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Arxiv
1d
256
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Learning in Budgeted Auctions with Spacing Objectives
- Researchers have introduced a model for budgeted auctions where the spacing of wins over time is crucial, especially in settings like online retail, compute services, and advertising campaigns.
- The model considers how the value of a win diminishes with time, leading to the importance of evenly spaced wins for a given number of total wins.
- The research extends to cases where not all wins result in actual gains, and the conversion probability depends on context.
- The objective is to optimize and evenly distribute conversions over time rather than just wins.
- The study focuses on optimal strategies in second-price auctions and provides learning algorithms for bidders to minimize regret in a Bayesian online setting.
- An online learning algorithm is introduced, achieving approximately square root regret in terms of time complexity.
- The algorithm operates by learning a bidding policy based on the context and system state, such as the time elapsed since the last win or conversion.
- State-independent strategies are found to incur linear regret even without uncertainty in conversions.
- Certain state-independent strategies can achieve a near-optimal reward approximation despite still having linear regret.
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