Learning-Augmented Online Bidding in Stochastic Settings

TL;DR

This paper explores online bidding in stochastic environments, developing algorithms that balance prediction accuracy and robustness, with theoretical bounds and optimal tradeoffs for learning-augmented settings.

Contribution

It introduces Pareto-optimal algorithms for distributional predictions and analyzes randomized bidding algorithms, advancing understanding of tradeoffs in stochastic online bidding.

Findings

Pareto-optimal algorithms for distributional predictions

Upper and lower bounds on consistency and robustness

Enhanced understanding of stochastic online bidding limitations

Abstract

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.