Jingfeng Wu

Jingfeng Wu / 吴京风

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

During summer 2022, I was fortunate to intern at Google Research. My hosts were Wennan Zhu and Peter Kairuoz.

Email / CV / Google Scholar / Github / Twitter

News

[09/2022] Looking for a post doc position starting from summer 2023. Please contact me if interested!
[09/2022] Two papers are accepted to NeurIPS 2022!

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

[05/2022] One paper is accepted to ICML 2022 as long presentation!

[01/2022] One paper is accepted to AISTATS 2022.

[12/2021] Passed the PhD candidacy exam.

[09/2021] Two papers are accepted to NeurIPS 2021.

[09/2021] Talk on problem-dependent bound of constant-stepsize SGD at JHU CS Theory Seminar.

[05/2021] Awarded MINDS Summer Data Science Fellowship. Many thanks to the committee.

[05/2021] One paper is accepted to COLT 2021.

[03/2021] Talk on the implicit bias of SGD at UCLA.

[02/2021] In a relationship with Yuan, happy Valentine's day~

[01/2021] One paper is accepted to ICLR 2021.

[12/2020] Talk on the implicit bias of SGD at JHU CS Theory Seminar.

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

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

[03/2020] Stay home stay safe.

[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 is accepted to ICML 2019.

[03/2019] One paper is accepted to CVPR 2019 as an oral presentation.

[12/2018] Looking for a Ph.D. position in machine learning / statistical learning starting from fall, 2019.

Research

My research focuses on providing theoretical understanding to machine learning problems stemming from practice.

I am interested in understanding how algorithm "adapts" to problem in the statistical learning context, with a focus on studying algorithm-problem-dependent performance guarantees.
I am also interested in developing efficient learning algorithms, with an aim to connect theoretical guarantees with practical motivations.

Key Words: Machine Learning, Reinforcement Learning, Stochastic Gradient Descent, Sketching

Preprints

Publications

	The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift Jingfeng Wu^, Difan Zou^, Vladimir Braverman, Quanquan Gu, Sham M. Kakade Conference on Neural Information Processing Systems (NeurIPS), 2022 bibtex / arXiv / 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 Conference on Neural Information Processing Systems (NeurIPS), 2022 bibtex / arXiv
	Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression Jingfeng Wu^, Difan Zou^, Vladimir Braverman, Quanquan Gu, Sham M. Kakade International Conference on Machine Learning (ICML), 2022, long presentation bibtex / arXiv / slides / poster
	Gap-Dependent Unsupervised Exploration for Reinforcement Learning Jingfeng Wu, Vladimir Braverman, Lin F. Yang International Conference on Artificial Intelligence and Statistics (AISTATS), 2022 bibtex / arXiv / slides / poster / code
	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 Conference on Neural Information Processing Systems (NeurIPS), 2021 bibtex / arXiv
	Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning Jingfeng Wu, Vladimir Braverman, Lin F. Yang Conference on Neural Information Processing Systems (NeurIPS), 2021 bibtex / arXiv / slides / poster / code
	Lifelong Learning with Sketched Structural Regularization Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, Vladimir Braverman Asian Conference on Machine Learning (ACML), 2021 bibtex / arXiv
	Benign Overfitting of Constant-Stepsize SGD for Linear Regression Difan Zou^, Jingfeng Wu^, Vladimir Braverman, Quanquan Gu, Sham M. Kakade Annual Conference on Learning Theory (COLT), 2021 bibtex / arXiv / slides
	Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu International Conference on Learning Representations (ICLR), 2021 bibtex / arXiv / slides / poster
	Obtaining Adjustable Regularization for Free via Iterate Averaging Jingfeng Wu, Vladimir Braverman, Lin F. Yang International Conference on Machine Learning (ICML), 2020 bibtex / arXiv / slides / code
	On the Noisy Gradient Descent that Generalizes as SGD Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu International Conference on Machine Learning (ICML), 2020 bibtex / arXiv / slides / code
	Tangent-Normal Adversarial Regularization for Semi-supervised Learning Bing Yu^, Jingfeng Wu^, Jinwen Ma, Zhanxing Zhu Conference on Computer Vision and Pattern Recognition (CVPR), 2019, oral bibtex / arXiv / 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 International Conference on Machine Learning (ICML), 2019 bibtex / arXiv / slides / poster / code

Talks

Learning theory seminar, Google, August 2022
A Fine-Grained Characterization for the Implicit Bias of SGD in Least Square Problems

Math Machine Learning seminar, MPI MiS + UCLA, June 2022
A Fine-Grained Characterization for the Implicit Bias of SGD in Least Square Problems

Services

Conference Reviewer: ICML 2020 - 2022; NeurIPS 2020 - 2022; ICLR 2021 - 2023; AISTATS 2021 - 2022
PC Member: AAAI 2021 - 2023
Journal Reviewer: TPAMI; JMLR; TMLR

Awards

ICML 2021 Best Reviewers (Top 10%)
JHU MINDS 2021 Summer Data Science Fellowship

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