Jason Lee

About

Jason D. Lee is an associate professor of Electrical Engineering and Computer Science (EECS) and Statistics at UC Berkeley. His research focuses on machine learning theory, data and information science, and related areas. Prior to his current position, he was a research scientist at Google Deepmind, a member of the Institute for Advanced Study (IAS), and an associate professor at Princeton University. He also completed a postdoctoral fellowship at UC Berkeley working with Michael I. Jordan. Dr. Lee earned his Ph.D. in Computational and Mathematical Engineering from Stanford University in 2015, where he was advised by Trevor Hastie and Jonathan Taylor. He holds a B.Sc. in Mathematics from Duke University, advised by Mauro Maggioni. His work has been recognized through funding from the Navy's Young Investigator Program, and he is known for his expertise in machine learning theory.

Research topics

• Computer science
• Mathematics
• Mathematical optimization
• Algorithm
• Artificial intelligence

Selected publications

• Statistical inference for model parameters in stochastic gradient descent

  The Annals of Statistics · 2020-02-01 · 91 citations

  articleOpen access

  The stochastic gradient descent (SGD) algorithm has been widely used in statistical estimation for large-scale data due to its computational and memory efficiency. While most existing works focus on the convergence of the objective function or the error of the obtained solution, we investigate the problem of statistical inference of true model parameters based on SGD when the population loss function is strongly convex and satisfies certain smoothness conditions. Our main contributions are twofo…

  PublisherOA PDFDOI

• Settling the Sample Complexity of Online Reinforcement Learning

  Journal of the ACM · 2025-05-02 · 2 citations

  articleOpen access

  A central issue lying at the heart of online reinforcement learning (RL) is data efficiency. While a number of recent works achieved asymptotically minimal regret in online RL, the optimality of these results is only guaranteed in a “large-sample” regime, imposing enormous burn-in cost in order for their algorithms to operate optimally. How to achieve minimax-optimal regret without incurring any burn-in cost has been an open problem in RL theory. We settle this problem for finite-horizon inhomog…

  PublisherDOI

• Risk Comparisons in Linear Regression: Implicit Regularization Dominates Explicit Regularization

  ArXiv.org · 2025-09-21 · 1 citations

  preprintOpen access

  Existing theory suggests that for linear regression problems categorized by capacity and source conditions, gradient descent (GD) is always minimax optimal, while both ridge regression and online stochastic gradient descent (SGD) are polynomially suboptimal for certain categories of such problems. Moving beyond minimax theory, this work provides instance-wise comparisons of the finite-sample risks for these algorithms on any well-specified linear regression problem. Our analysis yields three key…

  PublisherOA PDFDOI

• LZ Penalty: An information-theoretic repetition penalty for autoregressive language models

  ArXiv.org · 2025-04-28

  preprintOpen access

  We introduce the LZ penalty, a penalty specialized for reducing degenerate repetitions in autoregressive language models without loss of capability. The penalty is based on the codelengths in the LZ77 universal lossless compression algorithm. Through the lens of the prediction-compression duality, decoding the LZ penalty has the interpretation of sampling from the residual distribution after removing the information that is highly compressible. We demonstrate the LZ penalty enables state-of-the-…

  PublisherOA PDFDOI

• On the Statistical Query Complexity of Learning Semiautomata: a Random Walk Approach

  ArXiv.org · 2025-10-05

  preprintOpen accessSenior author

  Semiautomata form a rich class of sequence-processing algorithms with applications in natural language processing, robotics, computational biology, and data mining. We establish the first Statistical Query hardness result for semiautomata under the uniform distribution over input words and initial states. We show that Statistical Query hardness can be established when both the alphabet size and input length are polynomial in the number of states. Unlike the case of deterministic finite automata,…

  PublisherOA PDFDOI

Recent grants

• CIF: Medium: Collaborative Research: Theory of Optimization Geometry and Algorithms for Neural Networks

  NSF · $400k · 2019–2024

Frequent coauthors

• Simon S. Du

  38 shared

• Sham M. Kakade

  25 shared

• Meisam Razaviyayn

  23 shared

• Nathan Srebro

  20 shared

• Yuekai Sun

  20 shared

• Michael I. Jordan

  20 shared

• Suriya Gunasekar

  18 shared

• Daniel Soudry

  18 shared

Labs

• Jason D. Lee LabPI

Education

• Postdoc, Computer Science

  University of California Berkeley

  2016

• PhD, Computational Math

  Stanford University

  2015

• BS, Mathematics

  Duke University

  2010

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