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Our new work: “Expressive Sign Equivariant Networks for Spectral Geometric Learning”, a NeurIPS 2023 Spotlight! You may enjoy it if you like: eigenvectors, link prediction, frames, orthogonal equivariance (AI4Science), Graph Transformers, and/or approximation power of NNs. 1/n

Excited to present this work on Wednesday! Also, if you’re at NeurIPS and want to chat about equivariant learning, AI for science, or anything else, don’t hesitate to reach out 😄

I'll be presenting our work on optimal approximation algorithms for combinatorial problems with graph neural nets at the Mathematics of Modern Machine Learning (M3L) workshop. Saturday 4pm, room 242! Arxiv: arxiv.org/abs/2310.00526

yufeizhao.com/blog/2023/12/2… I’m delighted to showcase and celebrate a selection of recent papers by the talented combinatorialists at MIT, especially those by students and postdocs. These papers reflect the culmination of their hard work, dedication, and innovative problem solving.

Looking forward to mentoring a project at this great summer school! Students interested in GNNs / geometric deep learning should consider applying 😀

Are you canonicalizing your data? Depending on the group, you might be unavoidably introducing discontinuity...but there's a fix! Come to our ICML poster, #511, on Wednesday 1:30 - 3:00 PM to hear more! Joint work with Nadav Dym and Jonathan Siegel arxiv.org/pdf/2402.16077

It's been wild to see our work on Muon and the anthology start to get scaled up by the big labs. After Kimi.ai released Moonlight, people have asked whether Muon is compatible with muP. I wanted to write up an explainer, as there is something deeper going on here! (1/8)