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ORCID36th NeurIPS 2022: New Orleans, LA, USA
- Sanmi Koyejo, S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, A. Oh:
Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022. 2022, ISBN 9781713871088 - Yucheng Ding, Chaoyue Niu, Fan Wu, Shaojie Tang, Chengfei Lyu, Yanghe Feng, Guihai Chen:
Federated Submodel Optimization for Hot and Cold Data Features. - Xingyu Zhou, Bo Ji:
On Kernelized Multi-Armed Bandits with Constraints. - Seon-Ho Lee, Nyeong-Ho Shin, Chang-Su Kim:
Geometric Order Learning for Rank Estimation. - Changmin Yu, Hugo Soulat, Neil Burgess, Maneesh Sahani:
Structured Recognition for Generative Models with Explaining Away. - Yijian Qin, Ziwei Zhang, Xin Wang, Zeyang Zhang, Wenwu Zhu:
NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search. - Cian Naik, Judith Rousseau, Trevor Campbell:
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement. - Steven Stalder, Nathanaël Perraudin, Radhakrishna Achanta, Fernando Pérez-Cruz, Michele Volpi:
What You See is What You Classify: Black Box Attributions. - Martin Klissarov, Rasool Fakoor, Jonas W. Mueller, Kavosh Asadi, Taesup Kim, Alexander J. Smola:
Adaptive Interest for Emphatic Reinforcement Learning. - Dongze Lian, Daquan Zhou, Jiashi Feng, Xinchao Wang:
Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning. - Antoine Yang, Antoine Miech, Josef Sivic, Ivan Laptev, Cordelia Schmid:
Zero-Shot Video Question Answering via Frozen Bidirectional Language Models. - Yinglun Zhu, Robert Nowak:
Active Learning with Neural Networks: Insights from Nonparametric Statistics. - Yufei Guo, Yuanpei Chen, Liwen Zhang, Xiaode Liu, YingLei Wang, Xuhui Huang, Zhe Ma:
IM-Loss: Information Maximization Loss for Spiking Neural Networks. - Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta, Raja Marjieh, Michael Y. Hu, Robert D. Hawkins, Jonathan D. Cohen, Nathaniel D. Daw, Karthik Narasimhan, Tom Griffiths:
Using natural language and program abstractions to instill human inductive biases in machines. - Ruibo Liu, Chenyan Jia, Ge Zhang, Ziyu Zhuang, Tony X. Liu, Soroush Vosoughi:
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits. - Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David B. Lobell, Stefano Ermon:
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery. - Mathieu Even, Laurent Massoulié, Kevin Scaman:
On Sample Optimality in Personalized Collaborative and Federated Learning. - Wei-Cheng Tseng, Tsun-Hsuan Johnson Wang, Yen-Chen Lin, Phillip Isola:
Offline Multi-Agent Reinforcement Learning with Knowledge Distillation. - Shuoguang Yang, Xuezhou Zhang, Mengdi Wang:
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks. - Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto:
Conditional Meta-Learning of Linear Representations. - Peter Lippmann, Enrique Fita Sanmartín, Fred A. Hamprecht:
Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources. - Shichong Peng, Seyed Alireza Moazenipourasil, Ke Li:
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis. - Hao Lou, Tao Jin, Yue Wu, Pan Xu, Quanquan Gu, Farzad Farnoud:
Active Ranking without Strong Stochastic Transitivity. - Yecheng Jason Ma, Jason Yan, Dinesh Jayaraman, Osbert Bastani:
Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression. - Yiting Chen, Qibing Ren, Junchi Yan:
Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain. - Zhun Zhong, Yuyang Zhao, Gim Hee Lee, Nicu Sebe:
Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation. - Lue Fan, Feng Wang, Naiyan Wang, Zhaoxiang Zhang:
Fully Sparse 3D Object Detection. - Maximilian Augustin, Valentyn Boreiko, Francesco Croce, Matthias Hein:
Diffusion Visual Counterfactual Explanations. - Jingyun Liang, Yuchen Fan, Xiaoyu Xiang, Rakesh Ranjan, Eddy Ilg, Simon Green, Jiezhang Cao, Kai Zhang, Radu Timofte, Luc Van Gool:
Recurrent Video Restoration Transformer with Guided Deformable Attention. - Boxiang Wang, Archer Y. Yang:
A Consolidated Cross-Validation Algorithm for Support Vector Machines via Data Reduction. - Nika Haghtalab, Michael I. Jordan, Eric Zhao:
On-Demand Sampling: Learning Optimally from Multiple Distributions. - Konstantin Mishchenko, Francis R. Bach, Mathieu Even, Blake E. Woodworth:
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays. - Jiaxiang Chen, Qingyuan Yang, Ruomin Huang, Hu Ding:
Coresets for Relational Data and The Applications. - Kaiyang Guo, Yunfeng Shao, Yanhui Geng:
Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief. - Yu Meng, Jiaxin Huang, Yu Zhang, Jiawei Han:
Generating Training Data with Language Models: Towards Zero-Shot Language Understanding. - Florentin Guth, Simon Coste, Valentin De Bortoli, Stéphane Mallat:
Wavelet Score-Based Generative Modeling. - Chen Liu, Ziqi Zhao, Sabine Süsstrunk, Mathieu Salzmann:
Robust Binary Models by Pruning Randomly-initialized Networks. - Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux:
Why do tree-based models still outperform deep learning on typical tabular data? - Yuzhou Cao, Tianchi Cai, Lei Feng, Lihong Gu, Jinjie Gu, Bo An, Gang Niu, Masashi Sugiyama:
Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses. - Vivek F. Farias, Andrew A. Li, Tianyi Peng, Andrew Zheng:
Markovian Interference in Experiments. - Paul Rolland, Luca Viano, Norman Schürhoff, Boris Nikolov, Volkan Cevher:
Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning. - Nikola Surjanovic, Saifuddin Syed, Alexandre Bouchard-Côté, Trevor Campbell:
Parallel Tempering With a Variational Reference. - Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. - Rui Miao, Zhengling Qi, Xiaoke Zhang:
Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes under Non-Parametric Models. - Chaofei Wang, Qisen Yang, Rui Huang, Shiji Song, Gao Huang:
