Unconstrained Robust Online Convex Optimization

TL;DR

This paper develops algorithms for online convex optimization that are robust to corrupted gradient feedback in an unconstrained setting, achieving low regret despite arbitrary corruptions.

Contribution

It introduces new algorithms with regret guarantees in unconstrained online learning under arbitrary gradient corruptions, a setting previously lacking robust solutions.

Findings

Regret bound of $ orm{u}G ( oot T + k)$ when $G$ is known.

Additional penalty of $( orm{u}^2 + G^2)k$ when $G$ is unknown.

Algorithms are effective against outliers, mislabeled data, and malicious interference.

Abstract

This paper addresses online learning with ``corrupted'' feedback. Our learner is provided with potentially corrupted gradients $\tilde g_t$ instead of the ``true'' gradients $g_t$. We make no assumptions about how the corruptions arise: they could be the result of outliers, mislabeled data, or even malicious interference. We focus on the difficult ``unconstrained'' setting in which our algorithm must maintain low regret with respect to any comparison point $u \in \mathbb{R}^d$. The unconstrained setting is significantly more challenging as existing algorithms suffer extremely high regret even with very tiny amounts of corruption (which is not true in the case of a bounded domain). Our algorithms guarantee regret $ \|u\|G (\sqrt{T} + k) $ when $G \ge \max_t \|g_t\|$ is known, where $k$ is a measure of the total amount of corruption. When $G$ is unknown we incur an extra additive penalty of $(\|u\|^2+G^2) k$.