.html | Yuchen Xiao, Weihao Tan, Christopher Amato | https://papers.nips.cc/paper_files/paper/2022/hash/1c153788756d35559c22d105d1182c30-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16907-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1c153788756d35559c22d105d1182c30-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1c153788756d35559c22d105d1182c30-Supplemental-Conference.zip | Synchronizing decisions across multiple agents in realistic settings is problematic since it requires agents to wait for other agents to terminate and communicate about termination reliably. Ideally, agents should learn and execute asynchronously instead. Such asynchronous methods also allow temporally extended actions... | null | null |
Polynomial Neural Fields for Subband Decomposition and Manipulation | https://papers.nips.cc/paper_files/paper/2022/hash/1c364d98a5cdc426fd8c76fbb2c10e34-Abstract-Conference.html | Guandao Yang, Sagie Benaim, Varun Jampani, Kyle Genova, Jonathan Barron, Thomas Funkhouser, Bharath Hariharan, Serge Belongie | https://papers.nips.cc/paper_files/paper/2022/hash/1c364d98a5cdc426fd8c76fbb2c10e34-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17355-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1c364d98a5cdc426fd8c76fbb2c10e34-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1c364d98a5cdc426fd8c76fbb2c10e34-Supplemental-Conference.pdf | Neural fields have emerged as a new paradigm for representing signals, thanks to their ability to do it compactly while being easy to optimize. In most applications, however, neural fields are treated like a black box, which precludes many signal manipulation tasks. In this paper, we propose a new class of neural field... | null | null |
On the Generalizability and Predictability of Recommender Systems | https://papers.nips.cc/paper_files/paper/2022/hash/1c446a652e50b1ea5618b66c07bfc0c5-Abstract-Conference.html | Duncan McElfresh, Sujay Khandagale, Jonathan Valverde, John Dickerson, Colin White | https://papers.nips.cc/paper_files/paper/2022/hash/1c446a652e50b1ea5618b66c07bfc0c5-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17617-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1c446a652e50b1ea5618b66c07bfc0c5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1c446a652e50b1ea5618b66c07bfc0c5-Supplemental-Conference.pdf | While other areas of machine learning have seen more and more automation, designing a high-performing recommender system still requires a high level of human effort. Furthermore, recent work has shown that modern recommender system algorithms do not always improve over well-tuned baselines. A natural follow-up question... | null | null |
Optimal Rates for Regularized Conditional Mean Embedding Learning | https://papers.nips.cc/paper_files/paper/2022/hash/1c71cd4032da425409d8ada8727bad42-Abstract-Conference.html | Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton | https://papers.nips.cc/paper_files/paper/2022/hash/1c71cd4032da425409d8ada8727bad42-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17083-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1c71cd4032da425409d8ada8727bad42-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1c71cd4032da425409d8ada8727bad42-Supplemental-Conference.pdf | We address the consistency of a kernel ridge regression estimate of the conditional mean embedding (CME), which is an embedding of the conditional distribution of $Y$ given $X$ into a target reproducing kernel Hilbert space $\mathcal{H}_Y$. The CME allows us to take conditional expectations of target RKHS functions, a... | null | null |
Divert More Attention to Vision-Language Tracking | https://papers.nips.cc/paper_files/paper/2022/hash/1c8c87c36dc1e49e63555f95fa56b153-Abstract-Conference.html | Mingzhe Guo, Zhipeng Zhang, Heng Fan, Liping Jing | https://papers.nips.cc/paper_files/paper/2022/hash/1c8c87c36dc1e49e63555f95fa56b153-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17103-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1c8c87c36dc1e49e63555f95fa56b153-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1c8c87c36dc1e49e63555f95fa56b153-Supplemental-Conference.zip | Relying on Transformer for complex visual feature learning, object tracking has witnessed the new standard for state-of-the-arts (SOTAs). However, this advancement accompanies by larger training data and longer training period, making tracking increasingly expensive. In this paper, we demonstrate that the Transformer-r... | null | null |
Rethinking Image Restoration for Object Detection | https://papers.nips.cc/paper_files/paper/2022/hash/1cac8326ce3fbe79171db9754211530c-Abstract-Conference.html | Shangquan Sun, Wenqi Ren, Tao Wang, Xiaochun Cao | https://papers.nips.cc/paper_files/paper/2022/hash/1cac8326ce3fbe79171db9754211530c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16747-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1cac8326ce3fbe79171db9754211530c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1cac8326ce3fbe79171db9754211530c-Supplemental-Conference.pdf | Although image restoration has achieved significant progress, its potential to assist object detectors in adverse imaging conditions lacks enough attention. It is reported that the existing image restoration methods cannot improve the object detector performance and sometimes even reduce the detection performance. To a... | null | null |
Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning | https://papers.nips.cc/paper_files/paper/2022/hash/1caf09c9f4e6b0150b06a07e77f2710c-Abstract-Conference.html | Elias Frantar, Dan Alistarh | https://papers.nips.cc/paper_files/paper/2022/hash/1caf09c9f4e6b0150b06a07e77f2710c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17808-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1caf09c9f4e6b0150b06a07e77f2710c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1caf09c9f4e6b0150b06a07e77f2710c-Supplemental-Conference.pdf | We consider the problem of model compression for deep neural networks (DNNs) in the challenging one-shot/post-training setting, in which we are given an accurate trained model, and must compress it without any retraining, based only on a small amount of calibration input data. This problem has become popular in view of... | null | null |
Challenging Common Assumptions in Convex Reinforcement Learning | https://papers.nips.cc/paper_files/paper/2022/hash/1cb5b3d64bdf3c6642c8d9a8fbecd019-Abstract-Conference.html | Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli | https://papers.nips.cc/paper_files/paper/2022/hash/1cb5b3d64bdf3c6642c8d9a8fbecd019-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17207-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1cb5b3d64bdf3c6642c8d9a8fbecd019-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1cb5b3d64bdf3c6642c8d9a8fbecd019-Supplemental-Conference.zip | The classic Reinforcement Learning (RL) formulation concerns the maximization of a scalar reward function. More recently, convex RL has been introduced to extend the RL formulation to all the objectives that are convex functions of the state distribution induced by a policy. Notably, convex RL covers several relevant a... | null | null |
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