Efficient Knowledge Distillation from Model Checkpoints. - Teng Xiao, Zhengyu Chen, Zhimeng Guo, Zeyang Zhuang, Suhang Wang:
Decoupled Self-supervised Learning for Graphs. - Lujun Li, Zhe Jin:
Shadow Knowledge Distillation: Bridging Offline and Online Knowledge Transfer. - Limei Wang, Yi Liu, Yuchao Lin, Haoran Liu, Shuiwang Ji:
ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs. - Kaizhi Zheng, Xiaotong Chen, Odest Chadwicke Jenkins, Xin Eric Wang:
VLMbench: A Compositional Benchmark for Vision-and-Language Manipulation. - Jiawei Huang, Li Zhao, Tao Qin, Wei Chen, Nan Jiang, Tie-Yan Liu:
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret. - Sarah Sachs, Hédi Hadiji, Tim van Erven, Cristóbal Guzmán:
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness. - Jiayuan Ye, Reza Shokri:
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence). - Gabriel Cardoso, Sergey Samsonov, Achille Thin, Eric Moulines, Jimmy Olsson:
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling. - Luca Beurer-Kellner, Martin T. Vechev, Laurent Vanbever, Petar Velickovic:
Learning to Configure Computer Networks with Neural Algorithmic Reasoning. - Mingze Wang, Chao Ma:
Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks. - Balhae Kim, Jungwon Choi, Seanie Lee, Yoonho Lee, Jung-Woo Ha, Juho Lee:
On Divergence Measures for Bayesian Pseudocoresets. - Takeru Miyato, Masanori Koyama, Kenji Fukumizu:
Unsupervised Learning of Equivariant Structure from Sequences. - Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong:
Multi-Class $H$-Consistency Bounds. - Sameera Ramasinghe, Lachlan E. MacDonald, Simon Lucey:
On the Frequency-bias of Coordinate-MLPs. - Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh:
DC-BENCH: Dataset Condensation Benchmark. - Siyu Jiao, Gengwei Zhang, Shant Navasardyan, Ling Chen, Yao Zhao, Yunchao Wei, Honghui Shi:
Mask Matching Transformer for Few-Shot Segmentation. - Ilai Bistritz, Nicholas Bambos:
Queue Up Your Regrets: Achieving the Dynamic Capacity Region of Multiplayer Bandits. - Wei Dong, Yuting Liang, Ke Yi:
Differentially Private Covariance Revisited. - Pranjal Awasthi, Abhimanyu Das, Weihao Kong, Rajat Sen:
Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model. - Yuqin Yang, AmirEmad Ghassami, Mohamed S. Nafea, Negar Kiyavash, Kun Zhang, Ilya Shpitser:
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error. - Wenjian Huang, Hao Wang, Jiahao Xia, Chengyan Wang, Jianguo Zhang:
Density-driven Regularization for Out-of-distribution Detection. - Hananeh Aliee, Till Richter, Mikhail Solonin, Ignacio Ibarra, Fabian J. Theis, Niki Kilbertus:
Sparsity in Continuous-Depth Neural Networks. - Bo-Wei Huang, Keng-Te Liao, Chang-Sheng Kao, Shou-De Lin:
Environment Diversification with Multi-head Neural Network for Invariant Learning. - Mitch Hill, Erik Nijkamp, Jonathan Mitchell, Bo Pang, Song-Chun Zhu:
Learning Probabilistic Models from Generator Latent Spaces with Hat EBM. - Yuxin Zhang, Mingbao Lin, Zhihang Lin, Yiting Luo, Ke Li, Fei Chao, Yongjian Wu, Rongrong Ji:
Learning Best Combination for Efficient N: M Sparsity. - Yue Xing, Qifan Song, Guang Cheng:
Why Do Artificially Generated Data Help Adversarial Robustness. - Hongrui Cai, Wanquan Feng, Xuetao Feng, Yan Wang, Juyong Zhang:
Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera. - Jiayang Ren, Kaixun Hua, Yankai Cao:
Global Optimal K-Medoids Clustering of One Million Samples. - Shibo Li, Jeff M. Phillips, Xin Yu, Robert M. Kirby, Shandian Zhe:
Batch Multi-Fidelity Active Learning with Budget Constraints. - Janghyeon Lee, Jongsuk Kim, Hyounguk Shon, Bumsoo Kim, Seung Hwan Kim, Honglak Lee, Junmo Kim:
UniCLIP: Unified Framework for Contrastive Language-Image Pre-training. - Cong Guan, Feng Chen, Lei Yuan, Chenghe Wang, Hao Yin, Zongzhang Zhang, Yang Yu:
Efficient Multi-agent Communication via Self-supervised Information Aggregation. - Ramansh Sharma, Varun Shankar:
Accelerated Training of Physics-Informed Neural Networks (PINNs) using Meshless Discretizations. - Archana Bura, Aria HasanzadeZonuzy, Dileep Kalathil, Srinivas Shakkottai, Jean-François Chamberland:
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning. - Yeoneung Kim, Insoon Yang, Kwang-Sung Jun:
Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs. - Zhuoqing Song, Weijian Li, Kexin Jin, Lei Shi, Ming Yan, Wotao Yin, Kun Yuan:
Communication-Efficient Topologies for Decentralized Learning with $O(1)$ Consensus Rate. - Biru Zhu, Yujia Qin, Ganqu Cui, Yangyi Chen, Weilin Zhao, Chong Fu, Yangdong Deng, Zhiyuan Liu, Jingang Wang, Wei Wu, Maosong Sun, Ming Gu:
Moderate-fitting as a Natural Backdoor Defender for Pre-trained Language Models. - Songhua Liu, Kai Wang, Xingyi Yang, Jingwen Ye, Xinchao Wang:
Dataset Distillation via Factorization. - Ziping Xu, Eunjae Shim, Ambuj Tewari, Paul M. Zimmerman:
Adaptive Sampling for Discovery. - Lixin Zou, Haitao Mao, Xiaokai Chu, Jiliang Tang, Wenwen Ye, Shuaiqiang Wang, Dawei Yin:
A Large Scale Search Dataset for Unbiased Learning to Rank. - Meng-Hao Guo, Cheng-Ze Lu, Qibin Hou, Zhengning Liu, Ming-Ming Cheng, Shi-Min Hu:
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation. - Tao Yu, Yichi Zhang, Zhiru Zhang, Christopher De Sa:
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning. - Qin Ding, Yue Kang, Yi-Wei Liu, Thomas Chun Man Lee, Cho-Jui Hsieh, James Sharpnack:
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms. - Neil Mallinar, James B. Simon, Amirhesam Abedsoltan, Parthe Pandit, Misha Belkin, Preetum Nakkiran:
Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting. - Yuanhao Ban, Yinpeng Dong:
Pre-trained Adversarial Perturbations. - Jinkun Cao, Ruiqian Nai, Qing Yang, Jialei Huang, Yang Gao:
An Empirical Study on Disentanglement of Negative-free Contrastive Learning. - Mo Tiwari, Ryan Kang, Jaeyong Lee, Chris Piech, Ilan Shomorony, Sebastian Thrun, Martin J. Zhang:
