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arXiv · 2408.06297

LEARN: An Invex Loss for Outlier Oblivious Robust Online Optimization

Adarsh Barik·Anand Krishna·Vincent Y. F. Tan
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Abstract

We study a robust online convex optimization framework, where an adversary can introduce outliers by corrupting loss functions in an arbitrary number of rounds k, unknown to the learner. Our focus is on a novel setting allowing unbounded domains and large gradients for the losses without relying on a Lipschitz assumption. We introduce the Log Exponential Adjusted Robust and iNvex (LEARN) loss, a non-convex (invex) robust loss function to mitigate the effects of outliers and develop a robust variant of the online gradient descent algorithm by leveraging the LEARN loss. We establish tight regret guarantees (up to constants), in a dynamic setting, with respect to the uncorrupted rounds and conduct experiments to validate our theory. Furthermore, we present a unified analysis framework for developing online optimization algorithms for non-convex (invex) losses, utilizing it to provide regret bounds with respect to the LEARN loss, which may be of independent interest.

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Adarsh Barik, Anand Krishna, Vincent Y. F. Tan. 2024-08-12. LEARN: An Invex Loss for Outlier Oblivious Robust Online Optimization. https://arxiv.org/abs/2408.06297

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