Optimizing Bidding Strategies in First-Price Auctions in Binary Feedback Setting with Predictions
This addresses the problem of efficient bidding in online auctions for auction platforms and advertisers, representing an incremental improvement within an existing framework.
This paper tackles the problem of optimizing bidding strategies in first-price auctions with binary feedback by proposing a new algorithm that leverages predictions of the highest competing bid. The result is an algorithm achieving zero regret under accurate predictions and a bounded regret of O(T^(3/4) * Vt^(1/4)) under normality conditions.
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.