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New Algorithm Achieves Optimal Sample Complexity for Linear Contracts

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A new paper published on arXiv details an algorithm for learning optimal linear contracts from data. The Empirical Utility Maximization (EUM) algorithm can achieve an \(\\varepsilon\)-approximation of the best possible linear contract with high probability, using a sample complexity of \(O(\ln(1/\delta) / \varepsilon^2)\). This sample complexity is proven to be optimal, matching existing lower bounds and establishing uniform convergence guarantees. AI

RANK_REASON The cluster contains an academic paper detailing a new algorithm and its theoretical guarantees. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Algorithm Achieves Optimal Sample Complexity for Linear Contracts

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The cluster contains an academic paper detailing a new algorithm and its theoretical guarantees. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mikael M{\o}ller H{\o}gsgaard ·

    The Optimal Sample Complexity of Linear Contracts

    arXiv:2601.01496v2 Announce Type: replace-cross Abstract: In this paper, we settle the problem of learning optimal linear contracts from data in the offline setting, where agent types are drawn from an unknown distribution and the principal's goal is to design a contract that max…

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