Learning-Augmented Online Bidding in Stochastic Settings

2025-10-29 · Spyros Angelopoulos, Bertrand Simon arxiv

Online bidding is a classic optimization problem, with several applications in online decision-making, the design of interruptible systems, and the analysis of approximation algorithms. In this work, we study online bidding under learning-augmented settings that incorporate stochasticity, in either the prediction oracle or the algorithm itself. In the first part, we study bidding under distributional predictions, and find Pareto-optimal algorithms that offer the best-possible tradeoff between the consistency and the robustness of the algorithm. In the second part, we study the power and limitations of randomized bidding algorithms, by presenting upper and lower bounds on the consistency/robustness tradeoffs. Previous works focused predominantly on oracles that do not leverage stochastic information on the quality of the prediction, and deterministic algorithms.

📄 PDF Abstract BibTeX arXiv:2510.25582

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DRIVE: Distributional and Retrieval-Augmented Bidding with Value Evaluation

2026-06-12 · Miduo Cui, Haochen Wang, Shangqin Mao, Xun Yang 외 arxiv

Auto-bidding is a core component of real-time advertising systems, where decisions must optimize long-term performance under budget and cost constraints, while online exploration is prohibitively risky. Offline reinforce…

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ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning

2022-06-10 · Haozhe Wang, Chao Du, Panyan Fang, Shuo Yuan 외

Real-Time Bidding (RTB) is an important mechanism in modern online advertising systems. Advertisers employ bidding strategies in RTB to optimize their advertising effects subject to various financial requirements, especi…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Learning-Augmented Online Bidding in Stochastic Settings

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