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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. - Gregory Canal, Blake Mason, Ramya Korlakai Vinayak, Robert Nowak:
One for All: Simultaneous Metric and Preference Learning over Multiple Users. - Ze Wang, Yipin Zhou, Rui Wang, Tsung-Yu Lin, Ashish Shah, Ser Nam Lim:
Few-Shot Fast-Adaptive Anomaly Detection. - Bowen Zhang, Zhi Tian, Quan Tang, Xiangxiang Chu, Xiaolin Wei, Chunhua Shen, Yifan Liu:
SegViT: Semantic Segmentation with Plain Vision Transformers. - Julián Tachella, Dongdong Chen, Mike E. Davies:
Unsupervised Learning From Incomplete Measurements for Inverse Problems. - Eric Chen, Zhang-Wei Hong, Joni Pajarinen, Pulkit Agrawal:
Redeeming intrinsic rewards via constrained optimization. - Ganqu Cui, Lifan Yuan, Bingxiang He, Yangyi Chen, Zhiyuan Liu, Maosong Sun:
A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks. - Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard D. Bondell:
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. - Tomoya Murata, Taiji Suzuki:
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning. - Yao Qiang, Deng Pan, Chengyin Li, Xin Li, Rhongho Jang, Dongxiao Zhu:
AttCAT: Explaining Transformers via Attentive Class Activation Tokens. - Gyuhak Kim, Changnan Xiao, Tatsuya Konishi, Zixuan Ke, Bing Liu:
A Theoretical Study on Solving Continual Learning. - Yixing Xu, Xinghao Chen, Yunhe Wang:
BiMLP: Compact Binary Architectures for Vision Multi-Layer Perceptrons. - Fanghui Liu, Luca Viano, Volkan Cevher:
Understanding Deep Neural Function Approximation in Reinforcement Learning via $\epsilon$-Greedy Exploration. - Zhaoqiang Liu, Xinshao Wang, Jiulong Liu:
Misspecified Phase Retrieval with Generative Priors. - Tian Yu Liu, Baharan Mirzasoleiman:
Data-Efficient Augmentation for Training Neural Networks. - Ziyi Zhang, Weikai Chen, Hui Cheng, Zhen Li, Siyuan Li, Liang Lin, Guanbin Li:
Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning. - Yuanpei Chen, Tianhao Wu, Shengjie Wang, Xidong Feng, Jiechuan Jiang, Zongqing Lu, Stephen McAleer, Hao Dong, Song-Chun Zhu, Yaodong Yang:
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning. - Ilias Diakonikolas, Daniel Kane, Jasper C. H. Lee, Ankit Pensia:
Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions. - Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié, Alain Durmus, Alexey Naumov, Eric Moulines:
Local-Global MCMC kernels: the best of both worlds. - Adam Haber, Elad Schneidman:
The computational and learning benefits of Daleian neural networks. - Kevin Frans, Lisa B. Soros, Olaf Witkowski:
CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders. - Luis Herrmann, Maximilian Granz, Tim Landgraf:
Chaotic Dynamics are Intrinsic to Neural Network Training with SGD. - Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert:
Efficient and Modular Implicit Differentiation. - Gauthier Guinet, Saurabh Amin, Patrick Jaillet:
Effective Dimension in Bandit Problems under Censorship. - Tessa Han, Suraj Srinivas, Himabindu Lakkaraju:
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations. - Pratik Vaishnavi, Kevin Eykholt, Amir Rahmati:
Accelerating Certified Robustness Training via Knowledge Transfer. - Zelun Luo, Zane Durante, Linden Li, Wanze Xie, Ruochen Liu, Emily Jin, Zhuoyi Huang, Lun Yu Li, Jiajun Wu, Juan Carlos Niebles, Ehsan Adeli, Fei-Fei Li:
MOMA-LRG: Language-Refined Graphs for Multi-Object Multi-Actor Activity Parsing. - Jorge Quesada, Lakshmi Sathidevi, Ran Liu, Nauman Ahad, Joy M. Jackson, Mehdi Azabou, Jingyun Xiao, Christopher Liding, Matthew Jin, Carolina Urzay, William R. Gray Roncal, Erik C. Johnson, Eva L. Dyer:
MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction. - Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Telenczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux:
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. - Mike Wu, Noah D. Goodman:
Foundation Posteriors for Approximate Probabilistic Inference. - Zikui Cai, Chengyu Song, Srikanth Krishnamurthy, Amit Roy-Chowdhury, Salman Asif:
Blackbox Attacks via Surrogate Ensemble Search. - Libin Zhu, Chaoyue Liu, Misha Belkin:
Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture. - Keyu Duan, Zirui Liu, Peihao Wang, Wenqing Zheng, Kaixiong Zhou, Tianlong Chen, Xia Hu, Zhangyang Wang:
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking. - Philipp Holl, Vladlen Koltun, Nils Thuerey:
Scale-invariant Learning by Physics Inversion. - Róbert Busa-Fekete, Heejin Choi, Krzysztof Dembczynski, Claudio Gentile, Henry Reeve, Balázs Szörényi:
Regret Bounds for Multilabel Classification in Sparse Label Regimes. - Raghavendra Addanki, David Arbour, Tung Mai, Cameron Musco, Anup Rao:
Sample Constrained Treatment Effect Estimation. - Xuanli He, Qiongkai Xu, Yi Zeng, Lingjuan Lyu, Fangzhao Wu, Jiwei Li, Ruoxi Jia:
CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks. - Gabriele Cesa, Arash Behboodi, Taco S. Cohen, Max Welling:
On the symmetries of the synchronization problem in Cryo-EM: Multi-Frequency Vector Diffusion Maps on the Projective Plane. - Nishanth Dikkala, Sankeerth Rao Karingula, Raghu Meka, Jelani Nelson, Rina Panigrahy, Xin Wang:
Sketching based Representations for Robust Image Classification with Provable Guarantees. - Siqi Shen, Mengwei Qiu, Jun Liu, Weiquan Liu, Yongquan Fu, Xinwang Liu, Cheng Wang:
ResQ: A Residual Q Function-based Approach for Multi-Agent Reinforcement Learning Value Factorization. - Eli N. Weinstein, Alan Nawzad Amin, Jonathan Frazer, Debora S. Marks:
Non-identifiability and the Blessings of Misspecification in Models of Molecular Fitness. - Hao Jiang, Yadong Mu:
Conditional Diffusion Process for Inverse Halftoning. - Rihab Gorsane, Omayma Mahjoub, Ruan de Kock, Roland Dubb, Siddarth Singh, Arnu Pretorius:
Towards a Standardised Performance Evaluation Protocol for Cooperative MARL. - Dan Mikulincer, Daniel Reichman:
Size and depth of monotone neural networks: interpolation and approximation. - Jack Lindsey, Ashok Litwin-Kumar:
Action-modulated midbrain dopamine activity arises from distributed control policies. - Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang:
Latent Hierarchical Causal Structure Discovery with Rank Constraints. - Seiji Maekawa, Koki Noda, Yuya Sasaki, Makoto Onizuka:
Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs. - Tian Tian, Kenny Young, Richard S. Sutton:
Doubly-Asynchronous Value Iteration: Making Value Iteration Asynchronous in Actions. - Luke Marris, Ian Gemp, Thomas Anthony, Andrea Tacchetti, Siqi Liu, Karl Tuyls:
Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers. - Jacob H. Seidman, Georgios Kissas, Paris Perdikaris, George J. Pappas:
NOMAD: Nonlinear Manifold Decoders for Operator Learning. - Yongri Piao, Chenyang Lu, Miao Zhang, Huchuan Lu:
Semi-Supervised Video Salient Object Detection Based on Uncertainty-Guided Pseudo Labels. - Caizhi Tang, Huiyuan Wang, Xinyu Li, Qing Cui, Ya-Lin Zhang, Feng Zhu, Longfei Li, Jun Zhou, Linbo Jiang:
Debiased Causal Tree: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding. - Kyeongwon Lee, Jaeyong Lee:
Asymptotic Properties for Bayesian Neural Network in Besov Space. - Arash Behboodi, Gabriele Cesa, Taco S. Cohen:
A PAC-Bayesian Generalization Bound for Equivariant Networks. - Shijun Zhang, Zuowei Shen, Haizhao Yang:
Neural Network Architecture Beyond Width and Depth. - Yabin Wang, Zhiwu Huang, Xiaopeng Hong:
S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning. - Junke Wang, Dongdong Chen, Zuxuan Wu, Chong Luo, Luowei Zhou, Yucheng Zhao, Yujia Xie, Ce Liu, Yu-Gang Jiang, Lu Yuan:
OmniVL: One Foundation Model for Image-Language and Video-Language Tasks. - Jianzhun Shao, Zhiqiang Lou, Hongchang Zhang, Yuhang Jiang, Shuncheng He, Xiangyang Ji:
Self-Organized Group for Cooperative Multi-agent Reinforcement Learning. - Anders Aamand, Justin Y. Chen, Piotr Indyk:
(Optimal) Online Bipartite Matching with Degree Information. - Bhaskar Ray Chaudhury, Linyi Li, Mintong Kang, Bo Li, Ruta Mehta:
Fairness in Federated Learning via Core-Stability. - Bing Su, Ji-Rong Wen:
Log-Polar Space Convolution Layers. - Haoran Li, Yang Weng, Hanghang Tong:
CoNSoLe: Convex Neural Symbolic Learning. - Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu:
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps. - Qihua Zhou, Song Guo, Yi Liu, Jie Zhang, Jiewei Zhang, Tao Guo, Zhenda Xu, Xun Liu, Zhihao Qu:
Hierarchical Channel-spatial Encoding for Communication-efficient Collaborative Learning. - Shiqi Yang, Yaxing Wang, Kai Wang, Shangling Jui, Joost van de Weijer:
Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation. - Minhao Liu, Ailing Zeng, Muxi Chen, Zhijian Xu, Qiuxia Lai, Lingna Ma, Qiang Xu:
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction. - Rati Devidze, Parameswaran Kamalaruban, Adish Singla:
Exploration-Guided Reward Shaping for Reinforcement Learning under Sparse Rewards. - David Lindner, Andreas Krause, Giorgia Ramponi:
Active Exploration for Inverse Reinforcement Learning. - John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar:
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. - Alexander Ororbia, Ankur Arjun Mali, C. Lee Giles, Daniel Kifer:
Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting. - Siwei Wang, Xinwang Liu, Suyuan Liu, Jiaqi Jin, Wenxuan Tu, Xinzhong Zhu, En Zhu:
Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences. - Lecheng Kong, Yixin Chen, Muhan Zhang:
Geodesic Graph Neural Network for Efficient Graph Representation Learning. - Sergey Denisov, H. Brendan McMahan, John Rush, Adam D. Smith, Abhradeep Guha Thakurta:
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams. - Ken Ziyu Liu, Shengyuan Hu, Steven Wu, Virginia Smith:
On Privacy and Personalization in Cross-Silo Federated Learning. - Jianhong Wang, Yuan Zhang, Yunjie Gu, Tae-Kyun Kim:
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning. - Nikolay Malkin, Moksh Jain, Emmanuel Bengio, Chen Sun, Yoshua Bengio:
Trajectory balance: Improved credit assignment in GFlowNets. - Andrew Wagenmaker, Kevin G. Jamieson:
Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design. - Matt Deitke, Eli VanderBilt, Alvaro Herrasti, Luca Weihs, Kiana Ehsani, Jordi Salvador, Winson Han, Eric Kolve, Aniruddha Kembhavi, Roozbeh Mottaghi:
🏘️ ProcTHOR: Large-Scale Embodied AI Using Procedural Generation. - Xiang Cheng, Jingzhao Zhang, Suvrit Sra:
Efficient Sampling on Riemannian Manifolds via Langevin MCMC. - Chengyuan Deng, Shihang Feng, Hanchen Wang, Xitong Zhang, Peng Jin, Yinan Feng, Qili Zeng, Yinpeng Chen, Youzuo Lin:
OpenFWI: Large-scale Multi-structural Benchmark Datasets for Full Waveform Inversion. - Chenqing Hua, Guillaume Rabusseau, Jian Tang:
High-Order Pooling for Graph Neural Networks with Tensor Decomposition. - Babak Rahmani, Demetri Psaltis, Christophe Moser:
Natural image synthesis for the retina with variational information bottleneck representation. - Benjamin Eysenbach, Soumith Udatha, Russ Salakhutdinov, Sergey Levine:
Imitating Past Successes can be Very Suboptimal. - Zhenyu Sun, Ermin Wei:
A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning. - Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Zhou Qin, Wenwu Zhu:
Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift. - Jianchuan Ding, Bo Dong, Felix Heide, Yufei Ding, Yunduo Zhou, Baocai Yin, Xin Yang:
Biologically Inspired Dynamic Thresholds for Spiking Neural Networks. - Yehui Tang, Junchi Yan:
GraphQNTK: Quantum Neural Tangent Kernel for Graph Data. - Penghao Wu, Xiaosong Jia, Li Chen, Junchi Yan, Hongyang Li, Yu Qiao:
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline. - Bolivar Solarte, Chin-Hsuan Wu, Yueh-Cheng Liu, Yi-Hsuan Tsai, Min Sun:
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning. - Bingqing Song, Ioannis C. Tsaknakis, Chung-Yiu Yau, Hoi-To Wai, Mingyi Hong:
Distributed Optimization for Overparameterized Problems: Achieving Optimal Dimension Independent Communication Complexity. - Zeshan M. Hussain, Michael Oberst, Ming-Chieh Shih, David A. Sontag:
Falsification before Extrapolation in Causal Effect Estimation. - Karush Suri, Xiao Qi Shi, Konstantinos N. Plataniotis, Yuri A. Lawryshyn:
Surprise Minimizing Multi-Agent Learning with Energy-based Models. - Zhengyu Li, Xuan Tang, Zihao Xu, Xihao Wang, Hui Yu, Mingsong Chen, Xian Wei:
Geodesic Self-Attention for 3D Point Clouds. - Sachin Goyal, Mingjie Sun, Aditi Raghunathan, J. Zico Kolter:
Test Time Adaptation via Conjugate Pseudo-labels. - Reinmar J. Kobler, Jun-ichiro Hirayama, Qibin Zhao, Motoaki Kawanabe:
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG. - Sudipta Paul, Amit Roy-Chowdhury, Anoop Cherian:
AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments. - Swami Sankaranarayanan, Anastasios Angelopoulos, Stephen Bates, Yaniv Romano, Phillip Isola:
Semantic uncertainty intervals for disentangled latent spaces. - Jelena Diakonikolas, Chenghui Li, Swati Padmanabhan, Chaobing Song:
A Fast Scale-Invariant Algorithm for Non-negative Least Squares with Non-negative Data. - Jian Yao, Yuxin Hong, Chiyu Wang, Tianjun Xiao, Tong He, Francesco Locatello, David P. Wipf, Yanwei Fu, Zheng Zhang:
Self-supervised Amodal Video Object Segmentation. - Audrey Huang, Nan Jiang:
Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions. - Maciej Wolczyk, Michal Zajac, Razvan Pascanu, Lukasz Kucinski, Piotr Milos:
Disentangling Transfer in Continual Reinforcement Learning. - Jihoon Tack, Jongjin Park, Hankook Lee, Jaeho Lee, Jinwoo Shin:
Meta-Learning with Self-Improving Momentum Target. - Steven Yin, Shipra Agrawal, Assaf Zeevi:
Online Allocation and Learning in the Presence of Strategic Agents. - Shuo Chen, Chen Gong, Jun Li, Jian Yang, Gang Niu, Masashi Sugiyama:
Learning Contrastive Embedding in Low-Dimensional Space. - Zhihan Xiong, Ruoqi Shen, Qiwen Cui, Maryam Fazel, Simon S. Du:
Near-Optimal Randomized Exploration for Tabular Markov Decision Processes. - Xiyuan Li, Xin Zou, Weiwei Liu:
Defending Against Adversarial Attacks via Neural Dynamic System. - Yang Song, Qiyu Kang, Sijie Wang, Kai Zhao, Wee Peng Tay:
On the Robustness of Graph Neural Diffusion to Topology Perturbations. - Qian Huang, Hongyu Ren, Jure Leskovec:
Few-shot Relational Reasoning via Connection Subgraph Pretraining. - Darius Muglich, Christian Schröder de Witt, Elise van der Pol, Shimon Whiteson, Jakob N. Foerster:
Equivariant Networks for Zero-Shot Coordination. - Rong Yin, Yong Liu, Weiping Wang, Dan Meng:
Randomized Sketches for Clustering: Fast and Optimal Kernel $k$-Means. - Whiyoung Jung, Myungsik Cho, Jongeui Park, Youngchul Sung:
Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage Probability. - Manel Baradad, Chun-Fu Richard Chen, Jonas Wulff, Tongzhou Wang, Rogério Feris, Antonio Torralba, Phillip Isola:
Procedural Image Programs for Representation Learning. - Kyungmin Lee, Jinwoo Shin:
RényiCL: Contrastive Representation Learning with Skew Rényi Divergence. - Moses Charikar, Zhihao Jiang, Kirankumar Shiragur, Aaron Sidford:
On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood. - Cheng-Fu Yang, Yao-Hung Hubert Tsai, Wan-Cyuan Fan, Russ Salakhutdinov, Louis-Philippe Morency, Frank Wang:
Paraphrasing Is All You Need for Novel Object Captioning. - Yunjuan Wang, Enayat Ullah, Poorya Mianjy, Raman Arora:
Adversarial Robustness is at Odds with Lazy Training. - Myong Chol Jung, He Zhao, Joanna Dipnall, Belinda Gabbe, Lan Du:
Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture. - Shaoshuai Shi, Li Jiang, Dengxin Dai, Bernt Schiele:
Motion Transformer with Global Intention Localization and Local Movement Refinement. - Yu-Guan Hsieh, Kimon Antonakopoulos, Volkan Cevher, Panayotis Mertikopoulos:
No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation. - Michael I. Jordan, Tianyi Lin, Emmanouil V. Vlatakis-Gkaragkounis:
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces. - Jian-Wei Zhang, Yifan Sun, Yi Yang, Wei Chen:
Feature-Proxy Transformer for Few-Shot Segmentation. - Matteo Sesia, Stefano Favaro:
Conformal Frequency Estimation with Sketched Data. - Pablo Moreno-Muñoz, Cilie W. Feldager, Søren Hauberg:
Revisiting Active Sets for Gaussian Process Decoders. - Lukas Braun, Clémentine C. J. Dominé, James Fitzgerald, Andrew M. Saxe:
Exact learning dynamics of deep linear networks with prior knowledge. - Dacheng Li, Hongyi Wang, Eric P. Xing, Hao Zhang:
AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness. - Fadi Hamad, Oliver Hinder:
A consistently adaptive trust-region method. - Weihan Li, Yu Qi, Gang Pan:
Online Neural Sequence Detection with Hierarchical Dirichlet Point Process. - Changbao Wang, Dandan Zheng, Yuanliu Liu, Liang Li:
Leveraging Inter-Layer Dependency for Post -Training Quantization. - Paul Bertens, Seong-Whan Lee:
Emergence of Hierarchical Layers in a Single Sheet of Self-Organizing Spiking Neurons. - Damek Davis, Dmitriy Drusvyatskiy, Yin Tat Lee, Swati Padmanabhan, Guanghao Ye:
A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions. - Shashank Goel, Hritik Bansal, Sumit Bhatia, Ryan A. Rossi, Vishwa Vinay, Aditya Grover:
CyCLIP: Cyclic Contrastive Language-Image Pretraining. - Vijay Vasudevan, Benjamin Caine, Raphael Gontijo Lopes, Sara Fridovich-Keil, Rebecca Roelofs:
When does dough become a bagel? Analyzing the remaining mistakes on ImageNet. - Jianhui Liu, Yukang Chen, Xiaoqing Ye, Zhuotao Tian, Xiao Tan, Xiaojuan Qi:
Spatial Pruned Sparse Convolution for Efficient 3D Object Detection. - Aniket Das, Bernhard Schölkopf, Michael Muehlebach:
Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization. - Arthur Jacot, Eugene Golikov, Clément Hongler, Franck Gabriel:
Feature Learning in $L_2$-regularized DNNs: Attraction/Repulsion and Sparsity. - Seanie Lee, Bruno Andreis, Kenji Kawaguchi, Juho Lee, Sung Ju Hwang:
Set-based Meta-Interpolation for Few-Task Meta-Learning. - Ankit Goyal, Alexey Bochkovskiy, Jia Deng, Vladlen Koltun:
Non-deep Networks. - Meyer Scetbon, Marco Cuturi:
Low-rank Optimal Transport: Approximation, Statistics and Debiasing. - Zhengyi Luo, Shun Iwase, Ye Yuan, Kris Kitani:
Embodied Scene-aware Human Pose Estimation. - Kristian Georgiev, Samuel B. Hopkins:
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation. - ChangYong Oh, Roberto Bondesan, Efstratios Gavves, Max Welling:
Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel. - Charlotte Bunne, Andreas Krause, Marco Cuturi:
Supervised Training of Conditional Monge Maps. - Harald Strömfelt, Luke Dickens, Artur S. d'Avila Garcez, Alessandra Russo:
Formalizing Consistency and Coherence of Representation Learning. - Satoshi Hayakawa, Harald Oberhauser, Terry J. Lyons:
Positively Weighted Kernel Quadrature via Subsampling. - Lukas Prantl, Benjamin Ummenhofer, Vladlen Koltun, Nils Thuerey:
Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics. - Lichao Zhang, Ruiqi Li, Shoutong Wang, Liqun Deng, Jinglin Liu, Yi Ren, Jinzheng He, Rongjie Huang, Jieming Zhu, Xiao Chen, Zhou Zhao:
M4Singer: A Multi-Style, Multi-Singer and Musical Score Provided Mandarin Singing Corpus. - Luning Sun, Daniel Huang, Hao Sun, Jian-Xun Wang:
Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty. - Julien Klaus, Niklas Merk, Konstantin Wiedom, Sören Laue, Joachim Giesen:
Convexity Certificates from Hessians. - Wei Liu, Haozhao Wang, Jun Wang, Ruixuan Li, Chao Yue, Yuankai Zhang:
FR: Folded Rationalization with a Unified Encoder. - Takumi Tanabe, Rei Sato, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification. - Fan Yang, Lin Guo, Zhi Chen, Wenbing Tao:
One-Inlier is First: Towards Efficient Position Encoding for Point Cloud Registration. - Dandan Guo, Long Tian, He Zhao, Mingyuan Zhou, Hongyuan Zha:
Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport. - Sidi Lu, Tao Meng, Nanyun Peng:
InsNet: An Efficient, Flexible, and Performant Insertion-based Text Generation Model. - Fotis Iliopoulos, Vasilis Kontonis, Cenk Baykal, Gaurav Menghani, Khoa Trinh, Erik Vee:
Weighted Distillation with Unlabeled Examples. - Yimeng Chen, Ruibin Xiong, Zhi-Ming Ma, Yanyan Lan:
When Does Group Invariant Learning Survive Spurious Correlations? - Chengliang Zhong, Peixing You, Xiaoxue Chen, Hao Zhao, Fuchun Sun, Guyue Zhou, Xiaodong Mu, Chuang Gan, Wenbing Huang:
SNAKE: Shape-aware Neural 3D Keypoint Field. - Hidenori Iwakiri, Yuhang Wang, Shinji Ito, Akiko Takeda:
Single Loop Gaussian Homotopy Method for Non-convex Optimization. - Gokul Swamy, Nived Rajaraman, Matthew Peng, Sanjiban Choudhury, J. Andrew Bagnell, Steven Wu, Jiantao Jiao, Kannan Ramchandran:
Minimax Optimal Online Imitation Learning via Replay Estimation. - Max Daniels, Cédric Gerbelot, Florent Krzakala, Lenka Zdeborová:
Multi-layer State Evolution Under Random Convolutional Design. - Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Y. Zhao, Andrew M. Dai, Zhifeng Chen, Quoc V. Le, James Laudon:
Mixture-of-Experts with Expert Choice Routing. - Enric Boix-Adserà, Hannah Lawrence, George Stepaniants, Philippe Rigollet:
GULP: a prediction-based metric between representations. - Angeliki Giannou, Kyriakos Lotidis, Panayotis Mertikopoulos, Emmanouil V. Vlatakis-Gkaragkounis:
On the convergence of policy gradient methods to Nash equilibria in general stochastic games. - Haibo Yang, Zhuqing Liu, Xin Zhang, Jia Liu:
SAGDA: Achieving $\mathcal{O}(\epsilon^{-2})$ Communication Complexity in Federated Min-Max Learning. - Shariq Iqbal, Robby Costales, Fei Sha:
ALMA: Hierarchical Learning for Composite Multi-Agent Tasks. - Kuan-Lin Chen, Harinath Garudadri, Bhaskar D. Rao:
Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions. - Florian Wenzel, Andrea Dittadi, Peter V. Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Schölkopf, Francesco Locatello:
Assaying Out-Of-Distribution Generalization in Transfer Learning. - Wenbin Song, Mingrui Zhang, Joseph G. Wallwork, Junpeng Gao, Zheng Tian, Fanglei Sun, Matthew D. Piggott, Junqing Chen, Zuoqiang Shi, Xiang Chen, Jun Wang:
M2N: Mesh Movement Networks for PDE Solvers. - Kevin D. Smith, Francesco Seccamonte, Ananthram Swami, Francesco Bullo:
Physics-Informed Implicit Representations of Equilibrium Network Flows. - Ali Taghibakhshi, Nicolas Nytko, Tareq Uz Zaman, Scott P. MacLachlan, Luke N. Olson, Matthew West:
Learning Interface Conditions in Domain Decomposition Solvers. - Wei Lu, Qifeng Wu, Jixian Zhang, Jiahua Rao, Chengtao Li, Shuangjia Zheng:
TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction. - Asif Khan, Amos J. Storkey:
Hamiltonian Latent Operators for content and motion disentanglement in image sequences. - Mingguo He, Zhewei Wei, Ji-Rong Wen:
Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited. - Wenkai Xu, Gesine D. Reinert:
A Kernelised Stein Statistic for Assessing Implicit Generative Models. - Juncheng Li, Xin He, Longhui Wei, Long Qian, Linchao Zhu, Lingxi Xie, Yueting Zhuang, Qi Tian, Siliang Tang:
Fine-Grained Semantically Aligned Vision-Language Pre-Training. - Yichuan Deng, Zhao Song, Omri Weinstein, Ruizhe Zhang:
Fast Distance Oracles for Any Symmetric Norm. - Yu Cheng, Ilias Diakonikolas, Rong Ge, Shivam Gupta, Daniel Kane, Mahdi Soltanolkotabi:
Outlier-Robust Sparse Estimation via Non-Convex Optimization. - Surbhi Goel, Sham M. Kakade, Adam Kalai, Cyril Zhang:
Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms. - Yejia Liu, Wang Zhu, Shaolei Ren:
Navigating Memory Construction by Global Pseudo-Task Simulation for Continual Learning. - Wei Mao, Miaomiao Liu, Richard I. Hartley, Mathieu Salzmann:
Contact-aware Human Motion Forecasting. - Sara Fridovich-Keil, Raphael Gontijo Lopes, Rebecca Roelofs:
Spectral Bias in Practice: The Role of Function Frequency in Generalization. - Yewen Li, Chaojie Wang, Xiaobo Xia, Tongliang Liu, Xin Miao, Bo An:
Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE. - Qiao Feng, Yebin Liu, Yu-Kun Lai, Jingyu Yang, Kun Li:
FOF: Learning Fourier Occupancy Field for Monocular Real-time Human Reconstruction. - Asaf B. Cassel, Alon Peled-Cohen, Tomer Koren:
Rate-Optimal Online Convex Optimization in Adaptive Linear Control. - Jian Wang, Chenhui Gou, Qiman Wu, Haocheng Feng, Junyu Han, Errui Ding, Jingdong Wang:
RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer. - Mayleen Cortez, Matthew Eichhorn, Christina Lee Yu:
Staggered Rollout Designs Enable Causal Inference Under Interference Without Network Knowledge. - Tim Pearce, Jong-Hyeon Jeong, Yichen Jia, Jun Zhu:
Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis. - Zhiyu Zhu, Junhui Hou, Xianqiang Lyu:
Learning Graph-embedded Key-event Back-tracing for Object Tracking in Event Clouds. - Adam Block, Max Simchowitz:
Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions. - Hongwei Chen, Douglas Hendry, Phillip Weinberg, Adrian E. Feiguin:
Systematic improvement of neural network quantum states using Lanczos. - Shusheng Xu, Huaijie Wang, Yi Wu:
Grounded Reinforcement Learning: Learning to Win the Game under Human Commands. - Xiaofeng Mao, Yuefeng Chen, Ranjie Duan, Yao Zhu, Gege Qi, Shaokai Ye, Xiaodan Li, Rong Zhang, Hui Xue:
Enhance the Visual Representation via Discrete Adversarial Training. - Thomas Orton, Damon Falck:
Trading Off Resource Budgets For Improved Regret Bounds. - Shengming Yuan, Qilong Zhang, Lianli Gao, Yaya Cheng, Jingkuan Song:
Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks. - Ajay Jaiswal, Peihao Wang, Tianlong Chen, Justin F. Rousseau, Ying Ding, Zhangyang Wang:
Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again. - Kevin Qinghong Lin, Jinpeng Wang, Mattia Soldan, Michael Wray, Rui Yan, Eric Zhongcong Xu, Difei Gao, Rong-Cheng Tu, Wenzhe Zhao, Weijie Kong, Chengfei Cai, Hongfa Wang, Dima Damen, Bernard Ghanem, Wei Liu, Mike Zheng Shou:
Egocentric Video-Language Pretraining. - Yan Chen, Perry Dong, Qinxun Bai, Maria Dimakopoulou, Wei Xu, Zhengyuan Zhou:
Society of Agents: Regret Bounds of Concurrent Thompson Sampling. - Eloïse Berthier, Ziad Kobeissi, Francis R. Bach:
A Non-asymptotic Analysis of Non-parametric Temporal-Difference Learning. - Kai Yan, Alexander G. Schwing, Yu-Xiong Wang:
CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations. - Simone Bombari, Mohammad Hossein Amani, Marco Mondelli:
Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization. - Mislav Balunovic, Dimitar I. Dimitrov, Nikola Jovanovic, Martin T. Vechev:
LAMP: Extracting Text from Gradients with Language Model Priors. - Nicholas A. Roy, Junkyung Kim, Neil C. Rabinowitz:
Explainability Via Causal Self-Talk. - Zhiqiu Lin, Deepak Pathak, Yu-Xiong Wang, Deva Ramanan, Shu Kong:
Continual Learning with Evolving Class Ontologies. - Arya Akhavan, Evgenii Chzhen, Massimiliano Pontil, Alexandre B. Tsybakov:
A gradient estimator via L1-randomization for online zero-order optimization with two point feedback. - Sadhika Malladi, Kaifeng Lyu, Abhishek Panigrahi, Sanjeev Arora:
On the SDEs and Scaling Rules for Adaptive Gradient Algorithms. - Ho Huu Nghia Nguyen, Tan Nguyen, Huyen Vo, Stanley J. Osher, Thieu Vo:
Improving Neural Ordinary Differential Equations with Nesterov's Accelerated Gradient Method. - Erik Wijmans, Irfan Essa, Dhruv Batra:
VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement. - Martin Weiss, Nasim Rahaman, Francesco Locatello, Chris Pal, Yoshua Bengio, Bernhard Schölkopf, Li Erran Li, Nicolas Ballas:
Neural Attentive Circuits. - Jiayuan Mao, Xuelin Yang, Xikun Zhang, Noah D. Goodman, Jiajun Wu:
CLEVRER-Humans: Describing Physical and Causal Events the Human Way. - Yining Hong, Yilun Du, Chunru Lin, Josh Tenenbaum, Chuang Gan:
3D Concept Grounding on Neural Fields. - Hamed Shirzad, Kaveh Hassani, Danica J. Sutherland:
Evaluating Graph Generative Models with Contrastively Learned Features. - Cem Anil, Ashwini Pokle, Kaiqu Liang, Johannes Treutlein, Yuhuai Wu, Shaojie Bai, J. Zico Kolter, Roger B. Grosse:
Path Independent Equilibrium Models Can Better Exploit Test-Time Computation. - Marc-Etienne Brunet, Ashton Anderson, Richard S. Zemel:
Implications of Model Indeterminacy for Explanations of Automated Decisions. - Ruofan Liu, Yun Lin, Xianglin Yang, Jin Song Dong:
Debugging and Explaining Metric Learning Approaches: An Influence Function Based Perspective. - Mathieu Chalvidal, Thomas Serre, Rufin VanRullen:
Meta-Reinforcement Learning with Self-Modifying Networks. - Tao Qi, Fangzhao Wu, Chuhan Wu, Lingjuan Lyu, Tong Xu, Hao Liao, Zhongliang Yang, Yongfeng Huang, Xing Xie:
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning. - An Zhang, Wenchang Ma, Xiang Wang, Tat-Seng Chua:
Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering. - Zhixuan Yu, Linguang Zhang, Yuanlu Xu, Chengcheng Tang, Luan Tran, Cem Keskin, Hyun Soo Park:
Multiview Human Body Reconstruction from Uncalibrated Cameras. - Yan Huang, Yuming Wang, Yunan Zeng, Liang Wang:
MACK: Multimodal Aligned Conceptual Knowledge for Unpaired Image-text Matching. - Rui Zhao, Ruiqin Xiong, Jing Zhao, Zhaofei Yu, Xiaopeng Fan, Tiejun Huang:
Learning Optical Flow from Continuous Spike Streams. - Zifan Shi, Yinghao Xu, Yujun Shen, Deli Zhao, Qifeng Chen, Dit-Yan Yeung:
Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator. - Teodora Popordanoska, Raphael Sayer, Matthew B. Blaschko:
A Consistent and Differentiable Lp Canonical Calibration Error Estimator. - Michael Poli, Stefano Massaroli, Federico Berto, Jinkyoo Park, Tri Dao, Christopher Ré, Stefano Ermon:
Transform Once: Efficient Operator Learning in Frequency Domain. - Jiacheng Wang, Dan Nicolae:
Fused Orthogonal Alternating Least Squares for Tensor Clustering. - Yatin Nandwani, Rishabh Ranjan, Mausam, Parag Singla:
A Solver-free Framework for Scalable Learning in Neural ILP Architectures. - Yang Jiao, Kai Yang, Dongjin Song:
Distributed Distributionally Robust Optimization with Non-Convex Objectives. - Jiujia Zhang, Ashok Cutkosky:
Parameter-free Regret in High Probability with Heavy Tails. - Michael Arbel, Julien Mairal:
Non-Convex Bilevel Games with Critical Point Selection Maps. - Salim I. Amoukou, Nicolas J.-B. Brunel:
Consistent Sufficient Explanations and Minimal Local Rules for explaining the decision of any classifier or regressor. - Andrés F. López-Lopera, François Bachoc, Olivier Roustant:
High-dimensional Additive Gaussian Processes under Monotonicity Constraints. - Larry Zitnick, Abhishek Das, Adeesh Kolluru, Janice Lan, Muhammed Shuaibi, Anuroop Sriram, Zachary W. Ulissi, Brandon M. Wood:
Spherical Channels for Modeling Atomic Interactions. - Sanghyun Hong, Nicholas Carlini, Alexey Kurakin:
Handcrafted Backdoors in Deep Neural Networks. - Fengyu Yang, Chenyang Ma, Jiacheng Zhang, Jing Zhu, Wenzhen Yuan, Andrew Owens:
Touch and Go: Learning from Human-Collected Vision and Touch. - Haobo Wang, Mingxuan Xia, Yixuan Li, Yuren Mao, Lei Feng, Gang Chen, Junbo Zhao:
SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning. - Michael K. Cohen, Samuel Daulton, Michael A. Osborne:
Log-Linear-Time Gaussian Processes Using Binary Tree Kernels. - Samyak Gupta, Yangsibo Huang, Zexuan Zhong, Tianyu Gao, Kai Li, Danqi Chen:
Recovering Private Text in Federated Learning of Language Models. - Kun Su, Mingfei Chen, Eli Shlizerman:
INRAS: Implicit Neural Representation for Audio Scenes. - Chenze Shao, Yang Feng:
Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translation. - Mattie Tesfaldet, Derek Nowrouzezahrai, Chris Pal:
Attention-based Neural Cellular Automata. - Ryosuke Kojima, Yuji Okamoto:
Learning Deep Input-Output Stable Dynamics. - Zeeshan Khan, C. V. Jawahar, Makarand Tapaswi:
Grounded Video Situation Recognition. - Eric Yang Yu, Zhizhen Qin, Min Kyung Lee, Sicun Gao:
Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems. - Kartik Chandra, Audrey Xie, Jonathan Ragan-Kelley, Erik Meijer:
Gradient Descent: The Ultimate Optimizer. - Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization. - Nicolai Engelmann, Heinz Koeppl:
Forward-Backward Latent State Inference for Hidden Continuous-Time semi-Markov Chains. - Geon-Hyeong Kim, Jongmin Lee, Youngsoo Jang, Hongseok Yang, Kee-Eung Kim:
LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation. - Devansh Arpit, Huan Wang, Yingbo Zhou, Caiming Xiong:
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization. - Chuwei Wang, Shanda Li, Di He, Liwei Wang:
Is $L^2$ Physics Informed Loss Always Suitable for Training Physics Informed Neural Network? - Kai Han, Yunhe Wang, Jianyuan Guo, Yehui Tang, Enhua Wu:
Vision GNN: An Image is Worth Graph of Nodes. - Zixian Ma, Rose E. Wang, Fei-Fei Li, Michael S. Bernstein, Ranjay Krishna:
ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Reward. - Tong Mu, Yash Chandak, Tatsunori B. Hashimoto, Emma Brunskill:
Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits. - Yutong Lin, Ze Liu, Zheng Zhang, Han Hu, Nanning Zheng, Stephen Lin, Yue Cao:
Could Giant Pre-trained Image Models Extract Universal Representations? - Kaixun Hua, Jiayang Ren, Yankai Cao:
A Scalable Deterministic Global Optimization Algorithm for Training Optimal Decision Tree. - Albert Qiaochu Jiang, Wenda Li, Szymon Tworkowski, Konrad Czechowski, Tomasz Odrzygózdz, Piotr Milos, Yuhuai Wu, Mateja Jamnik:
Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers. - Haotong Yang, Zhouchen Lin, Muhan Zhang:
Rethinking Knowledge Graph Evaluation Under the Open-World Assumption. - Ta-Chung Chi, Ting-Han Fan, Peter J. Ramadge, Alexander Rudnicky:
KERPLE: Kernelized Relative Positional Embedding for Length Extrapolation. - Matt Jordan, Jonathan Hayase, Alex Dimakis, Sewoong Oh:
Zonotope Domains for Lagrangian Neural Network Verification. - Filip Radenovic, Abhimanyu Dubey, Dhruv Mahajan:
Neural Basis Models for Interpretability. - Lingfeng Yang, Xiang Li, Borui Zhao, Renjie Song, Jian Yang:
RecursiveMix: Mixed Learning with History. - Yuxin Wang, Zheng Xing, Wei W. Xing:
GAR: Generalized Autoregression for Multi-Fidelity Fusion. - Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Anonymized Histograms in Intermediate Privacy Models. - Ehsan Saleh, Saba Ghaffari, Timothy Bretl, Matthew West:
Truly Deterministic Policy Optimization. - Zhenhailong Wang, Manling Li, Ruochen Xu, Luowei Zhou, Jie Lei, Xudong Lin, Shuohang Wang, Ziyi Yang, Chenguang Zhu, Derek Hoiem, Shih-Fu Chang, Mohit Bansal, Heng Ji:
Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners. - Guillaume Leclerc, Hadi Salman, Andrew Ilyas, Sai Vemprala, Logan Engstrom, Vibhav Vineet, Kai Yuanqing Xiao, Pengchuan Zhang, Shibani Santurkar, Greg Yang, Ashish Kapoor, Aleksander Madry:
3DB: A Framework for Debugging Computer Vision Models. - Michael I. Jordan, Yixin Wang, Angela Zhou:
Empirical Gateaux Derivatives for Causal Inference. - Songkai Xue, Yuekai Sun, Mikhail Yurochkin:
Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees. - Laurent Meunier, Raphael Ettedgui, Rafael Pinot, Yann Chevaleyre, Jamal Atif:
Towards Consistency in Adversarial Classification. - Shayan Shekarforoush, David B. Lindell, David J. Fleet, Marcus A. Brubaker:
Residual Multiplicative Filter Networks for Multiscale Reconstruction. - Jinli Liao, Yikang Ding, Yoli Shavit, Dihe Huang, Shihao Ren, Jia Guo, Wensen Feng, Kai Zhang:
WT-MVSNet: Window-based Transformers for Multi-view Stereo. - Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Private Isotonic Regression. - Yuwei Fu, Di Wu, Benoit Boulet:
A Closer Look at Offline RL Agents. - Xin Wang, Shengfei Lyu, Xingyu Wu, Tianhao Wu, Huanhuan Chen:
Generalization Bounds for Estimating Causal Effects of Continuous Treatments. - Sebastian G. Gruber, Florian Buettner:
Better Uncertainty Calibration via Proper Scores for Classification and Beyond. - Jonathan Ho, Tim Salimans, Alexey A. Gritsenko, William Chan, Mohammad Norouzi, David J. Fleet:
Video Diffusion Models. - Sihui Dai, Saeed Mahloujifar, Prateek Mittal:
Formulating Robustness Against Unforeseen Attacks. - Jayaraman J. Thiagarajan, Rushil Anirudh, Vivek Sivaraman Narayanaswamy, Timo Bremer:
Single Model Uncertainty Estimation via Stochastic Data Centering. - Navid Ansari, Hans-Peter Seidel, Nima Vahidi Ferdowsi, Vahid Babaei:
Autoinverse: Uncertainty Aware Inversion of Neural Networks. - Nikita Kotelevskii, Maxime Vono, Alain Durmus, Eric Moulines:
FedPop: A Bayesian Approach for Personalised Federated Learning. - Haoru Tan, Sitong Wu, Jimin Pi:
Semantic Diffusion Network for Semantic Segmentation. - Aoran Wang, Jun Pang:
Iterative Structural Inference of Directed Graphs. - Aryan Pedawi, Pawel Gniewek, Chaoyi Chang, Brandon M. Anderson, Henry van den Bedem:
An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries. - Vladimir Fomenko, Ismail Elezi, Deva Ramanan, Laura Leal-Taixé, Aljosa Osep:
Learning to Discover and Detect Objects. - Jinho Choo, Yeong-Dae Kwon, Jihoon Kim, Jeongwoo Jae, André Hottung, Kevin Tierney, Youngjune Gwon:
Simulation-guided Beam Search for Neural Combinatorial Optimization. - Pawel Lorek, Rafal Nowak, Tomasz Trzcinski, Maciej Zieba:
FlowHMM: Flow-based continuous hidden Markov models. - Andrea Tirinzoni, Aymen Al Marjani, Emilie Kaufmann:
Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs. - Adrien Bardes, Jean Ponce, Yann LeCun:
VICRegL: Self-Supervised Learning of Local Visual Features. - Anna Kuzina, Max Welling, Jakub M. Tomczak:
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC. - Edoardo Cetin, Oya Çeliktutan:
Policy Gradient With Serial Markov Chain Reasoning. - Gang Li, Xiang Li, Yujie Wang, Yichao Wu, Ding Liang, Shanshan Zhang:
DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection. - Quanyi Li, Zhenghao Peng, Haibin Wu, Lan Feng, Bolei Zhou:
Human-AI Shared Control via Policy Dissection. - Yang Li, Yichuan Mo, Liangliang Shi, Junchi Yan:
Improving Generative Adversarial Networks via Adversarial Learning in Latent Space. - Rao Fu, Xiao Zhan, Yiwen Chen, Daniel Ritchie, Srinath Sridhar:
ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model. - Changan Chen, Carl Schissler, Sanchit Garg, Philip Kobernik, Alexander Clegg, Paul Calamia, Dhruv Batra, Philip W. Robinson, Kristen Grauman:
SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning. - Nicholas Carl Roberts, Xintong Li, Tzu-Heng Huang, Dyah Adila, Spencer Schoenberg, Cheng-Yu Liu, Lauren Pick, Haotian Ma, Aws Albarghouthi, Frederic Sala:
AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels. - Apostolos Avranas, Marios Kountouris:
Towards Disentangling Information Paths with Coded ResNeXt. - Yuzhou Chen, Yulia R. Gel, H. Vincent Poor:
Time-Conditioned Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting. - Felix Mujkanovic, Simon Geisler, Stephan Günnemann, Aleksandar Bojchevski:
Are Defenses for Graph Neural Networks Robust? - Yucheng Lu, Wentao Guo, Christopher De Sa:
GraB: Finding Provably Better Data Permutations than Random Reshuffling. - Juhan Bae, Paul Vicol, Jeff Z. HaoChen, Roger B. Grosse:
Amortized Proximal Optimization. - Chuhan Xie, Zhihua Zhang:
A Statistical Online Inference Approach in Averaged Stochastic Approximation. - Chuanyang Zheng, Zheyang Li, Kai Zhang, Zhi Yang, Wenming Tan, Jun Xiao, Ye Ren, Shiliang Pu:
SAViT: Structure-Aware Vision Transformer Pruning via Collaborative Optimization.