MABSplit: Faster Forest Training Using Multi-Armed Bandits. - Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong:
Counterfactual Fairness with Partially Known Causal Graph. - Jose Gallego-Posada, Juan Ramirez, Akram Erraqabi, Yoshua Bengio, Simon Lacoste-Julien:
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints. - Rishi Saket:
Algorithms and Hardness for Learning Linear Thresholds from Label Proportions. - Luca Pinchetti, Tommaso Salvatori, Yordan Yordanov, Beren Millidge, Yuhang Song, Thomas Lukasiewicz:
Predictive Coding beyond Gaussian Distributions. - Nived Rajaraman, Devvrit, Pranjal Awasthi:
Semi-supervised Active Linear Regression. - Avrim Blum, Omar Montasser, Greg Shakhnarovich, Hongyang Zhang:
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness. - Sanket Shah, Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe:
Decision-Focused Learning without Decision-Making: Learning Locally Optimized Decision Losses. - Cristopher Salvi, Maud Lemercier, Andris Gerasimovics:
Neural Stochastic PDEs: Resolution-Invariant Learning of Continuous Spatiotemporal Dynamics. - Myles Bartlett, Sara Romiti, Viktoriia Sharmanska, Novi Quadrianto:
Okapi: Generalising Better by Making Statistical Matches Match. - Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, Doina Precup:
Revisiting Heterophily For Graph Neural Networks. - Botao Yu, Peiling Lu, Rui Wang, Wei Hu, Xu Tan, Wei Ye, Shikun Zhang, Tao Qin, Tie-Yan Liu:
Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation. - Mathieu Rita, Corentin Tallec, Paul Michel, Jean-Bastien Grill, Olivier Pietquin, Emmanuel Dupoux, Florian Strub:
Emergent Communication: Generalization and Overfitting in Lewis Games. - Haoli Bai, Lu Hou, Lifeng Shang, Xin Jiang, Irwin King, Michael R. Lyu:
Towards Efficient Post-training Quantization of Pre-trained Language Models. - Shubhanshu Mishra, Aman Saini, Raheleh Makki, Sneha Mehta, Aria Haghighi, Ali Mollahosseini:
TweetNERD - End to End Entity Linking Benchmark for Tweets. - Sung Woo Park, Hyomin Kim, Kyungjae Lee, Junseok Kwon:
Riemannian Neural SDE: Learning Stochastic Representations on Manifolds. - Marta R. Costa-jussà, Christine Basta, Oriol Domingo, André Rubungo:
OccGen: Selection of Real-world Multilingual Parallel Data Balanced in Gender within Occupations. - Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Learning in Observable POMDPs, without Computationally Intractable Oracles. - Tingliang Feng, Wei Feng, Weiqi Li, Di Lin:
Cross-Image Context for Single Image Inpainting. - Sravanti Addepalli, Samyak Jain, Venkatesh Babu R.:
Efficient and Effective Augmentation Strategy for Adversarial Training. - Tongda Xu, Yan Wang, Dailan He, Chenjian Gao, Han Gao, Kunzan Liu, Hongwei Qin:
Multi-Sample Training for Neural Image Compression. - Yifan Yang, Yang Liu, Parinaz Naghizadeh:
Adaptive Data Debiasing through Bounded Exploration. - Manzil Zaheer, Kenneth Marino, Will Grathwohl, John Schultz, Wendy Shang, Sheila Babayan, Arun Ahuja, Ishita Dasgupta, Christine Kaeser-Chen, Rob Fergus:
Learning to Navigate Wikipedia by Taking Random Walks. - David Brandfonbrener, Alberto Bietti, Jacob Buckman, Romain Laroche, Joan Bruna:
When does return-conditioned supervised learning work for offline reinforcement learning? - Qi Lyu, Xiao Fu:
Provable Subspace Identification Under Post-Nonlinear Mixtures. - Wenqi Yang, Guanying Chen, Chaofeng Chen, Zhenfang Chen, Kwan-Yee K. Wong:
S3-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint. - Arindam Ghosh, Thomas Schaaf, Matthew R. Gormley:
AdaFocal: Calibration-aware Adaptive Focal Loss. - Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert:
PDEBench: An Extensive Benchmark for Scientific Machine Learning. - Arnav Kumar Jain, Shivakanth Sujit, Shruti Joshi, Vincent Michalski, Danijar Hafner, Samira Ebrahimi Kahou:
Learning Robust Dynamics through Variational Sparse Gating. - Ghada Sokar, Zahra Atashgahi, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Where to Pay Attention in Sparse Training for Feature Selection? - Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm:
Maximizing Revenue under Market Shrinkage and Market Uncertainty. - Huan Zhang, Shiqi Wang, Kaidi Xu, Linyi Li, Bo Li, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter:
General Cutting Planes for Bound-Propagation-Based Neural Network Verification. - Aakash Kaku, Kangning Liu, Avinash Parnandi, Haresh Rengaraj Rajamohan, Kannan Venkataramanan, Anita Venkatesan, Audre Wirtanen, Natasha Pandit, Heidi M. Schambra, Carlos Fernandez-Granda:
StrokeRehab: A Benchmark Dataset for Sub-second Action Identification. - Yossi Azar, Amos Fiat, Federico Fusco:
An $\alpha$-regret analysis of Adversarial Bilateral Trade. - Mingyu Yang, Jian Zhao, Xunhan Hu, Wengang Zhou, Jiangcheng Zhu, Houqiang Li:
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning. - Jiafei Lyu, Xiaoteng Ma, Xiu Li, Zongqing Lu:
Mildly Conservative Q-Learning for Offline Reinforcement Learning. - Yining Chen, Elan Rosenfeld, Mark Sellke, Tengyu Ma, Andrej Risteski:
Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments. - Hongwei Jin, Zishun Yu, Xinhua Zhang:
Certifying Robust Graph Classification under Orthogonal Gromov-Wasserstein Threats. - Coby Penso, Idan Achituve, Ethan Fetaya:
Functional Ensemble Distillation. - Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar:
Use-Case-Grounded Simulations for Explanation Evaluation. - Wenxiao Wang, Alexander Levine, Soheil Feizi:
Lethal Dose Conjecture on Data Poisoning. - Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar:
Online Decision Mediation. - Zezhong Xu, Wen Zhang, Peng Ye, Hui Chen, Huajun Chen:
Neural-Symbolic Entangled Framework for Complex Query Answering. - Tairan He, Yuge Zhang, Kan Ren, Minghuan Liu, Che Wang, Weinan Zhang, Yuqing Yang, Dongsheng Li:
