Publications · Journal article · J. Mach. Learn. Res. 2024
High probability and risk-averse guarantees for a stochastic accelerated primal-dual method
Journal of Machine Learning Research, 25(421), pp. 1–56, 2024.
In brief
Stochastic accelerated primal–dual methods are usually analyzed in expectation; this paper gives high-probability bounds on the distance to the saddle point, together with risk-averse (entropic value-at-risk) guarantees that control the tail of the error distribution. The analysis covers strongly convex–strongly concave problems with light-tailed gradient noise and shows how the step-sizes trade off speed against the size of the tails.
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Three ways to reach a saddle point → — a playground built around the ideas in this paper.
Cite
@article{laguel2024sapd,
title = {High probability and risk-averse guarantees for a stochastic accelerated primal-dual method},
author = {Yassine Laguel and Necdet Serhat Aybat and Mert Gürbüzbalaban},
year = {2024},
journal = {Journal of Machine Learning Research},
volume = {25(421)},
pages = {1--56},
url = {https://jmlr.org/papers/v25/23-0864.html},
}Relatedsame topics