No-Regret Learning in Bayesian Games
Authors: Jason Hartline, Vasilis Syrgkanis, Eva Tardos
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | 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 hartline@northwestern.edu Vasilis Syrgkanis Microsoft Research New York, NY vasy@microsoft.com Eva Tardos Cornell University Ithaca, NY eva@cs.cornell.edu |
| 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. |