Reinforcement Learning with Automated Auxiliary Loss Search. - Xiao-Yang Liu, Ziyi Xia, Jingyang Rui, Jiechao Gao, Hongyang Yang, Ming Zhu, Christina Dan Wang, Zhaoran Wang, Jian Guo:
FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning. - Klim Zaporojets, Lucie-Aimée Kaffee, Johannes Deleu, Thomas Demeester, Chris Develder, Isabelle Augenstein:
TempEL: Linking Dynamically Evolving and Newly Emerging Entities. - Pingyi Hu, Zihan Wang, Ruoxi Sun, Hu Wang, Minhui Xue:
M$^4$I: Multi-modal Models Membership Inference. - Aldo Pacchiano, Christoph Dann, Claudio Gentile:
Best of Both Worlds Model Selection. - Peter Kocsis, Peter Súkeník, Guillem Brasó, Matthias Nießner, Laura Leal-Taixé, Ismail Elezi:
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes. - Tianxin Wei, Yuning You, Tianlong Chen, Yang Shen, Jingrui He, Zhangyang Wang:
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative. - Runyu Zhang, Jincheng Mei, Bo Dai, Dale Schuurmans, Na Li:
On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games. - Minsu Kim, Junyoung Park, Jinkyoo Park:
Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization. - Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, Colin Raffel:
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning. - Yiqun Wang, Ivan Skorokhodov, Peter Wonka:
HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details. - Lucas Monteiro Paes, Carol Xuan Long, Berk Ustun, Flávio P. Calmon:
On the Epistemic Limits of Personalized Prediction. - Zeyu Yang, Jiaqi Chen, Zhenwei Miao, Wei Li, Xiatian Zhu, Li Zhang:
DeepInteraction: 3D Object Detection via Modality Interaction. - Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen:
Deep Differentiable Logic Gate Networks. - Parth Thaker, Mohit Malu, Nikhil Rao, Gautam Dasarathy:
Maximizing and Satisficing in Multi-armed Bandits with Graph Information. - Yunsong Zhou, Quan Liu, Hongzi Zhu, Yunzhe Li, Shan Chang, Minyi Guo:
MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation. - Harikrishnan N. B., Aditi Kathpalia, Nithin Nagaraj:
Causality Preserving Chaotic Transformation and Classification using Neurochaos Learning. - Shurui Gui, Xiner Li, Limei Wang, Shuiwang Ji:
GOOD: A Graph Out-of-Distribution Benchmark. - Siddhant Kharbanda, Atmadeep Banerjee, Erik Schultheis, Rohit Babbar:
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification. - Jiawei Jiang, Lukas Burkhalter, Fangcheng Fu, Bolin Ding, Bo Du, Anwar Hithnawi, Bo Li, Ce Zhang:
VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely? - Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun:
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits. - Anupam Gupta, Debmalya Panigrahi, Bernardo Subercaseaux, Kevin Sun:
Augmenting Online Algorithms with $\varepsilon$-Accurate Predictions. - Laurynas Karazija, Subhabrata Choudhury, Iro Laina, Christian Rupprecht, Andrea Vedaldi:
Unsupervised Multi-Object Segmentation by Predicting Probable Motion Patterns. - Ashwinkumar Badanidiyuru Varadaraja, Zhe Feng, Tianxi Li, Haifeng Xu:
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards. - Jiaxing Huang, Kaiwen Cui, Dayan Guan, Aoran Xiao, Fangneng Zhan, Shijian Lu, Shengcai Liao, Eric P. Xing:
Masked Generative Adversarial Networks are Data-Efficient Generation Learners. - Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu, Jaroslaw Blasiok, Preetum Nakkiran:
What You See is What You Get: Principled Deep Learning via Distributional Generalization. - Hanxu Zhou, Qixuan Zhou, Tao Luo, Yaoyu Zhang, Zhi-Qin John Xu:
Towards Understanding the Condensation of Neural Networks at Initial Training. - Chenxin An, Jiangtao Feng, Kai Lv, Lingpeng Kong, Xipeng Qiu, Xuanjing Huang:
CoNT: Contrastive Neural Text Generation. - Yifei Zhou, Renyu Li, Hayden Housen, Ser Nam Lim:
GAPX: Generalized Autoregressive Paraphrase-Identification X. - Yanzhi Chen, Weihao Sun, Yingzhen Li, Adrian Weller:
Scalable Infomin Learning. - Tailin Wu, Takashi Maruyama, Jure Leskovec:
Learning to Accelerate Partial Differential Equations via Latent Global Evolution. - Albert Wilcox, Ashwin Balakrishna, Jules Dedieu, Wyame Benslimane, Daniel S. Brown, Ken Goldberg:
Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations. - Nicolas Keriven:
Not too little, not too much: a theoretical analysis of graph (over)smoothing. - Kyle Luther, H. Sebastian Seung:
Kernel similarity matching with Hebbian networks. - Haoran Wei, Ping Guo, Yangguang Zhu, Chenglong Liu, Peng Wang:
HumanLiker: A Human-like Object Detector to Model the Manual Labeling Process. - Andrea Tirinzoni, Matteo Papini, Ahmed Touati, Alessandro Lazaric, Matteo Pirotta:
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees. - Tianqi Wei, Rana Alkhoury Maroun, Qinghai Guo, Barbara Webb:
DevFly: Bio-Inspired Development of Binary Connections for Locality Preserving Sparse Codes. - Benoit Dherin, Michael Munn, Mihaela Rosca, David Barrett:
Why neural networks find simple solutions: The many regularizers of geometric complexity. - Zhouxing Shi, Yihan Wang, Huan Zhang, J. Zico Kolter, Cho-Jui Hsieh:
Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation. - Sander Beckers, Hana Chockler, Joseph Y. Halpern:
A Causal Analysis of Harm. - Ran Liu, Mehdi Azabou, Max Dabagia, Jingyun Xiao, Eva L. Dyer:
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers. - Arman Zharmagambetov, Miguel Á. Carreira-Perpiñán:
Semi-Supervised Learning with Decision Trees: Graph Laplacian Tree Alternating Optimization. - Valentin De Bortoli, Emile Mathieu, Michael J. Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet:
Riemannian Score-Based Generative Modelling. - Chen Yan, Federico Carnevale, Petko Georgiev, Adam Santoro, Aurelia Guy, Alistair Muldal, Chia-Chun Hung, Josh Abramson, Timothy P. Lillicrap, Gregory Wayne:
