← Authors

Jason D. Lee

56 papers · 2015–2025 · 7 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (7) πŸƒ Academic Marathon (10) 🌈 Renaissance Researcher (5) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (11) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (17) 🧭 Keyword Pioneer 🏠 Conference Loyalist (25) πŸ‘‘ Triple Crown πŸ—ƒοΈ Keyword Collector (122) ⚑ Prolific Year (19) πŸš€ Conference Pioneer πŸ’Ž Century Club (56) πŸ”₯ Unstoppable (6) πŸ“ˆ Trend Setter ❓ The Questioner (3)

Conferences

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

Top co-authors

Wenhao Zhan (9) Wen Sun (8) Simon Shaolei Du (6) Qi Lei (5) Eshaan Nichani (5) Masatoshi Uehara (4) Zhuoran Yang (4) Dimitris Papailiopoulos (3) Sham M. Kakade (3) Baihe Huang (3)

Keywords

sample complexity (4) gradient descent (3) reinforcement learning (3) neural network (3) stochastic gradient descent (2) policy optimization (2) implicit bia (2) language modeling (2) neural network optimization (2) neural network training (1) transfer learning (1) model quantization (1) convex optimization (1) matrix factorization (1) non-convex optimization (1) preference learning (1) online learning (1) optimal transport (1) collaborative filtering (1) model selection (1)

Papers

Learning Hierarchical Polynomials of Multiple Nonlinear Features ICLR 2025
Transformers Provably Learn Two-Mixture of Linear Classification via Gradient Flow ICLR 2025
Understanding Optimization in Deep Learning with Central Flows ICLR 2025
Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization ICLR 2025
Understanding Factual Recall in Transformers via Associative Memories ICLR 2025
Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHF ICLR 2025
Rethinking Addressing in Language Models via Contextualized Equivariant Positional Encoding ICML 2025
How Well Can Transformers Emulate In-Context Newton’s Method? AISTATS 2025
Minimax Optimal Regret Bound for Reinforcement Learning with Trajectory Feedback ICML 2025
Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation ICML 2025
Discrepancies are Virtue: Weak-to-Strong Generalization through Lens of Intrinsic Dimension ICML 2025
Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought ICLR 2025
Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank ICLR 2025
Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads ICML 2024
Learning and Transferring Sparse Contextual Bigrams with Linear Transformers NIPS 2024
REBEL: Reinforcement Learning via Regressing Relative Rewards NIPS 2024
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit NIPS 2024
Scaling Laws in Linear Regression: Compute, Parameters, and Data NIPS 2024
Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity NIPS 2024
Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking ICLR 2024
Learning Hierarchical Polynomials with Three-Layer Neural Networks 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
Teaching Arithmetic to Small Transformers ICLR 2024
Provably Efficient CVaR RL in Low-rank MDPs ICLR 2024
BitDelta: Your Fine-Tune May Only Be Worth One Bit NIPS 2024
LoRA Training in the NTK Regime has No Spurious Local Minima ICML 2024
An Information-Theoretic Analysis of In-Context Learning ICML 2024
How Transformers Learn Causal Structure with Gradient Descent ICML 2024
Transformers Provably Learn Sparse Token Selection While Fully-Connected Nets Cannot ICML 2024
Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark ICML 2024
Can We Find Nash Equilibria at a Linear Rate in Markov Games? ICLR 2023
Regret Guarantees for Online Deep Control L4DC 2023
PAC Reinforcement Learning for Predictive State Representations ICLR 2023
Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games ICLR 2023
Efficient displacement convex optimization with particle gradient descent ICML 2023
Looped Transformers as Programmable Computers ICML 2023
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing ICML 2023
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings ICML 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
Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability ICLR 2023
Towards General Function Approximation in Zero-Sum Markov Games ICLR 2022
Few-Shot Learning via Learning the Representation, Provably ICLR 2021
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift JMLR 2021
Impact of Representation Learning in Linear Bandits ICLR 2021
Kernel and Rich Regimes in Overparametrized Models COLT 2020
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks ICLR 2020
When is a Convolutional Filter Easy to Learn? ICLR 2018
Learning One-hidden-layer Neural Networks with Landscape Design ICLR 2018
Communication-efficient Sparse Regression JMLR 2017
Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement JMLR 2017
Gradient Descent Only Converges to Minimizers COLT 2016
L1-regularized Neural Networks are Improperly Learnable in Polynomial Time ICML 2016
Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares JMLR 2015