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..
Dynamic Pricing and Learning with Bayesian Persuasion
Authors: Shipra Agrawal, Yiding Feng, Wei Tang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is a computationally ef๏ฌcient online algorithm that achieves an O(T 2/3(m log T) 1/3) regret bound when the valuation function is linear in the product quality. |
| Researcher Affiliation | Academia | Shipra Agrawal Columbia University EMAIL Yiding Feng University of Chicago EMAIL Wei Tang Columbia University EMAIL |
| Pseudocode | Yes | Algorithm 1: Algorithm for Dynamic Pricing and Advertising with Demand Learning. |
| Open Source Code | No | The paper provides a link to an arXiv preprint ('https://arxiv.org/abs/2304.14385') for a full version of the work, but it does not contain any explicit statements about releasing source code for the methodology or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical experiments using datasets, so there is no mention of dataset availability or access information. |
| Dataset Splits | No | As a theoretical paper, it does not involve empirical experiments with data, and thus does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is purely theoretical, describing an algorithm and its regret bounds without conducting empirical experiments, and therefore does not specify any hardware used. |
| Software Dependencies | No | The paper focuses on theoretical algorithm design and analysis, and thus does not specify any ancillary software or library versions used for empirical experiments. |
| Experiment Setup | No | As a theoretical paper, it does not describe an experimental setup with specific details such as hyperparameters or system-level training settings. |