Intra-agent speech permits zero-shot task acquisition. - Reda Chhaibi, Tariq Daouda, Ezechiel Kahn:
Free Probability for predicting the performance of feed-forward fully connected neural networks. - Shilong Bao, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang:
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm. - Robert Meier, Asier Mujika:
Open-Ended Reinforcement Learning with Neural Reward Functions. - Ibrahim M. Alabdulmohsin, Jessica Schrouff, Sanmi Koyejo:
A Reduction to Binary Approach for Debiasing Multiclass Datasets. - Zhiyang Chen, Yousong Zhu, Zhaowen Li, Fan Yang, Wei Li, Haixin Wang, Chaoyang Zhao, Liwei Wu, Rui Zhao, Jinqiao Wang, Ming Tang:
Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks. - Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, Ashwin Kalyan:
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering. - Sagnik Majumder, Changan Chen, Ziad Al-Halah, Kristen Grauman:
Few-Shot Audio-Visual Learning of Environment Acoustics. - Tom Schaul, André Barreto, John Quan, Georg Ostrovski:
The Phenomenon of Policy Churn. - Xiangzhe Kong, Wenbing Huang, Zhixing Tan, Yang Liu:
Molecule Generation by Principal Subgraph Mining and Assembling. - Zekun Hao, Arun Mallya, Serge J. Belongie, Ming-Yu Liu:
Implicit Neural Representations with Levels-of-Experts. - Zhao-Heng Yin, Weirui Ye, Qifeng Chen, Yang Gao:
Planning for Sample Efficient Imitation Learning. - Jonathan Crabbé, Mihaela van der Schaar:
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations. - Haonan Yu, Wei Xu, Haichao Zhang:
Towards Safe Reinforcement Learning with a Safety Editor Policy. - Jaehoon Oh, Sungnyun Kim, Namgyu Ho, Jin-Hwa Kim, Hwanjun Song, Se-Young Yun:
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty. - Kate Sanders, Reno Kriz, Anqi Liu, Benjamin Van Durme:
Ambiguous Images With Human Judgments for Robust Visual Event Classification. - Zhiyu Mou, Yusen Huo, Rongquan Bai, Mingzhou Xie, Chuan Yu, Jian Xu, Bo Zheng:
Sustainable Online Reinforcement Learning for Auto-bidding. - Jinguo Zhu, Xizhou Zhu, Wenhai Wang, Xiaohua Wang, Hongsheng Li, Xiaogang Wang, Jifeng Dai:
Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs. - Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn, Omar Ali Sheikh-Omar:
Improved Coresets for Euclidean k-Means. - Zhijie Deng, Feng Zhou, Jun Zhu:
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning. - Emmanuel Abbe, Samy Bengio, Elisabetta Cornacchia, Jon M. Kleinberg, Aryo Lotfi, Maithra Raghu, Chiyuan Zhang:
Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures. - Loay Mualem, Moran Feldman:
Using Partial Monotonicity in Submodular Maximization. - Desik Rengarajan, Sapana Chaudhary, Jaewon Kim, Dileep Kalathil, Srinivas Shakkottai:
Enhanced Meta Reinforcement Learning via Demonstrations in Sparse Reward Environments. - Chin-Wei Huang, Milad Aghajohari, Joey Bose, Prakash Panangaden, Aaron C. Courville:
Riemannian Diffusion Models. - Andy Shih, Dorsa Sadigh, Stefano Ermon:
Training and Inference on Any-Order Autoregressive Models the Right Way. - Shinichi Hemmi, Taihei Oki, Shinsaku Sakaue, Kaito Fujii, Satoru Iwata:
Lazy and Fast Greedy MAP Inference for Determinantal Point Process. - Sejun Park, Umut Simsekli, Murat A. Erdogdu:
Generalization Bounds for Stochastic Gradient Descent via Localized $\varepsilon$-Covers. - Ying Jin, Jiaqi Wang, Dahua Lin:
Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant. - Michael Lindon, Alan Malek:
Anytime-Valid Inference For Multinomial Count Data. - Julien Colin, Thomas Fel, Rémi Cadène, Thomas Serre:
What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods. - Eric Nguyen, Karan Goel, Albert Gu, Gordon W. Downs, Preey Shah, Tri Dao, Stephen Baccus, Christopher Ré:
S4ND: Modeling Images and Videos as Multidimensional Signals with State Spaces. - Jieyu Zhang, Haonan Wang, Cheng-Yu Hsieh, Alexander J. Ratner:
Understanding Programmatic Weak Supervision via Source-aware Influence Function. - Guohao Shen, Yuling Jiao, Yuanyuan Lin, Jian Huang:
Approximation with CNNs in Sobolev Space: with Applications to Classification. - Shinsaku Sakaue, Taihei Oki:
Sample Complexity of Learning Heuristic Functions for Greedy-Best-First and A* Search. - Zifeng Wang, Jimeng Sun:
TransTab: Learning Transferable Tabular Transformers Across Tables. - Weixia Zhang, Dingquan Li, Xiongkuo Min, Guangtao Zhai, Guodong Guo, Xiaokang Yang, Kede Ma:
Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop. - Mucong Ding, Tahseen Rabbani, Bang An, Evan Wang, Furong Huang:
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity. - Ching-Yao Chuang, Stefanie Jegelka:
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks. - Yu Shen, Yupeng Lu, Yang Li, Yaofeng Tu, Wentao Zhang, Bin Cui:
DivBO: Diversity-aware CASH for Ensemble Learning. - Nian Liu, Xiao Wang, Deyu Bo, Chuan Shi, Jian Pei:
Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum. - Kha Pham, Hung Le, Man Ngo, Truyen Tran:
Functional Indirection Neural Estimator for Better Out-of-distribution Generalization. - Qingsong Liu, Weihang Xu, Siwei Wang, Zhixuan Fang:
Combinatorial Bandits with Linear Constraints: Beyond Knapsacks and Fairness. - Kaiyi Ji, Mingrui Liu, Yingbin Liang, Lei Ying:
Will Bilevel Optimizers Benefit from Loops. - Dan Zhao:
Combining Explicit and Implicit Regularization for Efficient Learning in Deep Networks. - Mosam Dabhi, Chaoyang Wang, Tim Clifford, László A. Jeni, Ian R. Fasel, Simon Lucey:
MBW: Multi-view Bootstrapping in the Wild. - Yifan Feng, Yuxuan Tang:
On A Mallows-type Model For (Ranked) Choices. - Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
(De-)Randomized Smoothing for Decision Stump Ensembles. - Jin Xu, Xiaojiang Liu, Jianhao Yan, Deng Cai, Huayang Li, Jian Li:
Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation. - Qizhao Chen, Vasilis Syrgkanis, Morgane Austern:
