Publications · Conference paper · NeurIPS 2024
High-probability complexity bounds for stochastic non-convex minimax optimization
Advances in Neural Information Processing Systems (NeurIPS), 2024.
In brief
For stochastic non-convex minimax problems — the structure behind adversarial training and distributionally robust learning — the paper establishes complexity bounds that hold with high probability rather than only in expectation. The guarantees are for computing approximate stationary points with a stochastic accelerated primal–dual method, so that a single run, not just the average run, comes with a performance certificate.
Cite
@inproceedings{laguel2024minimax,
title = {High-probability complexity bounds for stochastic non-convex minimax optimization},
author = {Yassine Laguel and Yasa Syed and Necdet Serhat Aybat and Mert Gürbüzbalaban},
year = {2024},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
url = {http://papers.nips.cc/paper_files/paper/2024/hash/fec946957ce1af51a61e8f2d851ac98f-Abstract-Conference.html},
}Relatedsame topics