Jingfeng Wu | 吴京风

I am a Ph.D. candidate (2019 - 2023, expected) at Johns Hopkins University, Computer Science Department. I am fortunate to be supervised by Prof. Vladimir Braverman. Previously I obtained B.S. in Mathematics (2012 - 2016) and M.S. in Applied Math (2016 - 2019) from Peking University, School of Mathematical Sciences.

Starting in fall 2023, I will be joining the Simons Institute for the Theory of Computing at UC Berkeley as a postdoc, hosted by Prof. Peter Bartlett and Prof. Bin Yu.

Email | CV | Short Bio | Twitter

Research

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 am interested in algorithms, machine learning, optimization, and statistical learning theory.

BibTeX | arXiv | Google Scholar | Semantic Scholar | Github

News

[05/2023] One paper accepted to CoLLAs 2023; congrats to Haoran!
[04/2023] One paper accepted to ICML 2023.
[03/2023] I will join the Simons Institute for the Theory of Computing at UC Berkeley as a postdoc.

[09/2022] Two papers accepted to NeurIPS 2022.

[06/2022] Interning at Google Research Seattle during summer 2022.

[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.

[10/2020] One paper will be presented at Workshop on Optimization for Machine Learning (OPT).

[05/2020] Two papers accepted to ICML 2020.

[11/2019] One paper will be orally presented at Workshop on Optimization for Machine Learning (OPT).

[09/2019] Starting working at 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 starting from fall, 2019.

Pulications

Preprints

Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability
Jingfeng Wu, Vladimir Braverman, Jason D. Lee
arXiv 2023 | bib | slides

Conference Papers

Fixed Design Analysis of Regularization-Based Continual Learning
Haoran Li*, Jingfeng Wu*, Vladimir Braverman
CoLLAs 2023 | bib
Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron
Jingfeng Wu*, Difan Zou*, Zixiang Chen*, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
ICML 2023 | bib
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift
Jingfeng Wu*, Difan Zou*, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
NeurIPS 2022 | bib | slides | poster
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime
Difan Zou*, Jingfeng Wu*, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
NeurIPS 2022 | bib
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression
Jingfeng Wu*, Difan Zou*, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
ICML 2022 (long presentation) | bib | slides | poster
Gap-Dependent Unsupervised Exploration for Reinforcement Learning
Jingfeng Wu, Vladimir Braverman, Lin F. Yang
AISTATS 2022 | bib | slides | poster
The Benefits of Implicit Regularization from SGD in Least Squares Problems
Difan Zou*, Jingfeng Wu*, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade
NeurIPS 2021 | bib
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning
Jingfeng Wu, Vladimir Braverman, Lin F. Yang
NeurIPS 2021 | bib | slides | poster
Lifelong Learning with Sketched Structural Regularization
Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, Vladimir Braverman
ACML 2021 | bib
Benign Overfitting of Constant-Stepsize SGD for Linear Regression
Difan Zou*, Jingfeng Wu*, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
COLT 2021 | bib | slides
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu
ICLR 2021 | bib | slides | poster
Obtaining Adjustable Regularization for Free via Iterate Averaging
Jingfeng Wu, Vladimir Braverman, Lin F. Yang
ICML 2020 | bib | slides | code
On the Noisy Gradient Descent that Generalizes as SGD
Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu
ICML 2020 | bib | slides | code
Tangent-Normal Adversarial Regularization for Semi-supervised Learning
Bing Yu*, Jingfeng Wu*, Jinwen Ma, Zhanxing Zhu
CVPR 2019 (oral) | bib | slides | poster | code
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Minima and Regularization Effects
Zhanxing Zhu*, Jingfeng Wu*, Bing Yu, Lei Wu, Jinwen Ma
ICML 2019 | bib | slides | poster | code

Services

Conference Reviewer

International Conference on Machine Learning (ICML), 2020 - 2023
Conference on Neural Information Processing Systems (NeurIPS), 2020 - 2023
International Conference on Learning Representations (ICLR), 2021 - 2023
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021 - 2022
Conference on Uncertainty in Artificial Intelligence (UAI), 2023

Conference Program Committee Member

AAAI Conference on Artificial Intelligence (AAAI), 2021 - 2023

Journal Reviewer

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Journal of Machine Learning Research (JMLR)
Transactions on Machine Learning Research (TMLR)
Applied Probability Journals

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