Debiased Machine Learning without Sample-Splitting for Stable Estimators. - Sharan Vaswani, Lin Yang, Csaba Szepesvári:
Near-Optimal Sample Complexity Bounds for Constrained MDPs. - Ron Amit, Baruch Epstein, Shay Moran, Ron Meir:
Integral Probability Metrics PAC-Bayes Bounds. - Andrea Zanette, Martin J. Wainwright:
Bellman Residual Orthogonalization for Offline Reinforcement Learning. - Tongyang Li, Ruizhe Zhang:
Quantum Speedups of Optimizing Approximately Convex Functions with Applications to Logarithmic Regret Stochastic Convex Bandits. - Andrew F. Luo, Yilun Du, Michael J. Tarr, Josh Tenenbaum, Antonio Torralba, Chuang Gan:
Learning Neural Acoustic Fields. - Giulia Bernardini, Alexander Lindermayr, Alberto Marchetti-Spaccamela, Nicole Megow, Leen Stougie, Michelle Sweering:
A Universal Error Measure for Input Predictions Applied to Online Graph Problems. - Junzhe Zhang, Elias Bareinboim:
Online Reinforcement Learning for Mixed Policy Scopes. - Seungeon Lee, Xiting Wang, Sungwon Han, Xiaoyuan Yi, Xing Xie, Meeyoung Cha:
Self-explaining deep models with logic rule reasoning. - Xiaoxia Wu, Zhewei Yao, Minjia Zhang, Conglong Li, Yuxiong He:
XTC: Extreme Compression for Pre-trained Transformers Made Simple and Efficient. - Ihsan Ullah, Dustin Carrión-Ojeda, Sergio Escalera, Isabelle Guyon, Mike Huisman, Felix Mohr, Jan N. van Rijn, Haozhe Sun, Joaquin Vanschoren, Phan Anh Vu:
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification. - Devvrit, Aditya Sinha, Inderjit S. Dhillon, Prateek Jain:
S3GC: Scalable Self-Supervised Graph Clustering. - Benjamin Kurt Miller, Christoph Weniger, Patrick Forré:
Contrastive Neural Ratio Estimation. - Hong Jun Jeon, Benjamin Van Roy:
An Information-Theoretic Framework for Deep Learning. - Ioannis Anagnostides, Gabriele Farina, Christian Kroer, Chung-Wei Lee, Haipeng Luo, Tuomas Sandholm:
Uncoupled Learning Dynamics with O(log T) Swap Regret in Multiplayer Games. - Lan-Zhe Guo, Yi-Ge Zhang, Zhi-Fan Wu, Jie-Jing Shao, Yufeng Li:
Robust Semi-Supervised Learning when Not All Classes have Labels. - Hui Lu, Mia Chiquier, Carl Vondrick:
Private Multiparty Perception for Navigation. - Long-Kai Huang, Ying Wei:
Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization. - Baoxiong Jia, Ting Lei, Song-Chun Zhu, Siyuan Huang:
EgoTaskQA: Understanding Human Tasks in Egocentric Videos. - Huaxiu Yao, Yiping Wang, Linjun Zhang, James Y. Zou, Chelsea Finn:
C-Mixup: Improving Generalization in Regression. - Louis Ohl, Pierre-Alexandre Mattei, Charles Bouveyron, Warith Harchaoui, Mickaël Leclercq, Arnaud Droit, Frédéric Precioso:
Generalised Mutual Information for Discriminative Clustering. - Yutong Wang, Clayton Scott:
Consistent Interpolating Ensembles via the Manifold-Hilbert Kernel. - Qiancheng Fu, Qingshan Xu, Yew Soon Ong, Wenbing Tao:
Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction. - Arpit Agarwal, Sanjeev Khanna, Huan Li, Prathamesh Patil:
Sublinear Algorithms for Hierarchical Clustering. - Soroush Ebadian, Gregory Kehne, Evi Micha, Ariel D. Procaccia, Nisarg Shah:
Is Sortition Both Representative and Fair? - Shayegan Omidshafiei, Andrei Kapishnikov, Yannick Assogba, Lucas Dixon, Been Kim:
Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis. - Noémie Périvier, Vineet Goyal:
Dynamic pricing and assortment under a contextual MNL demand. - Marie Maros, Gesualdo Scutari:
DGD^2: A Linearly Convergent Distributed Algorithm For High-dimensional Statistical Recovery. - Bo Xiong, Shichao Zhu, Nico Potyka, Shirui Pan, Chuan Zhou, Steffen Staab:
Pseudo-Riemannian Graph Convolutional Networks. - Philippe Weinzaepfel, Vincent Leroy, Thomas Lucas, Romain Brégier, Yohann Cabon, Vaibhav Arora, Leonid Antsfeld, Boris Chidlovskii, Gabriela Csurka, Jérôme Revaud:
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion. - Elías Abad-Rocamora, Mehmet Fatih Sahin, Fanghui Liu, Grigorios Chrysos, Volkan Cevher:
Sound and Complete Verification of Polynomial Networks. - Hoang Tran, Ashok Cutkosky:
Better SGD using Second-order Momentum. - Misha Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii:
Learning Predictions for Algorithms with Predictions. - Changfeng Ma, Yang Yang, Jie Guo, Fei Pan, Chongjun Wang, Yanwen Guo:
Unsupervised Point Cloud Completion and Segmentation by Generative Adversarial Autoencoding Network. - Chen Chen, Yuchen Liu, Xingjun Ma, Lingjuan Lyu:
CalFAT: Calibrated Federated Adversarial Training with Label Skewness. - Markus Hiller, Rongkai Ma, Mehrtash Harandi, Tom Drummond:
Rethinking Generalization in Few-Shot Classification. - Peng Ye, Shengji Tang, Baopu Li, Tao Chen, Wanli Ouyang:
Stimulative Training of Residual Networks: A Social Psychology Perspective of Loafing. - Min Zhao, Fan Bao, Chongxuan Li, Jun Zhu:
EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations. - Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren:
Cryptographic Hardness of Learning Halfspaces with Massart Noise. - Lior Danon, Dan Garber:
Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator. - Matthias Schultheis, Constantin A. Rothkopf, Heinz Koeppl:
Reinforcement Learning with Non-Exponential Discounting. - Rishi Bommasani, Kathleen A. Creel, Ananya Kumar, Dan Jurafsky, Percy Liang:
Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization? - Amin Jaber, Adèle H. Ribeiro, Jiji Zhang, Elias Bareinboim:
Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness. - Abishek Thangamuthu, Gunjan Kumar, Suresh Bishnoi, Ravinder Bhattoo, N. M. Anoop Krishnan, Sayan Ranu:
Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems. - Gerdus Benadè, Daniel Halpern, Alexandros Psomas:
Dynamic Fair Division with Partial Information. - Veit D. Wild, Robert Hu, Dino Sejdinovic:
