Jingfeng Wu  |  吴京风

My research focuses on bridging the gap between theory and practice in machine learning by developing efficient algorithms to solve real-world problems and providing a deep understanding of the underlying theoretical principles.

I work on the theory and algorithms of deep learning, and related topics in algorithms, machine learning, optimization, and statistical learning theory.

• [07/2025] Invited talk at the 6th Youth in High-Dimensions Conference.
• [05/2025] Invited talk at SIAM DS25.
• [05/2025] Three papers accepted to ICML 2025.
• [02/2025] Organizing a deep learning theory workshop at Simons.
• [01/2025] One paper accepted to ICLR 2025.

Prior to 2025

• [09/2024] Three papers accepted to NeurIPS 2024; huge congrats to Yuhang, Licong, and Ruiqi!!
• [05/2024] One paper accepted to COLT 2024.
• [03/2024] Invited talk at UCLA CS.
• [02/2024] Invited talk at UC Berkeley Biostatistics.
• [01/2024] Two papers accepted to ICLR 2024.
• [10/2023] Selected as Rising Star in Data Science by UChicago and UCSD.
• [09/2023] Two papers accepted to NeurIPS 2023.
• [08/2023] Joining the Simons Institute at UC Berkeley as a postdoc.
• [06/2023] Defended my PhD dissertation!
• [05/2023] One paper accepted to CoLLAs 2023; congrats to Haoran!
• [04/2023] One paper accepted to ICML 2023.
• [09/2022] Two papers accepted to NeurIPS 2022.
• [06/2022] Interning at Google Research Seattle.
• [05/2022] One paper accepted to ICML 2022 as long presentation!
• [01/2022] One paper accepted to AISTATS 2022.
• [12/2021] Passed the PhD candidacy exam.
• [09/2021] Two papers accepted to NeurIPS 2021.
• [05/2021] Awarded MINDS Summer Data Science Fellowship!
• [05/2021] One paper accepted to COLT 2021.
• [02/2021] In a relationship with Yuan, happy Valentine's day~
• [01/2021] One paper is accepted to ICLR 2021.
• [05/2020] Two papers accepted to ICML 2020.
• [09/2019] Joining Hopkins. Veritas vos liberabit!
• [06/2019] Graduated from Peking University.
• [04/2019] One paper accepted to ICML 2019.
• [03/2019] One paper accepted to CVPR 2019 as an oral presentation.
• [12/2018] Looking for a Ph.D. position in machine learning.

Optimization with Large Stepsizes (Slides)

Statistical Approaches to Implicit Regularization (Slides 1)

Applications of Implicit Regularization

Margin Theory for Neural Networks

• Reimagining Gradient Descent: Large Stepsize, Oscillation, and Acceleration
  • [06/2025] MPI & UCLA, Math Machine Learning Seminar, hosted by Guido Montufar
  • [05/2025] SIAM DS25, Dynamical Systems for Machine Learning, hosted by Molei Tao
  • [01/2025] UCLA, Level Set Meeting, hosted by Shu Liu, Stanley Osher
  • [09/2024] Simons Foundation, MoDL Annual Meeting, hosted by Peter Bartlett, Rene Vidal
  • [05/2024] UC San Diego, MoDL Collaboration Meeting, hosted by Chaoyue Liu et al.
  • [03/2024] UCLA, Computer Science Seminar, hosted by Quanquan Gu
  • [02/2024] UC Berkeley, Biostatistics Seminar, hosted by Lexin Li
  • [02/2024] UC San Diego, Group Seminar, hosted by Mikhail Belkin
• A Statistical Viewpoint on Implicit Regularization: GD for Logistic Regression
  • [07/2025] ICTP, 6th Youth in High-Dimensions Conference, hosted by Marco Mondelli et al.
  • [02/2025] UC Berkeley, Deep Learning Theory Workshop, hosted by Peter Bartlett et al.

Prior to 2024

• New Insights about SGD: Stepsize, Risk Convergence, and Implicit Regularization
  • [10/2023] UC Davis, Statistics Seminar, hosted by Xiao Hui Tai
• Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability
  • [05/2023] TTIC, MoDL Collaboration Meeting, hosted by Sam Buchanan et al.
• The Implicit Regularization of SGD in Least Squares and Beyond
  • [01/2023] Rice University, Algorithms and ML Seminar, hosted by Anastasios Kyrillidis
  • [12/2022] Princeton University, Group Seminar, hosted by Jason Lee
  • [11/2022] Georgia Tech, Group Seminar, hosted by Molei Tao
  • [08/2022] Google Research, Learning Theory Seminar, hosted by Mehryar Mohri
  • [06/2022] MPI & UCLA, Math Machine Learning Seminar, hosted by Guido Montufar

• Organizer

  • [02/2025] Deep learning theory workshop, Simons Institute, UC Berkeley

• Conference Reviewer
  ICML (2020 - 2025), NeurIPS (2020 - 2025), ICLR (2021 - 2025), AISTATS (2021 - 2023), UAI (2023)

• Conference Program Committee Member
  AAAI (2021 - 2023)

• Journal Reviewer
  JMLR, TPAMI, TMLR, SIMODS, JAIR, Applied Probability Journals, IEEE Transactions on Information Theory,

Recent Student Collaborators

Yuhang Cai    Licong Lin    Ruiqi Zhang   

Collaborators

Amir Abedsoltan    Peter Bartlett    Mikhail Belkin    Vladimir Braverman    Zixiang Chen    Yihe Deng    Udaya Ghai    Quanquan Gu    Peter Kairouz    Sham Kakade    Jason Lee    Haoran Li    Xuheng Li    Michael Lindsey    Pierre Marion    Song Mei    Depen Morwani    Adit Radhakrishnan    Matus Telgarsky    Nikhil Vyas    Lin Yang    Bin Yu    Hanlin Zhang    Wennan Zhu    Dongruo Zhou    Kangjie Zhou    Difan Zou