Effective Dimension in Bandit Problems under Censorship
Authors: Gauthier Guinet, Saurabh Amin, Patrick Jaillet
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contributions include the introduction of a broad class of censorship models and their analysis in terms of the effective dimension of the problem a natural measure of its underlying statistical complexity and main driver of the regret bound. Our analysis involves a continuous generalization of the Elliptical Potential Inequality, which we believe is of independent interest. |
| Researcher Affiliation | Collaboration | Gauthier Guinet AWS AI Labs guinetgg@amazon.com Saurabh Amin MIT amins@mit.edu Patrick Jaillet MIT jaillet@mit.edu |
| Pseudocode | Yes | Algorithm 1: Generic UCB |
| Open Source Code | No | The paper does not provide any statement about releasing open-source code for the described methodology, nor does it provide any links to code repositories. |
| Open Datasets | No | The paper is theoretical and defines conceptual bandit models (MAB, LCB) but does not refer to or use any specific public or open datasets for empirical training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments, thus no training/validation/test dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| 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 system-level training settings. Algorithm 1 is a generic description, not a specific experimental configuration. |