Optimizing Bidding Strategies in First-Price Auctions in Binary Feedback Setting with Predictions

TL;DR

This paper introduces a new algorithm for first-price auctions in a binary feedback setting that leverages predictions to achieve zero regret with accurate information, improving bidding strategies using machine learning insights.

Contribution

It proposes a novel algorithm within the BROAD-OMD framework that utilizes bid predictions to optimize bidding strategies in first-price auctions, achieving zero regret with perfect predictions.

Findings

Achieves zero regret with accurate bid predictions.

Establishes a bounded regret of O(T^(3/4) * Vt^(1/4)) under normality conditions.

Enhances the performance of auction bidding algorithms using machine learning predictions.

Abstract

This paper studies Vickrey first-price auctions under binary feedback. Leveraging the enhanced performance of machine learning algorithms, the new algorithm uses past information to improve the regret bounds of the BROAD-OMD algorithm. Motivated by the growing relevance of first-price auctions and the predictive capabilities of machine learning models, this paper proposes a new algorithm within the BROAD-OMD framework (Hu et al., 2025) that leverages predictions of the highest competing bid. This paper's main contribution is an algorithm that achieves zero regret under accurate predictions. Additionally, a bounded regret bound of O(T^(3/4) * Vt^(1/4)) is established under certain normality conditions.