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New paper details Bayesian updating and regret in game theory

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A new paper titled "The concentration game: Bayesian updating, regret, and information" by Akshay Balsubramani explores a two-player zero-sum repeated game. This game's value identity is designed to simultaneously generate Bayesian updating and an exact accounting of exponential-weights regret. The research introduces a comparator-class variational form that underlies various concentration phenomena, with the terminal payoff representing the maximum gain a comparator can achieve relative to a prior at a fixed relative entropy. The paper details how Gibbs/Bayes weights emerge as the unique Bellman equalizer for the learner, with log-partition functions serving as value functions. AI

RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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

New paper details Bayesian updating and regret in game theory

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The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Akshay Balsubramani ·

    The concentration game: Bayesian updating, regret, and information

    arXiv:2608.18061v1 Announce Type: new Abstract: We give a two-player zero-sum repeated game between a learner and nature whose value identity generates Bayesian updating and an exact accounting of exponential-weights regret at once, and supplies the comparator-class variational f…

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