Jingfeng Wu

127 posts

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Jingfeng Wu

Jingfeng Wu

@uuujingfeng

Postdoc @SimonsInstitute @UCBerkeley; alumnus of @JohnsHopkins @PKU1898; DL theory, opt, and stat learning.

Berkeley, CA Katılım Ekim 2013
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Jingfeng Wu
Jingfeng Wu@uuujingfeng·
sharing a new paper w Peter Bartlett, @jasondeanlee, @ShamKakade6, Bin Yu ppl talking about implicit regularization, but how good is it? We show its surprisingly effective, that GD dominates ridge for all linear regression, w/ more cool stuff on GD vs SGD arxiv.org/abs/2509.17251
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Hongxun Wu
Hongxun Wu@HongxunWu·
🧵(1/8) An @OpenAI internal reasoning LLM achieved an AI Math milestone: solving an open problem central to its mathematical subfield— in this case, the unit distance problem of discrete geometry. We came across it in a side quest to truly push our model on the hardest problems.
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Thomas G. Dietterich
Thomas G. Dietterich@tdietterich·
Attention @arxiv authors: Our Code of Conduct states that by signing your name as an author of a paper, each author takes full responsibility for all its contents, irrespective of how the contents were generated. 1/
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Berkeley Statistics
Berkeley Statistics@UCBStatistics·
Professor of the Graduate School Peter Bartlett is one of six Berkeley faculty members recently inducted into the National Academy of Sciences in 2026! #BerkeleyStats
National Academy of Sciences@theNASciences

We are thrilled to announce the election of 120 members and 25 international members to the National Academy of Sciences in recognition of their distinguished and continued achievements in original research. Congratulations to our new #NASmembers and welcome to the Academy! 🎊

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Jeremias Sulam
Jeremias Sulam@Jere_je_je·
Belated professional update 🔊 I’ve been promoted to Associate Professor with tenure at @JohnsHopkins @JHUBME. I’m incredibly thankful to my mentors over the past two decades, to my past and current collaborators (and friends!), and immensely proud of my students!
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Mahdi Soltanolkotabi
Mahdi Soltanolkotabi@mahdisoltanol·
Fun new theory paper on data reuse: 𝗙𝗨𝗟𝗟-𝗕𝗔𝗧𝗖𝗛 𝗚𝗗 can beat 𝗢𝗡𝗘-𝗣𝗔𝗦𝗦 𝗦𝗚𝗗 by a log-factor in samples. Same single-index model, same data: → 𝗚𝗗 recovers with n ≈ d → 𝗦𝗚𝗗 needs n ≳ d log d arxiv.org/abs/2602.02431
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Weijie Su
Weijie Su@weijie444·
It is my tremendous honor and privilege to receive the 2026 COPSS Presidents' Award. Statistics is powerful and will only grow more vital in the AI age. Grateful to my mentors, collaborators, colleagues, and students who made this journey possible.
The Wharton School@Wharton

Congratulations to Prof. Weijie Su (@weijie444) from our Statistics and Data Science Department on being named the recipient of this year's Committee of Presidents of Statistical Societies (@COPSSNews) Presidents' Award: whr.tn/3ZToJu9 The honor is given annually to a young member of the statistical community in recognition of outstanding contributions to the profession of statistics. It's jointly sponsored by five statistical societies: @AmstatNews, @ENAR_ibs, @InstMathStat, @SSC_stat, and @WNAR_ibs.

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The Wharton School
The Wharton School@Wharton·
Congratulations to Prof. Weijie Su (@weijie444) from our Statistics and Data Science Department on being named the recipient of this year's Committee of Presidents of Statistical Societies (@COPSSNews) Presidents' Award: whr.tn/3ZToJu9 The honor is given annually to a young member of the statistical community in recognition of outstanding contributions to the profession of statistics. It's jointly sponsored by five statistical societies: @AmstatNews, @ENAR_ibs, @InstMathStat, @SSC_stat, and @WNAR_ibs.
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Data Science Institute
Data Science Institute@DSI_UChicago·
Don't miss the next Statistics and DSI Joint Colloquium! @uuujingfeng, postdoc fellow at @SimonsInstitute at @UCBerkeley, presents 'Towards a Less Conservative Theory of Machine Learning: Unstable Optimization and Implicit Regularization' on Thursday, February 5th at DSI
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TTIC
TTIC@TTIC_Connect·
Thursday, December 11th at 11AM: Talks at TTIC presents Jingfeng Wu (@uuujingfeng) of @SimonsInstitute with a talk titled "A Statistical View on Implicit Regularization: Gradient Descent Dominates Ridge." Please join us in Room 530, 5th floor.
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Spencer Frei
Spencer Frei@sfrei_·
I'm hiring a Student Researcher to work on scaling laws at Google DeepMind! Project is for 16 weeks, starting spring/summer '26, in-person in SF (pic from the amazing office). If you're interested, fill out this form: forms.gle/MsgPfJumTLLobN…
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Sham Kakade
Sham Kakade@ShamKakade6·
1/6 Introducing Seesaw: a principled batch size scheduling algo. Seesaw achieves theoretically optimal serial run time given a fixed compute budget and also matches the performance of cosine annealing at fixed batch size.
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Jeremy Cohen
Jeremy Cohen@deepcohen·
Even with full-batch gradients, DL optimizers defy classical optimization theory, as they operate at the *edge of stability.* With @alex_damian_, we introduce "central flows": a theoretical tool to analyze these dynamics that makes accurate quantitative predictions on real NNs.
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Jingfeng Wu
Jingfeng Wu@uuujingfeng·
sharing a new paper w Peter Bartlett, @jasondeanlee, @ShamKakade6, Bin Yu ppl talking about implicit regularization, but how good is it? We show its surprisingly effective, that GD dominates ridge for all linear regression, w/ more cool stuff on GD vs SGD arxiv.org/abs/2509.17251
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Sadhika Malladi
Sadhika Malladi@SadhikaMalladi·
Excited to share that I will be starting as an Assistant Professor in CSE at UCSD (@ucsd_cse) in Fall 2026! I am currently recruiting PhD students who want to bridge theory and practice in deep learning - see here: cs.princeton.edu/~smalladi/recr…
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