Skip to main content

Search

Show all results for

Home

Person

Ya-Ping Hsieh

This person is no longer with EPFL

About
Privacy
Disclaimer

Copyright © 2026 EPFL, all rights reserved

Graph Chatbot

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (16)

Please note that this is not a complete list of this person’s publications. It includes only semantically relevant works. For a full list, please refer to Infoscience.

No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation

Volkan Cevher, Ya-Ping Hsieh, Kimon Antonakopoulos

We examine the problem of regret minimization when the learner is involved in a continuous game with other optimizing agents: in this case, if all players follow a no-regret algorithm, it is possible to achieve significantly lower regret relative to fully ...

2022

Conditional gradient methods for stochastically constrained convex minimization

Volkan Cevher, Maria-Luiza Vladarean, Ya-Ping Hsieh, Ahmet Alacaoglu

We propose two novel conditional gradient-based methods for solving structured stochastic convex optimization problems with a large number of linear constraints. Instances of this template naturally arise from SDP-relaxations of combinatorial problems, whi ...

2020

Convergence without Convexity: Sampling, Optimization, and Games

Many important problems in contemporary machine learning involve solving highly non- convex problems in sampling, optimization, or games. The absence of convexity poses significant challenges to convergence analysis of most training algorithms, and in some ...

EPFL2020