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 Without a Clue
Authors: Francesco Bacchiocchi, Matteo Bollini, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study online Bayesian persuasion problems in which an informed sender repeatedly faces a receiver with the goal of influencing their behavior through the provision of payoff-relevant information. ... We design an algorithm that achieves sublinear in the number of rounds regret with respect to an optimal signaling scheme, and we also provide a collection of lower bounds showing that the guarantees of such an algorithm are tight. |
| Researcher Affiliation | Academia | Francesco Bacchiocchi Politecnico di Milano EMAIL Matteo Bollini Politecnico di Milano EMAIL Matteo Castiglioni Politecnico di Milano EMAIL Alberto Marchesi Politecnico di Milano EMAIL Nicola Gatti Politecnico di Milano EMAIL |
| Pseudocode | Yes | Algorithm 1 Learn-to-Persuade-w/o-Clue |
| Open Source Code | No | The paper does not mention releasing any source code for its methodology. |
| Open Datasets | No | The paper is theoretical and does not use or mention any datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup or hyperparameters. |