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Jason D. Lee

59 papers · 2015–2026 · 7 venues · across top CS/AI venues

This profile combines several different researchers. Authors are keyed by name, and this name's papers fall into 17 separate collaboration groups - the largest holds only 44% of them. Pick the group below that matches the researcher you mean. How a shared name is split.

Achievements are not shown for a combined profile - each one describes a single researcher's record, which this is not.

Which Jason D. Lee?

9 groups of papers that share co-authors internally but none with each other - each most likely a different researcher. Pick one to see its papers.

with Wenhao Zhan, Wen Sun, Simon Shaolei Du 26 papers · 2021–2026 · ICLR, ICML, NeurIPS Hide ▲
Risk Comparisons in Linear Regression: Implicit Regularization Dominates Explicit Regularization (Extended Abstract) COLT 2026 Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization ICLR 2025 Discrepancies are Virtue: Weak-to-Strong Generalization through Lens of Intrinsic Dimension ICML 2025 Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank ICLR 2025 Minimax Optimal Regret Bound for Reinforcement Learning with Trajectory Feedback ICML 2025 Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHF ICLR 2025 An Information-Theoretic Analysis of In-Context Learning ICML 2024 Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking ICLR 2024 Horizon-Free Regret for Linear Markov Decision Processes ICLR 2024 Provable Offline Preference-Based Reinforcement Learning ICLR 2024 Provable Reward-Agnostic Preference-Based Reinforcement Learning ICLR 2024 Provably Efficient CVaR RL in Low-rank MDPs ICLR 2024 REBEL: Reinforcement Learning via Regressing Relative Rewards NeurIPS 2024 Scaling Laws in Linear Regression: Compute, Parameters, and Data NeurIPS 2024 Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity NeurIPS 2024 Can We Find Nash Equilibria at a Linear Rate in Markov Games? ICLR 2023 Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings ICML 2023 Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games ICLR 2023 Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning ICML 2023 Optimal Sample Complexity Bounds for Non-convex Optimization under Kurdyka-Lojasiewicz Condition AISTATS 2023 PAC Reinforcement Learning for Predictive State Representations ICLR 2023 Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing ICML 2023 Towards General Function Approximation in Zero-Sum Markov Games ICLR 2022 Few-Shot Learning via Learning the Representation, Provably ICLR 2021 Impact of Representation Learning in Linear Bandits ICLR 2021 On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift JMLR 2021
8 papers whose co-authors appear nowhere else under this name
LoRA Training in the NTK Regime has No Spurious Local Minima ICML 2024 Efficient displacement convex optimization with particle gradient descent ICML 2023 Regret Guarantees for Online Deep Control L4DC 2023 Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks ICLR 2020 Kernel and Rich Regimes in Overparametrized Models COLT 2020 When is a Convolutional Filter Easy to Learn? ICLR 2018 Communication-efficient Sparse Regression JMLR 2017 Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares JMLR 2015

Venues · all papers under this name

ICLR (25) ICML (16) NeurIPS (6) COLT (5) JMLR (4) AISTATS (2) L4DC (1)