Online Bidding for Contextual First-Price Auctions with Budgets under One-Sided Information Feedback
This paper proposes a novel bidding algorithm for repeated contextual first-price auctions with budget constraints under one-sided information feedback, utilizing a robust regression method based on conditional quantile invariance to learn unknown competitor bid distributions and achieving order-optimal regret.