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ORCIDJason D. Lee
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- affiliation: Stanford University, Institute of Computational and Mathematical Engineering
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2020 – today
- 2024
[j11]Qi Cai
, Zhuoran Yang, Jason D. Lee, Zhaoran Wang:
Neural Temporal Difference and Q Learning Provably Converge to Global Optima. Math. Oper. Res. 49(1): 619-651 (2024)- [c114]Alex Damian, Loucas Pillaud-Vivien, Jason D. Lee, Joan Bruna:
Computational-Statistical Gaps in Gaussian Single-Index Models (Extended Abstract). COLT 2024: 1262 - [c113]Zihan Zhang, Yuxin Chen, Jason D. Lee, Simon S. Du:
Settling the sample complexity of online reinforcement learning. COLT 2024: 5213-5219 - [c112]Zihan Zhang, Wenhao Zhan, Yuxin Chen, Simon S. Du, Jason D. Lee:
Optimal Multi-Distribution Learning. COLT 2024: 5220-5223 - [c111]Yulai Zhao, Wenhao Zhan, Xiaoyan Hu, Ho-fung Leung, Farzan Farnia, Wen Sun, Jason D. Lee:
Provably Efficient CVaR RL in Low-rank MDPs. ICLR 2024 - [c110]Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, Kangwook Lee, Dimitris Papailiopoulos:
Teaching Arithmetic to Small Transformers. ICLR 2024 - [c109]Kaifeng Lyu, Jikai Jin, Zhiyuan Li, Simon Shaolei Du, Jason D. Lee, Wei Hu:
Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking. ICLR 2024 - [c108]Zihao Wang, Eshaan Nichani, Jason D. Lee:
Learning Hierarchical Polynomials with Three-Layer Neural Networks. ICLR 2024 - [c107]Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee:
Provable Reward-Agnostic Preference-Based Reinforcement Learning. ICLR 2024 - [c106]Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Provable Offline Preference-Based Reinforcement Learning. ICLR 2024 - [c105]Zihan Zhang, Jason D. Lee, Yuxin Chen, Simon Shaolei Du:
Horizon-Free Regret for Linear Markov Decision Processes. ICLR 2024 - [c104]Tianle Cai, Yuhong Li, Zhengyang Geng, Hongwu Peng, Jason D. Lee, Deming Chen, Tri Dao:
Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads. ICML 2024 - [c103]Uijeong Jang, Jason D. Lee, Ernest K. Ryu:
LoRA Training in the NTK Regime has No Spurious Local Minima. ICML 2024 - [c102]Hong Jun Jeon, Jason D. Lee, Qi Lei, Benjamin Van Roy:
An Information-Theoretic Analysis of In-Context Learning. ICML 2024 - [c101]Eshaan Nichani, Alex Damian, Jason D. Lee:
How Transformers Learn Causal Structure with Gradient Descent. ICML 2024 - [c100]Zixuan Wang, Stanley Wei, Daniel Hsu, Jason D. Lee:
Transformers Provably Learn Sparse Token Selection While Fully-Connected Nets Cannot. ICML 2024 - [c99]Yihua Zhang, Pingzhi Li, Junyuan Hong, Jiaxiang Li, Yimeng Zhang, Wenqing Zheng, Pin-Yu Chen, Jason D. Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, Tianlong Chen:
Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark. ICML 2024 - [c98]Zhenyu He, Zexuan Zhong, Tianle Cai, Jason D. Lee, Di He:
REST: Retrieval-Based Speculative Decoding. NAACL-HLT 2024: 1582-1595 - [i132]Tianle Cai, Yuhong Li, Zhengyang Geng, Hongwu Peng, Jason D. Lee, Deming Chen, Tri Dao:
Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads. CoRR abs/2401.10774 (2024) - [i131]Hong Jun Jeon, Jason D. Lee, Qi Lei, Benjamin Van Roy:
An Information-Theoretic Analysis of In-Context Learning. CoRR abs/2401.15530 (2024) - [i130]James Liu, Guangxuan Xiao, Kai Li, Jason D. Lee, Song Han, Tri Dao, Tianle Cai:
BitDelta: Your Fine-Tune May Only Be Worth One Bit. CoRR abs/2402.10193 (2024) - [i129]Yihua Zhang, Pingzhi Li, Junyuan Hong, Jiaxiang Li, Yimeng Zhang, Wenqing Zheng, Pin-Yu Chen, Jason D. Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, Tianlong Chen:
Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark. CoRR abs/2402.11592 (2024) - [i128]Uijeong Jang, Jason D. Lee, Ernest K. Ryu:
LoRA Training in the NTK Regime has No Spurious Local Minima. CoRR abs/2402.11867 (2024) - [i127]Eshaan Nichani, Alex Damian, Jason D. Lee:
How Transformers Learn Causal Structure with Gradient Descent. CoRR abs/2402.14735 (2024) - [i126]Angeliki Giannou, Liu Yang, Tianhao Wang, Dimitris Papailiopoulos, Jason D. Lee:
How Well Can Transformers Emulate In-context Newton's Method? CoRR abs/2403.03183 (2024) - [i125]Alex Damian, Loucas Pillaud-Vivien, Jason D. Lee, Joan Bruna:
Computational-Statistical Gaps in Gaussian Single-Index Models. CoRR abs/2403.05529 (2024) - [i124]Zihan Zhang, Jason D. Lee, Yuxin Chen, Simon S. Du:
Horizon-Free Regret for Linear Markov Decision Processes. CoRR abs/2403.10738 (2024) - [i123]Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Kianté Brantley, Dipendra Misra, Jason D. Lee, Wen Sun:
Dataset Reset Policy Optimization for RLHF. CoRR abs/2404.08495 (2024) - [i122]Zhaolin Gao, Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kianté Brantley, Thorsten Joachims, J. Andrew Bagnell, Jason D. Lee, Wen Sun:
REBEL: Reinforcement Learning via Regressing Relative Rewards. CoRR abs/2404.16767 (2024) - [i121]Jason D. Lee, Kazusato Oko, Taiji Suzuki, Denny Wu:
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit. CoRR abs/2406.01581 (2024) - [i120]Zixuan Wang, Stanley Wei, Daniel Hsu, Jason D. Lee:
Transformers Provably Learn Sparse Token Selection While Fully-Connected Nets Cannot. CoRR abs/2406.06893 (2024) - [i119]Licong Lin, Jingfeng Wu, Sham M. Kakade, Peter L. Bartlett, Jason D. Lee:
Scaling Laws in Linear Regression: Compute, Parameters, and Data. CoRR abs/2406.08466 (2024) - [i118]Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee:
Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity. CoRR abs/2406.19617 (2024) - [i117]Audrey Huang, Wenhao Zhan, Tengyang Xie, Jason D. Lee, Wen Sun, Akshay Krishnamurthy, Dylan J. Foster:
Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization. CoRR abs/2407.13399 (2024) - 2023
- [j10]Wenhao Zhan, Shicong Cen, Baihe Huang, Yuxin Chen, Jason D. Lee, Yuejie Chi:
Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence. SIAM J. Optim. 33(2): 1061-1091 (2023) - [c97]Hanlin Zhu, Ruosong Wang, Jason D. Lee:
Provably Efficient Reinforcement Learning via Surprise Bound. AISTATS 2023: 4006-4032 - [c96]Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee:
Optimal Sample Complexity Bounds for Non-convex Optimization under Kurdyka-Lojasiewicz Condition. AISTATS 2023: 6806-6821 - [c95]Kurtland Chua, Qi Lei, Jason D. Lee:
Provable Hierarchy-Based Meta-Reinforcement Learning. AISTATS 2023: 10918-10967 - [c94]Alex Damian, Eshaan Nichani, Jason D. Lee:
Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability. ICLR 2023 - [c93]Zhuoqing Song, Jason D. Lee, Zhuoran Yang:
Can We Find Nash Equilibria at a Linear Rate in Markov Games? ICLR 2023 - [c92]Wenhao Zhan, Jason D. Lee, Zhuoran Yang:
Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games. ICLR 2023 - [c91]Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee:
PAC Reinforcement Learning for Predictive State Representations. ICLR 2023 - [c90]Hadi Daneshmand, Jason D. Lee, Chi Jin:
Efficient displacement convex optimization with particle gradient descent. ICML 2023: 6836-6854 - [c89]Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D. Lee, Dimitris Papailiopoulos:
