LEARN: An Invex Loss for Outlier Oblivious Robust Online Optimization
<|reference_start|>LEARN: An Invex Loss for Outlier Oblivious Robust Online Optimization: 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.<|reference_end|>
@article{barik2024learn:,
    title={LEARN: An Invex Loss for Outlier Oblivious Robust Online Optimization},
    author={Adarsh Barik, Anand Krishna, Vincent Y. F. Tan},
    journal={arXiv preprint arXiv:2408.06297},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06297},
    primaryClass={cs.LG math.OC}
}