Optimal Rates and Efficient Algorithms for Online Bayesian Persuasion
Authors: Martino Bernasconi, Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Francesco Trovò, Nicola Gatti
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | First, we show how to obtain a tight O(T 1/2) regret bound in the case in which the sender faces a single receiver and has partial feedback, improving over the best previously-known bound of O(T 4/5). Then, we provide the first no-regret guarantees for the multi-receiver setting under partial feedback. Finally, we show how to design noregret algorithms with polynomial per-iteration running time by exploiting type reporting, thereby circumventing known intractability results on online Bayesian persuasion. We provide efficient algorithms guaranteeing a O(T 1/2) regret upper bound both in the singleand the multi-receiver scenario when type reporting is allowed. |
| Researcher Affiliation | Academia | 1Dipartimento di Elettronica, Informatica e Bioingegneria, Politecnico di Milano, Milan, Italy 2Department of Computing Sciences, Università Bocconi, Milan, Italy. Correspondence to: Martino Bernasconi <martino.bernasconideluca@polimi.it>. |
| Pseudocode | Yes | Algorithm 1 NO-REGRET ALGORITHM; Algorithm 2 NO-REGRET ALGORITHM TYPE-REPORTING |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not mention using any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not mention any dataset splits for validation, training, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail any experimental setup, hyperparameters, or training configurations. |