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Award ID contains: 2052926

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  1. ; (, Journal of the American Statistical Association)
    Free, publicly-accessible full text available April 3, 2026
  2. ; ; ; (, Journal of the American Statistical Association)
    Free, publicly-accessible full text available January 2, 2026
  3. ; ; ; (, Operations Research)
    This paper makes progress toward learning Nash equilibria in two-player, zero-sum Markov games from offline data. Despite a large number of prior works tackling this problem, the state-of-the-art results suffer from the curse of multiple agents in the sense that their sample complexity bounds scale linearly with the total number of joint actions. The current paper proposes a new model-based algorithm, which provably finds an approximate Nash equilibrium with a sample complexity that scales linearly with the total number of individual actions. This work also develops a matching minimax lower bound, demonstrating the minimax optimality of the proposed algorithm for a broad regime of interest. An appealing feature of the result lies in algorithmic simplicity, which reveals the unnecessity of sophisticated variance reduction and sample splitting in achieving sample optimality. 
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    Free, publicly-accessible full text available November 1, 2025
  4. ; ; ; (, The Annals of Statistics)
    Full Text Available 
  5. ; (, Journal of the American Statistical Association)
    Full Text Available 
  6. ; ; (, Journal of machine learning research)
    Accepted Manuscript Full Text Available
  7. ; ; (, The Annals of Statistics)
    Full Text Available 
  8. ; ; ; (, IEEE Transactions on Information Theory)
  9. ; ; (, The Annals of Statistics)
    Full Text Available 
  10. ; ; (, Journal of the American Statistical Association)
    Full Text Available