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..
Online Bayesian Persuasion
Authors: Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Nicola Gatti
NeurIPS 2020 | Venue PDF | 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 EMAIL Andrea Celli Facebook Core Data Science EMAIL Alberto Marchesi Politecnico di Milano EMAIL Nicola Gatti Politecnico di Milano EMAIL |
| 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. |