Papers
Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss
Jeff Z. HaoChen, Colin Wei, Adrien Gaidon et al.
Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature
Kefan Dong, Jiaqi Yang, Tengyu Ma
Provable Representation Learning for Imitation with Contrastive Fourier Features
Ofir Nachum, Mengjiao Yang
Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning
Guanlin Liu, Lifeng LAI
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data
Lingxiao Wang, Zhuoran Yang, Zhaoran Wang
Provably efficient multi-task reinforcement learning with model transfer
Chicheng Zhang, Zhi Wang
Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints
Tianhao Wang, Dongruo Zhou, Quanquan Gu
Provably efficient, succinct, and precise explanations
Guy Blanc, Jane Lange, Li-Yang Tan
Provably Faster Algorithms for Bilevel Optimization
Junjie Yang, Kaiyi Ji, Yingbin Liang
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent
Spencer Frei, Quanquan Gu
Proxy-Normalizing Activations to Match Batch Normalization while Removing Batch Dependence
Antoine Labatie, Dominic Masters, Zach Eaton-Rosen et al.
Pruning Randomly Initialized Neural Networks with Iterative Randomization
Daiki Chijiwa, Shin'ya Yamaguchi, Yasutoshi Ida et al.
PSD Representations for Effective Probability Models
Alessandro Rudi, Carlo Ciliberto
Pseudo-Spherical Contrastive Divergence
Lantao Yu, Jiaming Song, Yang Song et al.
PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning
Yining Hong, Li Yi, Josh Tenenbaum et al.
Pure Exploration in Kernel and Neural Bandits
Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang et al.
Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples
Kanghyun Choi, Deokki Hong, Noseong Park et al.
Quantifying and Improving Transferability in Domain Generalization
Guojun Zhang, Han Zhao, Yaoliang Yu et al.
Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes
Sanghyun Hong, Michael-Andrei Panaitescu-Liess, Yigitcan Kaya et al.
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning
Kaan Ozkara, Navjot Singh, Deepesh Data et al.
Random Noise Defense Against Query-Based Black-Box Attacks
Zeyu Qin, Yanbo Fan, Hongyuan Zha et al.
Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems
Itay Safran, Ohad Shamir
Ranking Policy Decisions
Hadrien Pouget, Hana Chockler, Youcheng Sun et al.
Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery
Lijun Ding, Liwei Jiang, Yudong Chen et al.