Hugging Face Trending Papers
Offline Learning of Reserve Prices from Censored Bids
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arXiv Machine Learning
1d ago
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...
arXiv Machine Learning
Sep 11
Bilateral Trade Under Heavy-Tailed Valuations: Minimax Regret without a Variance Bound
The paper studies contextual bilateral trade with full feedback, showing that action-independent observations eliminate the usual polynomial adaptation penalty seen in heavy-tailed bandits. It presents fully parameter-free algorithms that achieve oracle minimax regret rates without knowing the moment order or scale, and derives new regret bounds for both parametric and nonparametric settings. The key technical insight is a paired squared‑loss statistic whose noise cancels, enabling model selection and yielding regret rates that interpolate between classical nonparametric and linear extremes.
arXiv Machine Learning
Jul 21
Regret Minimization for Piecewise Linear Rewards: Contracts, Auctions, and Beyond
arXiv:2503. 01701v2 Announce Type: replace-cross Abstract: Most microeconomic models of interest involve optimizing a piecewise linear function.
arXiv Machine Learning
Oct 3
Online Generalized-Mean Welfare Maximization: Achieving Near-Optimal Regret from Samples
The paper investigates online fair allocation of sequential items to agents with heterogeneous preferences, aiming to maximize generalized-mean welfare. In an i.i.d. arrival setting, a pure greedy algorithm achieves near-optimal “~O(1/T)” average regret without needing distributional knowledge. For nonstationary arrivals, the authors show that a single historical sample per distribution suffices to recover the same regret rate, using re-solving algorithms that remain robust to distribution shifts.
arXiv Machine Learning
Oct 22
Learning to Allocate Resources with Censored Feedback
The paper addresses an online resource allocation problem where a budget must be distributed among multiple arms under censored feedback. Each arm yields a reward only if it is activated by an unknown Bernoulli process and the allocated budget exceeds a random threshold from an unknown parametric distribution. The authors establish a worst‑case regret lower bound of Σ(√T), propose the RA‑UCB algorithm achieving near‑optimal regret when the budget is known, and introduce MG‑UCB for unknown, round‑dependent budgets, validating both approaches experimentally.
arXiv Machine Learning
Aug 3
Parameter-Free Heavy-Tailed Bandits
arXiv:2607. 29460v1 Announce Type: new Abstract: Heavy-tailed distributions arise naturally in sequential decision-making problems such as financial investment, online advertising, and network management, where rare but extreme outcomes can dominate performance.