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning. - Samuel Acquaviva, Yewen Pu, Marta Kryven, Theodoros Sechopoulos, Catherine Wong, Gabrielle E. Ecanow, Maxwell I. Nye, Michael Henry Tessler, Josh Tenenbaum:
Communicating Natural Programs to Humans and Machines. - Daniel McDuff, Miah Wander, Xin Liu, Brian L. Hill, Javier Hernandez, Jonathan Lester, Tadas Baltrusaitis:
SCAMPS: Synthetics for Camera Measurement of Physiological Signals. - James Harrison, Luke Metz, Jascha Sohl-Dickstein:
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases. - Lingyu Gu, Yongqi Du, Yuan Zhang, Di Xie, Shiliang Pu, Robert C. Qiu, Zhenyu Liao:
"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach. - Jason M. Altschuler, Kunal Talwar:
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss. - Luis Pineda, Taosha Fan, Maurizio Monge, Shobha Venkataraman, Paloma Sodhi, Ricky T. Q. Chen, Joseph Ortiz, Daniel DeTone, Austin S. Wang, Stuart Anderson, Jing Dong, Brandon Amos, Mustafa Mukadam:
Theseus: A Library for Differentiable Nonlinear Optimization. - Dong-Hee Paek, Seung-Hyun Kong, Kevin Tirta Wijaya:
K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions. - Xin-Chun Li, Wen-Shu Fan, Shaoming Song, Yinchuan Li, Bingshuai Li, Yunfeng Shao, De-Chuan Zhan:
Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again. - Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay V. Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, Vedant Misra:
Solving Quantitative Reasoning Problems with Language Models. - Philip de Rijk, Lukas Schneider, Marius Cordts, Dariu Gavrila:
Structural Knowledge Distillation for Object Detection. - Mohamad Kazem Shirani Faradonbeh, Mohamad Sadegh Shirani Faradonbeh, Mohsen Bayati:
Thompson Sampling Efficiently Learns to Control Diffusion Processes. - Riashat Islam, Hongyu Zang, Anirudh Goyal, Alex Lamb, Kenji Kawaguchi, Xin Li, Romain Laroche, Yoshua Bengio, Remi Tachet des Combes:
Discrete Compositional Representations as an Abstraction for Goal Conditioned Reinforcement Learning. - Leyan Deng, Defu Lian, Chenwang Wu, Enhong Chen:
Graph Convolution Network based Recommender Systems: Learning Guarantee and Item Mixture Powered Strategy. - Haanvid Lee, Jongmin Lee, Yunseon Choi, Wonseok Jeon, Byung-Jun Lee, Yung-Kyun Noh, Kee-Eung Kim:
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions. - Yuekun Dai, Chongyi Li, Shangchen Zhou, Ruicheng Feng, Chen Change Loy:
Flare7K: A Phenomenological Nighttime Flare Removal Dataset. - Yidong Wang, Hao Chen, Yue Fan, Wang Sun, Ran Tao, Wenxin Hou, Renjie Wang, Linyi Yang, Zhi Zhou, Lan-Zhe Guo, Heli Qi, Zhen Wu, Yufeng Li, Satoshi Nakamura, Wei Ye, Marios Savvides, Bhiksha Raj, Takahiro Shinozaki, Bernt Schiele, Jindong Wang, Xing Xie, Yue Zhang:
USB: A Unified Semi-supervised Learning Benchmark for Classification. - Eugene Vinitsky, Nathan Lichtlé, Xiaomeng Yang, Brandon Amos, Jakob Foerster:
Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world. - Chengchang Liu, Luo Luo:
Quasi-Newton Methods for Saddle Point Problems. - Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, Marinka Zitnik:
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency. - Kailas Vodrahalli, Tobias Gerstenberg, James Y. Zou:
Uncalibrated Models Can Improve Human-AI Collaboration. - Vladimir Kostic, Pietro Novelli, Andreas Maurer, Carlo Ciliberto, Lorenzo Rosasco, Massimiliano Pontil:
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces. - Jung-Hee Kim, Junhwa Hur, Tien Phuoc Nguyen, Seong-Gyun Jeong:
Self-supervised surround-view depth estimation with volumetric feature fusion. - Bonifaz Stuhr, Johann Haselberger, Julian Gebele:
CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World Domains. - Markus Hiller, Mehrtash Harandi, Tom Drummond:
On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation. - Nika Haghtalab, Yanjun Han, Abhishek Shetty, Kunhe Yang:
Oracle-Efficient Online Learning for Smoothed Adversaries. - Haoran Xu, Li Jiang, Jianxiong Li, Xianyuan Zhan:
A Policy-Guided Imitation Approach for Offline Reinforcement Learning. - Ziang Song, Song Mei, Yu Bai:
Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games. - Selena Ling, Nicholas Sharp, Alec Jacobson:
VectorAdam for Rotation Equivariant Geometry Optimization. - Rahul Jain, Georgios Piliouras, Ryann Sim:
Matrix Multiplicative Weights Updates in Quantum Zero-Sum Games: Conservation Laws & Recurrence. - Daouda Sow, Kaiyi Ji, Yingbin Liang:
On the Convergence Theory for Hessian-Free Bilevel Algorithms. - Sékou-Oumar Kaba, Siamak Ravanbakhsh:
Equivariant Networks for Crystal Structures. - Fred Lu, Joseph Munoz, Maya Fuchs, Tyler LeBlond, Elliott Zaresky-Williams, Edward Raff, Francis Ferraro, Brian Testa:
A General Framework for Auditing Differentially Private Machine Learning. - Masaaki Nishino, Kengo Nakamura, Norihito Yasuda:
Generalization Analysis on Learning with a Concurrent Verifier. - Kai Sheng Tai, Tai-Peng Tian, Ser Nam Lim:
Spartan: Differentiable Sparsity via Regularized Transportation. - Jianwei Yang, Chunyuan Li, Xiyang Dai, Jianfeng Gao:
Focal Modulation Networks. - Qing Li, Yu-Shen Liu, Jin-San Cheng, Cheng Wang, Yi Fang, Zhizhong Han:
HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces. - Daniel Bienstock, Minchan Jeong, Apurv Shukla, Se-Young Yun:
Robust Streaming PCA. - Chengan He, Jun Saito, James Zachary, Holly E. Rushmeier, Yi Zhou:
NeMF: Neural Motion Fields for Kinematic Animation. - Ehsan Variani, Ke Wu, Michael D. Riley, David Rybach, Matt Shannon, Cyril Allauzen:
Global Normalization for Streaming Speech Recognition in a Modular Framework. - Yiqun Mei, Pengfei Guo, Mo Zhou, Vishal Patel:
Resource-Adaptive Federated Learning with All-In-One Neural Composition. - Zhize Li, Haoyu Zhao, Boyue Li, Yuejie Chi:
SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression. - Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He:
