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New method learns optimal auction reserve prices from censored bids

Researchers have developed a method to learn optimal reserve prices for second-price auctions using censored bid data. The approach leverages a monotone hazard rate (MHR) assumption to identify the optimal reserve price, even when the full value distribution is unknown. The study provides theoretical guarantees on learning regret and demonstrates practical gains through experiments on synthetic and eBay data, highlighting the benefits of the robust rule when parametric models are misspecified. AI

IMPACT This research could lead to more efficient auction mechanisms by improving reserve price setting with incomplete data.

RANK_REASON The cluster describes a new academic paper published on arXiv detailing a novel method for learning reserve prices in auctions. [lever_c_demoted from research: ic=2 ai=0.4]

Read on Hugging Face Daily Papers →

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New method learns optimal auction reserve prices from censored bids

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The cluster describes a new academic paper published on arXiv detailing a novel method for learning reserve prices in auctions. [lever_c_demoted from research: ic=2 ai=0.4]
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Lavanya Marla, Yunzong Xu, Boxuan Zhou ·

    Offline Learning of Reserve Prices from Censored Bids

    arXiv:2611.06067v1 Announce Type: cross Abstract: We study how a seller can learn a reserve price for a second-price auction from censored historical bid data. Because values below past reserves are unobserved, even unlimited data may not reveal which reserve earns the most reven…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Offline Learning of Reserve Prices from Censored Bids

    We study how a seller can learn a reserve price for a second-price auction from censored historical bid data. Because values below past reserves are unobserved, even unlimited data may not reveal which reserve earns the most revenue. Under a monotone hazard rate (MHR) assumption,…