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Jason D. Lee

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

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* 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

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* 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

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* 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

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* 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

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* 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

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* 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

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* 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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