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- W4367190639 abstract "Budget management strategies in repeated auctions have received growing attention in online advertising markets. However, previous work on budget management in online bidding mainly focused on second-price auctions. The rapid shift from second-price auctions to first-price auctions for online ads in recent years has motivated the challenging question of how to bid in repeated first-price auctions while controlling budgets. In this work, we study the problem of learning in repeated first-price auctions with budgets. We design a dual-based algorithm that can achieve a near-optimal $widetilde{O}(sqrt{T})$ regret with full information feedback where the maximum competing bid is always revealed after each auction. We further consider the setting with one-sided information feedback where only the winning bid is revealed after each auction. We show that our modified algorithm can still achieve an $widetilde{O}(sqrt{T})$ regret with mild assumptions on the bidder's value distribution. Finally, we complement the theoretical results with numerical experiments to confirm the effectiveness of our budget management policy." @default.
- W4367190639 created "2023-04-28" @default.
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- W4367190639 date "2023-04-26" @default.
- W4367190639 modified "2023-10-18" @default.
- W4367190639 title "Learning to Bid in Repeated First-Price Auctions with Budgets" @default.
- W4367190639 doi "https://doi.org/10.48550/arxiv.2304.13477" @default.
- W4367190639 hasPublicationYear "2023" @default.
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