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Aaron Sidford, Efficiently Minimizing the Maximum Loss
Aaron Sidford

Sat Dec 03 01:15 PM -- 01:45 PM (PST) @
in OPT 2022: Optimization for Machine Learning »

Title: Efficiently Minimizing the Maximum Loss

Abstract: In this talk I will discuss recent advances in the fundamental robust optimization problem of minimizing the maximum of a finite number of convex loss functions. In particular I will show how to develop stochastic methods for approximately solving this problem with a near-optimal number of gradient queries. Along the way, I will cover several optimization techniques of broader utility, including accelerated methods for using ball-optimization oracles and stochastic bias-reduced gradient methods.

This talk will include joint work with Hilal Asi, Yair Carmon, Arun Jambulapati, and Yujia Jin including https://arxiv.org/abs/2105.01778 and https://arxiv.org/abs/2106.09481.

Author Information

Aaron Sidford (Stanford)

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