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Bilateral Trade Under Heavy-Tailed Valuations: Minimax Regret with Infinite Variance

Machine Learning 2026-03-10 v1 Computer Science and Game Theory Machine Learning

Abstract

We study contextual bilateral trade under full feedback when trader valuations have bounded density but infinite variance. We first extend the self-bounding property of Bachoc et al. (ICML 2025) from bounded to real-valued valuations, showing that the expected regret of any price π\pi satisfies E[g(m,V,W)g(π,V,W)]Lmπ2\mathbb{E}[g(m,V,W) - g(\pi,V,W)] \le L|m-\pi|^2 under bounded density alone. Combining this with truncated-mean estimation, we prove that an epoch-based algorithm achieves regret O~(T12β(p1)/(βp+d(p1)))\widetilde{O}(T^{1-2\beta(p-1)/(\beta p + d(p-1))}) when the noise has finite pp-th moment for p(1,2)p \in (1,2) and the market value function is β\beta-H\"older, and we establish a matching Ω()\Omega(\cdot) lower bound via Assouad's method with a smoothed moment-matching construction. Our results characterize the exact minimax rate for this problem, interpolating between the classical nonparametric rate at p=2p=2 and the trivial linear rate as p1+p \to 1^+.

Keywords

Cite

@article{arxiv.2603.06851,
  title  = {Bilateral Trade Under Heavy-Tailed Valuations: Minimax Regret with Infinite Variance},
  author = {Hangyi Zhao},
  journal= {arXiv preprint arXiv:2603.06851},
  year   = {2026}
}

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9 pages