Reposted by Yuzhou Cao
The next #NeurIPS2025 poster this evening on the interaction between loss functions and gradient descent dynamics: See you soon at Poster No.3007😎
🧗♂️Why GD converges beyond [step size]<2/[smoothness]? We investigate loss functions and identify their *separation margin* is an important factor. Surprisingly Renyi 2-entropy yields super fast rate T=Ω(ε^{-1/3})!
arxiv.org/abs/2502.04889
arxiv.org/abs/2502.04889
December 4, 2025 at 5:19 PM
The next #NeurIPS2025 poster this evening on the interaction between loss functions and gradient descent dynamics: See you soon at Poster No.3007😎
Although I’ll unfortunately miss #NeurIPS2025, our spotlight work on Conv-FY losses will still be there. My excellent collaborator @han-b.bsky.social will be presenting at Exhibit Hall C,D,E, 3001. If you’re on site, feel free to stop by and chat with him!
Convolutional Fenchel-Young loss paper has been updated slightly, with more examples e.g. classification with rejection (with the great effort of
@caoyuzhou.bsky.social
). We're gonna present the spotlight poster at #NeurIPS2025, at 4:30pm-7:30pm on Dec 3. See you all soon😎
@caoyuzhou.bsky.social
). We're gonna present the spotlight poster at #NeurIPS2025, at 4:30pm-7:30pm on Dec 3. See you all soon😎
Folks, here's our latest work on convolutional Fenchel-Young losses! We show convex smooth losses can have a linear surrogate regret bound on discrete losses (e.g. 01 loss, Prec@k), and it's relies on inf-conv, which is the most beautiful proof I saw ever.
arxiv.org/abs/2505.09432
arxiv.org/abs/2505.09432
December 1, 2025 at 11:24 PM
Although I’ll unfortunately miss #NeurIPS2025, our spotlight work on Conv-FY losses will still be there. My excellent collaborator @han-b.bsky.social will be presenting at Exhibit Hall C,D,E, 3001. If you’re on site, feel free to stop by and chat with him!
Reposted by Yuzhou Cao
Convolutional Fenchel-Young loss paper has been updated slightly, with more examples e.g. classification with rejection (with the great effort of
@caoyuzhou.bsky.social
). We're gonna present the spotlight poster at #NeurIPS2025, at 4:30pm-7:30pm on Dec 3. See you all soon😎
@caoyuzhou.bsky.social
). We're gonna present the spotlight poster at #NeurIPS2025, at 4:30pm-7:30pm on Dec 3. See you all soon😎
Folks, here's our latest work on convolutional Fenchel-Young losses! We show convex smooth losses can have a linear surrogate regret bound on discrete losses (e.g. 01 loss, Prec@k), and it's relies on inf-conv, which is the most beautiful proof I saw ever.
arxiv.org/abs/2505.09432
arxiv.org/abs/2505.09432
November 29, 2025 at 6:11 AM
Convolutional Fenchel-Young loss paper has been updated slightly, with more examples e.g. classification with rejection (with the great effort of
@caoyuzhou.bsky.social
). We're gonna present the spotlight poster at #NeurIPS2025, at 4:30pm-7:30pm on Dec 3. See you all soon😎
@caoyuzhou.bsky.social
). We're gonna present the spotlight poster at #NeurIPS2025, at 4:30pm-7:30pm on Dec 3. See you all soon😎
Reposted by Yuzhou Cao
Chen Ning Yang, Chinese-American physicist and Nobel laureate, dies at 103
Chen Ning Yang, Chinese-American physicist and Nobel laureate, dies at 103
Renowned 1957 Nobel prize winner worked on statistical mechanics and symmetry principles in elementary particle physics
www.theguardian.com
October 18, 2025 at 7:22 AM
Chen Ning Yang, Chinese-American physicist and Nobel laureate, dies at 103
Thrilled to share our paper is accepted to #NeurIPS2025 as a Spotlight! 🎉 Big thanks to my awesome collaborators and the program committee. See you in San Diego!
Folks, here's our latest work on convolutional Fenchel-Young losses! We show convex smooth losses can have a linear surrogate regret bound on discrete losses (e.g. 01 loss, Prec@k), and it's relies on inf-conv, which is the most beautiful proof I saw ever.
arxiv.org/abs/2505.09432
arxiv.org/abs/2505.09432
September 22, 2025 at 5:19 AM
Thrilled to share our paper is accepted to #NeurIPS2025 as a Spotlight! 🎉 Big thanks to my awesome collaborators and the program committee. See you in San Diego!