Your Transformer May Not be as Powerful as You Expect. - Rongqin Chen, Shenghui Zhang, Leong Hou U, Ye Li:
Redundancy-Free Message Passing for Graph Neural Networks. - Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, Tatsunori B. Hashimoto:
Diffusion-LM Improves Controllable Text Generation. - Paul Novello, Thomas Fel, David Vigouroux:
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure. - Beomsu Kim, Jong Chul Ye:
Energy-Based Contrastive Learning of Visual Representations. - Binghui Li, Jikai Jin, Han Zhong, John E. Hopcroft, Liwei Wang:
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power. - Yuchen Xiao, Weihao Tan, Christopher Amato:
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning. - Guandao Yang, Sagie Benaim, Varun Jampani, Kyle Genova, Jonathan T. Barron, Thomas A. Funkhouser, Bharath Hariharan, Serge J. Belongie:
Polynomial Neural Fields for Subband Decomposition and Manipulation. - Duncan C. McElfresh, Sujay Khandagale, Jonathan Valverde, John Dickerson, Colin White:
On the Generalizability and Predictability of Recommender Systems. - Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton:
Optimal Rates for Regularized Conditional Mean Embedding Learning. - Mingzhe Guo, Zhipeng Zhang, Heng Fan, Liping Jing:
Divert More Attention to Vision-Language Tracking. - Shangquan Sun, Wenqi Ren, Tao Wang, Xiaochun Cao:
Rethinking Image Restoration for Object Detection. - Elias Frantar, Dan Alistarh:
Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning. - Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli:
Challenging Common Assumptions in Convex Reinforcement Learning. - Jiayi Shen, Zehao Xiao, Xiantong Zhen, Cees Snoek, Marcel Worring:
Association Graph Learning for Multi-Task Classification with Category Shifts. - Jiazhi Guan, Hang Zhou, Zhibin Hong, Errui Ding, Jingdong Wang, Chengbin Quan, Youjian Zhao:
Delving into Sequential Patches for Deepfake Detection. - Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang Wang, Jan Gasthaus:
On the detrimental effect of invariances in the likelihood for variational inference. - Franz Scherr, Qinghai Guo, Timoleon Moraitis:
Self-Supervised Learning Through Efference Copies. - Lechao Xiao, Hong Hu, Theodor Misiakiewicz, Yue Lu, Jeffrey Pennington:
Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression. - Zhiyuan You, Lei Cui, Yujun Shen, Kai Yang, Xin Lu, Yu Zheng, Xinyi Le:
A Unified Model for Multi-class Anomaly Detection. - Jaekyeom Kim, Seohong Park, Gunhee Kim:
Constrained GPI for Zero-Shot Transfer in Reinforcement Learning. - Yanbo Xu, Alind Khare, Glenn Matlin, Monish Ramadoss, Rishikesan Kamaleswaran, Chao Zhang, Alexey Tumanov:
UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification. - Di Lin, Xin Wang, Jia Shen, Renjie Zhang, Ruonan Liu, Miaohui Wang, Wuyuan Xie, Qing Guo, Ping Li:
Generative Status Estimation and Information Decoupling for Image Rain Removal. - Ingvar M. Ziemann, Stephen Tu:
Learning with little mixing. - Rui Ding, Kehua Guo, Xiangyuan Zhu, Zheng Wu, Liwei Wang:
ComGAN: Unsupervised Disentanglement and Segmentation via Image Composition. - Mukund Varma T., Xuxi Chen, Zhenyu Zhang, Tianlong Chen, Subhashini Venugopalan, Zhangyang Wang:
Sparse Winning Tickets are Data-Efficient Image Recognizers. - Eleonora Misino, Giuseppe Marra, Emanuele Sansone:
VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming. - Lei Wu, Mingze Wang, Weijie Su:
The alignment property of SGD noise and how it helps select flat minima: A stability analysis. - Runlin Lei, Zhen Wang, Yaliang Li, Bolin Ding, Zhewei Wei:
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks. - Jiaqi Leng, Yuxiang Peng, Yi-Ling Qiao, Ming C. Lin, Xiaodi Wu:
Differentiable Analog Quantum Computing for Optimization and Control. - Yafei Yang, Bo Yang:
Promising or Elusive? Unsupervised Object Segmentation from Real-world Single Images. - Sebastian Dalleiger, Jilles Vreeken:
Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent. - Ye Du, Yujun Shen, Haochen Wang, Jingjing Fei, Wei Li, Liwei Wu, Rui Zhao, Zehua Fu, Qingjie Liu:
Learning from Future: A Novel Self-Training Framework for Semantic Segmentation. - Jiafan He, Tianhao Wang, Yifei Min, Quanquan Gu:
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits. - Jiarui Feng, Yixin Chen, Fuhai Li, Anindya Sarkar, Muhan Zhang:
How Powerful are K-hop Message Passing Graph Neural Networks. - Aravind Reddy, Zhao Song, Lichen Zhang:
Dynamic Tensor Product Regression. - Sohir Maskey, Ron Levie, Yunseok Lee, Gitta Kutyniok:
Generalization Analysis of Message Passing Neural Networks on Large Random Graphs. - Alexandros Psomas, Ariel Schvartzman, S. Matthew Weinberg:
On Infinite Separations Between Simple and Optimal Mechanisms. - Huiwen Jia, Cong Shi, Siqian Shen:
Online Learning and Pricing for Network Revenue Management with Reusable Resources. - Jonathan Laurent, André Platzer:
Learning to Find Proofs and Theorems by Learning to Refine Search Strategies: The Case of Loop Invariant Synthesis. - Denizalp Goktas, Amy Greenwald:
Exploitability Minimization in Games and Beyond. - Yue Hu, Shaoheng Fang, Zixing Lei, Yiqi Zhong, Siheng Chen:
Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps. - Mohammad Azizmalayeri, Arshia Soltani Moakhar, Arman Zarei, Reihaneh Zohrabi, Mohammad Taghi Manzuri, Mohammad Hossein Rohban:
Your Out-of-Distribution Detection Method is Not Robust! - Piyush Raikwar, Deepak Mishra:
Discovering and Overcoming Limitations of Noise-engineered Data-free Knowledge Distillation. - Sean Welleck, Jiacheng Liu, Ximing Lu, Hannaneh Hajishirzi, Yejin Choi:
NaturalProver: Grounded Mathematical Proof Generation with Language Models. - Lijun Zhang, Wei Jiang, Jinfeng Yi, Tianbao Yang:
Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor.