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arXiv · 2605.07349

Optimal Learning-Augmented Algorithm for Online Bidding

Changyeol Lee·Dahoon Lee·Jongseo Lee·Yongho Shin·Changki Yun
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Abstract

Recent advances in machine learning have spurred significant interest in learning-augmented algorithms, particularly for online optimization. A growing body of work has studied online bidding in this framework, aiming to characterize the trade-off between robustness and consistency. While this trade-off is fully understood for deterministic algorithms, a gap between upper and lower bounds remains in the randomized setting. In this paper, we close this gap by presenting a Pareto-optimal randomized learning-augmented algorithm for this problem. Our approach introduces the notion of a bidding profile, a novel framework for representing the distribution over bids generated by an algorithm. We show that any bidding algorithm can be reduced, without loss of generality, to one driven by a bidding profile, and we characterize the optimal profile via a system of delayed differential equations. Finally, we demonstrate the broader applicability of our approach by extending it to the linear search problem, yielding a significant improvement over prior learning-augmented algorithms for linear search.

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Changyeol Lee, Dahoon Lee, Jongseo Lee, Yongho Shin, Changki Yun. 2026-05-08. Optimal Learning-Augmented Algorithm for Online Bidding. https://arxiv.org/abs/2605.07349

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