Learning Theory and Algorithms for Revenue Optimization in Second-Price\n Auctions with Reserve

2013/10/21 by Mehryar Mohri, Mohri, Mehryar, Andrés Muñoz Medina +1 · 12 citations
Business, Management and Accounting · Decision Sciences · #Advanced Bandit Algorithms Research #Auction Theory and Applications #Consumer Market Behavior and Pricing #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.1310.5665

openalex publication_date 2013/10/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Second-price auctions with reserve play a critical role for modern search\nengine and popular online sites since the revenue of these companies often\ndirectly de- pends on the outcome of such auctions. The choice of the reserve\nprice is the main mechanism through which the auction revenue can be influenced\nin these electronic markets. We cast the problem of selecting the reserve price\nto optimize revenue as a learning problem and present a full theoretical\nanalysis dealing with the complex properties of the corresponding loss\nfunction. We further give novel algorithms for solving this problem and report\nthe results of several experiments in both synthetic and real data\ndemonstrating their effectiveness.\n

Citations

• A Theory of Auctions and Competitive Bidding

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• Provable Watermarking for Data Poisoning Attacks
• Generalization Analysis for Game-Theoretic Machine Learning
• On consistency of optimal pricing algorithms in repeated posted-price auctions with strategic buyer
• Learning to Bid Without Knowing your Value
• Algorithmic Price Discrimination
• Learning to Bid Optimally and Efficiently in Adversarial First-price Auctions
• Max-Affine Regression: Provable, Tractable, and Near-Optimal Statistical\n Estimation
• Learning Theory and Algorithms for Revenue Management in Sponsored Search
• LP-based Approximation for Personalized Reserve Prices

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• Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve
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