Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
No-Regret Learning in Bayesian Games
Authors: Jason Hartline, Vasilis Syrgkanis, Eva Tardos
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work provides two main technical results that lift this conclusion to games of incomplete information, a.k.a., Bayesian games. First, near-optimal welfare in Bayesian games follows directly from the smoothness-based proof of near-optimal welfare in the same game when the private information is public. Second, no-regret learning dynamics converge to Bayesian coarse correlated equilibrium in these incomplete information games. |
| Researcher Affiliation | Collaboration | Jason Hartline Northwestern University Evanston, IL EMAIL Vasilis Syrgkanis Microsoft Research New York, NY EMAIL Eva Tardos Cornell University Ithaca, NY EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information for source code. |
| Open Datasets | No | This paper is theoretical and does not involve empirical training on a dataset. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical validation on a dataset. |
| Hardware Specification | No | This is a theoretical paper and does not mention any hardware specifications for running experiments. |
| Software Dependencies | No | This is a theoretical paper and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with hyperparameters or training settings. |