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Kevin Han Huang

@kevhhuang.bsky.social

Founding researcher @ AI startup. Previously @ProbAIHub @warwickstats @Princeton @GatsbyUCL @Cambridge_Uni. ML theory & probability. He/him. 🌈 kevinhanhuang.page

15 Posts93 Follower77 Folgt
Kevin Han Huang@kevhhuang.bsky.social · 6mo ago

Given popular demands, we have resumed the registration for a short period of time until *30 Apr*! Those already on the waiting list are guaranteed a slot so long as you register by 30 Apr. Other additional slots are available on a first-come-first basis :)

Kevin Han Huang@kevhhuang.bsky.social · 9mo ago

Very excited to announce the ProbAI Theory of Scaling Laws Workshop (warwick.ac.uk/fac/sci/stat...) at @warwickstats.bsky.social, 22-24 June! (1/4)

Kevin Han Huang@kevhhuang.bsky.social · last yr.

Last but not least of my travel updates: Courtesy of the very kind @slchau.bsky.social, I'm giving a talk at 14:30, 27 Jun @NTU CCDS in SG on data augmentation & Gaussian universality, which strings together several works over my PhD. If you're in SG/Lyon over the next few weeks, let me know! :)

Kevin Han Huang@kevhhuang.bsky.social · last yr.

Meanwhile, excited to be in #Lyon for #COLT2025, with a co-first author paper (arxiv.org/abs/2502.15752) with the amazing team -- Matthew M Mallory and our advisor Morgane Austern! Keywords: Gaussian universality, dependent data, convex Gaussian min-max theorem, data augmentation!

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation

Over the last decade, a wave of research has characterized the exact asymptotic risk of many high-dimensional models in the proportional regime. Two foundational results have driven this progress: Gau...

arxiv.org

Kevin Han Huang@kevhhuang.bsky.social · last yr.

Sadly missing ICML due to visa :'(, but looking forward to share our ICML paper (arxiv.org/abs/2502.05318) as a poster at #BayesComp, Singapore! Work on symmetrising neural nets for schrodinger equation in crystals, with the amazing Zhan Ni, Elif Ertekin, Peter Orbanz and Ryan P. Adams

Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation

Incorporating group symmetries into neural networks has been a cornerstone of success in many AI-for-science applications. Diagonal groups of isometries, which describe the invariance under a simultan...

arxiv.org