Online Bayesian Persuasion
Authors: Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Nicola Gatti
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | First, we prove a hardness result on the per-round running time required to achieve no-α-regret for any α < 1. Then, we provide algorithms for the full and partial feedback models with regret bounds sublinear in the number of rounds and polynomial in the size of the instance. |
| Researcher Affiliation | Collaboration | Matteo Castiglioni Politecnico di Milano matteo.castiglioni@polimi.it Andrea Celli Facebook Core Data Science andreacelli@fb.com Alberto Marchesi Politecnico di Milano alberto.marchesi@polimi.it Nicola Gatti Politecnico di Milano nicola.gatti@polimi.it |
| Pseudocode | Yes | Algorithm 1 ONLINE BAYESIAN PERSUASION WITH PARTIAL INFORMATION FEEDBACK |
| Open Source Code | No | The paper does not contain any statement about making its source code available or provide any links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve experiments with datasets, thus no information about public datasets is provided. |
| Dataset Splits | No | The paper focuses on theoretical algorithms and their regret bounds, not empirical evaluation. Therefore, it does not discuss training/validation/test dataset splits. |
| Hardware Specification | No | The paper presents theoretical work and does not report on experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |