skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 2

Search for: All records

Award ID contains: 2053832

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  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. ; ; (, The Annals of Statistics)
    Free, publicly-accessible full text available December 1, 2025
  4. ; ; ; (, 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. 
    more » « less
    Free, publicly-accessible full text available November 1, 2025
  5. ; ; ; (, The Annals of Statistics)
    Full Text Available 
  6. ; (, Journal of the American Statistical Association)
    Full Text Available 
  7. ; ; (, Journal of machine learning research)
    Accepted Manuscript Full Text Available
  8. ; ; (, The Annals of Statistics)
    Full Text Available 
  9. ; ; ; (, IEEE Transactions on Information Theory)
  10. ; ; (, The Annals of Statistics)
    Full Text Available