Stochastic Linear Optimization with Adversarial Corruption
Machine Learning
2019-09-06 v1 Machine Learning
Abstract
We extend the model of stochastic bandits with adversarial corruption (Lykouriset al., 2018) to the stochastic linear optimization problem (Dani et al., 2008). Our algorithm is agnostic to the amount of corruption chosen by the adaptive adversary. The regret of the algorithm only increases linearly in the amount of corruption. Our algorithm involves using L\"owner-John's ellipsoid for exploration and dividing time horizon into epochs with exponentially increasing size to limit the influence of corruption.
Keywords
Cite
@article{arxiv.1909.02109,
title = {Stochastic Linear Optimization with Adversarial Corruption},
author = {Yingkai Li and Edmund Y. Lou and Liren Shan},
journal= {arXiv preprint arXiv:1909.02109},
year = {2019}
}