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Bilinear Classes: A Structural Framework for Provable Generalization in RL

Mar 19, 2021
Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang

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MUSBO: Model-based Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learning

Feb 23, 2021
DiJia Su, Jason D. Lee, John M. Mulvey, H. Vincent Poor

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A Theory of Label Propagation for Subpopulation Shift

Feb 22, 2021
Tianle Cai, Ruiqi Gao, Jason D. Lee, Qi Lei

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Provably Efficient Policy Gradient Methods for Two-Player Zero-Sum Markov Games

Feb 17, 2021
Yulai Zhao, Yuandong Tian, Jason D. Lee, Simon S. Du

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Beyond Lazy Training for Over-parameterized Tensor Decomposition

Oct 22, 2020
Xiang Wang, Chenwei Wu, Jason D. Lee, Tengyu Ma, Rong Ge

* NeurIPS 2020; the first two authors contribute equally 

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Provable Benefits of Representation Learning in Linear Bandits

Oct 13, 2020
Jiaqi Yang, Wei Hu, Jason D. Lee, Simon S. Du

* 28 pages, 6 figures 

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How Important is the Train-Validation Split in Meta-Learning?

Oct 12, 2020
Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham Kakade, Huan Wang, Caiming Xiong

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Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot

Sep 22, 2020
Jingtong Su, Yihang Chen, Tianle Cai, Tianhao Wu, Ruiqi Gao, Liwei Wang, Jason D. Lee

* Original submission to NeurIPS 2020. Under review 

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Generalized Leverage Score Sampling for Neural Networks

Sep 21, 2020
Jason D. Lee, Ruoqi Shen, Zhao Song, Mengdi Wang, Zheng Yu

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Predicting What You Already Know Helps: Provable Self-Supervised Learning

Aug 03, 2020
Jason D. Lee, Qi Lei, Nikunj Saunshi, Jiacheng Zhuo

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Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy

Jul 13, 2020
Edward Moroshko, Suriya Gunasekar, Blake Woodworth, Jason D. Lee, Nathan Srebro, Daniel Soudry

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Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks

Jul 03, 2020
Cong Fang, Jason D. Lee, Pengkun Yang, Tong Zhang

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Towards Understanding Hierarchical Learning: Benefits of Neural Representations

Jun 24, 2020
Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher

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Shape Matters: Understanding the Implicit Bias of the Noise Covariance

Jun 18, 2020
Jeff Z. HaoChen, Colin Wei, Jason D. Lee, Tengyu Ma

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Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters

Jun 16, 2020
Kaiyi Ji, Jason D. Lee, Yingbin Liang, H. Vincent Poor

* 28 pages, 8 figures, 2 tables 

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Distributed Estimation for Principal Component Analysis: a Gap-free Approach

Apr 05, 2020
Xi Chen, Jason D. Lee, He Li, Yun Yang

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Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting

Mar 23, 2020
Lemeng Wu, Mao Ye, Qi Lei, Jason D. Lee, Qiang Liu

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Kernel and Rich Regimes in Overparametrized Models

Feb 24, 2020
Blake Woodworth, Suriya Gunasekar, Jason D. Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, Nathan Srebro

* This updates and significantly extends a previous article (arXiv:1906.05827), Sections 6 and 7.1 are the most major additions. 30 pages. arXiv admin note: text overlap with arXiv:1906.05827 

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Few-Shot Learning via Learning the Representation, Provably

Feb 21, 2020
Simon S. Du, Wei Hu, Sham M. Kakade, Jason D. Lee, Qi Lei

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Agnostic Q-learning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity

Feb 17, 2020
Simon S. Du, Jason D. Lee, Gaurav Mahajan, Ruosong Wang

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When Does Non-Orthogonal Tensor Decomposition Have No Spurious Local Minima?

Nov 22, 2019
Maziar Sanjabi, Sina Baharlouei, Meisam Razaviyayn, Jason D. Lee

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SGD Learns One-Layer Networks in WGANs

Oct 15, 2019
Qi Lei, Jason D. Lee, Alexandros G. Dimakis, Constantinos Daskalakis

* 21 pages, 4 figures 

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Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks

Oct 03, 2019
Yu Bai, Jason D. Lee

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Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes

Aug 29, 2019
Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan

* Additional references and discussion of prior work 

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Optimal transport mapping via input convex neural networks

Aug 28, 2019
Ashok Vardhan Makkuva, Amirhossein Taghvaei, Sewoong Oh, Jason D. Lee

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Convergence of Adversarial Training in Overparametrized Networks

Jun 19, 2019
Ruiqi Gao, Tianle Cai, Haochuan Li, Liwei Wang, Cho-Jui Hsieh, Jason D. Lee

* 33 pages 

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Neural Temporal-Difference Learning Converges to Global Optima

May 24, 2019
Qi Cai, Zhuoran Yang, Jason D. Lee, Zhaoran Wang

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Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models

May 17, 2019
Mor Shpigel Nacson, Suriya Gunasekar, Jason D. Lee, Nathan Srebro, Daniel Soudry

* ICML Camera ready version 

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Solving Non-Convex Non-Concave Min-Max Games Under Polyak-Ɓojasiewicz Condition

Dec 07, 2018
Maziar Sanjabi, Meisam Razaviyayn, Jason D. Lee

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