Looped Transformers as Programmable Computers. ICML 2023: 11398-11442 - [c88]Jikai Jin, Zhiyuan Li, Kaifeng Lyu, Simon Shaolei Du, Jason D. Lee:
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing. ICML 2023: 15200-15238 - [c87]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. ICML 2023: 34615-34641 - [c86]Yulai Zhao, Zhuoran Yang, Zhaoran Wang, Jason D. Lee:
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning. ICML 2023: 42200-42226 - [c85]Xinyi Chen, Edgar Minasyan, Jason D. Lee, Elad Hazan:
Regret Guarantees for Online Deep Control. L4DC 2023: 1032-1045 - [c84]Gen Li, Wenhao Zhan, Jason D. Lee, Yuejie Chi, Yuxin Chen:
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning. NeurIPS 2023 - [c83]Alex Damian, Eshaan Nichani, Rong Ge, Jason D. Lee:
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models. NeurIPS 2023 - [c82]Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Alex Damian, Jason D. Lee, Danqi Chen, Sanjeev Arora:
Fine-Tuning Language Models with Just Forward Passes. NeurIPS 2023 - [c81]Eshaan Nichani, Alex Damian, Jason D. Lee:
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks. NeurIPS 2023 - [c80]Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage. NeurIPS 2023 - [c79]Jingfeng Wu, Vladimir Braverman, Jason D. Lee:
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability. NeurIPS 2023 - [c78]Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee:
Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms. NeurIPS 2023 - [i116]Jikai Jin, Zhiyuan Li, Kaifeng Lyu, Simon S. Du, Jason D. Lee:
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing. CoRR abs/2301.11500 (2023) - [i115]Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D. Lee, Dimitris S. Papailiopoulos:
Looped Transformers as Programmable Computers. CoRR abs/2301.13196 (2023) - [i114]Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Refined Value-Based Offline RL under Realizability and Partial Coverage. CoRR abs/2302.02392 (2023) - [i113]Hadi Daneshmand, Jason D. Lee, Chi Jin:
Efficient displacement convex optimization with particle gradient descent. CoRR abs/2302.04753 (2023) - [i112]Hanlin Zhu, Ruosong Wang, Jason D. Lee:
Provably Efficient Reinforcement Learning via Surprise Bound. CoRR abs/2302.11634 (2023) - [i111]Zhuoqing Song, Jason D. Lee, Zhuoran Yang:
Can We Find Nash Equilibria at a Linear Rate in Markov Games? CoRR abs/2303.03095 (2023) - [i110]Yulai Zhao, Zhuoran Yang, Zhaoran Wang, Jason D. Lee:
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning. CoRR abs/2305.04819 (2023) - [i109]Eshaan Nichani, Alex Damian, Jason D. Lee:
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks. CoRR abs/2305.06986 (2023) - [i108]Gen Li, Wenhao Zhan, Jason D. Lee, Yuejie Chi, Yuxin Chen:
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning. CoRR abs/2305.10282 (2023) - [i107]Alex Damian, Eshaan Nichani, Rong Ge, Jason D. Lee:
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models. CoRR abs/2305.10633 (2023) - [i106]Jingfeng Wu, Vladimir Braverman, Jason D. Lee:
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability. CoRR abs/2305.11788 (2023) - [i105]Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Provable Offline Reinforcement Learning with Human Feedback. CoRR abs/2305.14816 (2023) - [i104]Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Alex Damian, Jason D. Lee, Danqi Chen, Sanjeev Arora:
Fine-Tuning Language Models with Just Forward Passes. CoRR abs/2305.17333 (2023) - [i103]Ziang Song, Tianle Cai, Jason D. Lee, Weijie J. Su:
Reward Collapse in Aligning Large Language Models. CoRR abs/2305.17608 (2023) - [i102]Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee:
How to Query Human Feedback Efficiently in RL? CoRR abs/2305.18505 (2023) - [i101]Etash Kumar Guha, Jason D. Lee:
Solving Robust MDPs through No-Regret Dynamics. CoRR abs/2305.19035 (2023) - [i100]Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee:
Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms. CoRR abs/2306.12383 (2023) - [i99]Tianle Cai, Kaixuan Huang, Jason D. Lee, Mengdi Wang:
Scaling In-Context Demonstrations with Structured Attention. CoRR abs/2307.02690 (2023) - [i98]Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, Kangwook Lee, Dimitris Papailiopoulos:
Teaching Arithmetic to Small Transformers. CoRR abs/2307.03381 (2023) - [i97]Zihan Zhang, Yuxin Chen, Jason D. Lee, Simon S. Du:
Settling the Sample Complexity of Online Reinforcement Learning. CoRR abs/2307.13586 (2023) - [i96]Zhenyu He, Zexuan Zhong, Tianle Cai, Jason D. Lee, Di He:
REST: Retrieval-Based Speculative Decoding. CoRR abs/2311.08252 (2023) - [i95]Yulai Zhao, Wenhao Zhan, Xiaoyan Hu, Ho-fung Leung, Farzan Farnia, Wen Sun, Jason D. Lee:
Provably Efficient CVaR RL in Low-rank MDPs. CoRR abs/2311.11965 (2023) - [i94]Zihao Wang, Eshaan Nichani, Jason D. Lee:
Learning Hierarchical Polynomials with Three-Layer Neural Networks. CoRR abs/2311.13774 (2023) - [i93]Kaifeng Lyu, Jikai Jin, Zhiyuan Li, Simon S. Du, Jason D. Lee, Wei Hu:
Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking. CoRR abs/2311.18817 (2023) - [i92]Zihan Zhang, Wenhao Zhan, Yuxin Chen, Simon S. Du, Jason D. Lee:
Optimal Multi-Distribution Learning. CoRR abs/2312.05134 (2023) - [i91]Baihe Huang, Banghua Zhu, Hanlin Zhu, Jason D. Lee, Jiantao Jiao, Michael I. Jordan:
Towards Optimal Statistical Watermarking. CoRR abs/2312.07930 (2023) - 2022
- [c77]Yulai Zhao, Yuandong Tian, Jason D. Lee, Simon S. Du:
Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games. AISTATS 2022: 2736-2761 - [c76]Itay Safran, Jason D. Lee:
Optimization-Based Separations for Neural Networks. COLT 2022: 3-64 - [c75]Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, Jason D. Lee:
Offline Reinforcement Learning with Realizability and Single-policy Concentrability. COLT 2022: 2730-2775 - [c74]Alexandru Damian, Jason D. Lee, Mahdi Soltanolkotabi:
Neural Networks can Learn Representations with Gradient Descent. COLT 2022: 5413-5452 - [c73]DiJia Su, Jason D. Lee, John M. Mulvey, H. Vincent Poor:
Competitive Multi-Agent Reinforcement Learning with Self-Supervised Representation. ICASSP 2022: 4098-4102 - [c72]Baihe Huang, Jason D. Lee, Zhaoran Wang, Zhuoran Yang:
Towards General Function Approximation in Zero-Sum Markov Games. ICLR 2022 - [c71]Zhiyuan Li, Tianhao Wang, Jason D. Lee, Sanjeev Arora:
Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent. NeurIPS 2022 - [c70]Eshaan Nichani, Yu Bai, Jason D. Lee:
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials. NeurIPS 2022 - [c69]Christopher De Sa, Satyen Kale, Jason D. Lee, Ayush Sekhari, Karthik Sridharan:
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. NeurIPS 2022 - [c68]Itay Safran, Gal Vardi, Jason D. Lee:
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias. NeurIPS 2022 - [c67]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. NeurIPS 2022 - [i90]Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, Jason D. Lee:
Offline Reinforcement Learning with Realizability and Single-policy Concentrability. CoRR abs/2202.04634 (2022) - [i89]Jiaqi Yang, Qi Lei, Jason D. Lee, Simon S. Du:
Nearly Minimax Algorithms for Linear Bandits with Shared Representation. CoRR abs/2203.15664 (2022) - [i88]Itay Safran, Gal Vardi, Jason D. Lee:
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias. CoRR abs/2205.09072 (2022) - [i87]Wenhao Zhan, Jason D. Lee, Zhuoran Yang:
Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games. CoRR abs/2206.01588 (2022) - [i86]Eshaan Nichani, Yu Bai, Jason D. Lee:
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials. CoRR abs/2206.03688 (2022) - [i85]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. CoRR abs/2206.12020 (2022) - [i84]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. CoRR abs/2206.12081 (2022) - [i83]Alex Damian, Jason D. Lee, Mahdi Soltanolkotabi:
Neural Networks can Learn Representations with Gradient Descent. CoRR abs/2206.15144 (2022) - [i82]Zhiyuan Li, Tianhao Wang, Jason D. Lee, Sanjeev Arora:
Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent. CoRR abs/2207.04036 (2022) - [i81]Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee:
PAC Reinforcement Learning for Predictive State Representations. CoRR abs/2207.05738 (2022) - [i80]Alex Damian, Eshaan Nichani, Jason D. Lee:
Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability. CoRR abs/2209.15594 (2022) - [i79]Satyen Kale, Jason D. Lee, Chris De Sa, Ayush Sekhari, Karthik Sridharan:
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. CoRR abs/2210.06705 (2022) - [i78]Zihan Wang, Jason D. Lee, Qi Lei:
Reconstructing Training Data from Model Gradient, Provably. CoRR abs/2212.03714 (2022) - 2021
- [j9]Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan:
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift. J. Mach. Learn. Res. 22: 98:1-98:76 (2021) - [j8]Songtao Lu, Jason D. Lee, Meisam Razaviyayn, Mingyi Hong:
Linearized ADMM Converges to Second-Order Stationary Points for Non-Convex Problems. IEEE Trans. Signal Process. 69: 4859-4874 (2021) - [c66]Cong Fang, Jason D. Lee, Pengkun Yang, Tong Zhang:
Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks. COLT 2021: 1887-1936 - [c65]Jeff Z. HaoChen, Colin Wei, Jason D. Lee, Tengyu Ma:
Shape Matters: Understanding the Implicit Bias of the Noise Covariance. COLT 2021: 2315-2357 - [c64]Simon Shaolei Du, Wei Hu, Sham M. Kakade, Jason D. Lee, Qi Lei:
Few-Shot Learning via Learning the Representation, Provably. ICLR 2021 - [c63]Jiaqi Yang, Wei Hu, Jason D. Lee, Simon Shaolei Du:
Impact of Representation Learning in Linear Bandits. ICLR 2021 - [c62]Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham M. Kakade, Huan Wang, Caiming Xiong:
How Important is the Train-Validation Split in Meta-Learning? ICML 2021: 543-553 - [c61]Tianle Cai, Ruiqi Gao, Jason D. Lee, Qi Lei:
A Theory of Label Propagation for Subpopulation Shift. ICML 2021: 1170-1182 - [c60]Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang:
Bilinear Classes: A Structural Framework for Provable Generalization in RL. ICML 2021: 2826-2836 - [c59]Qi Lei, Wei Hu, Jason D. Lee:
Near-Optimal Linear Regression under Distribution Shift. ICML 2021: 6164-6174 - [c58]Jason D. Lee, Qi Lei, Nikunj Saunshi, Jiacheng Zhuo:
Predicting What You Already Know Helps: Provable Self-Supervised Learning. NeurIPS 2021: 309-323 - [c57]Kurtland Chua, Qi Lei, Jason D. Lee:
How Fine-Tuning Allows for Effective Meta-Learning. NeurIPS 2021: 8871-8884 - [c56]Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang:
Going Beyond Linear RL: Sample Efficient Neural Function Approximation. NeurIPS 2021: 8968-8983 - [c55]