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 [1].
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. |