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..
Effective Dimension in Bandit Problems under Censorship
Authors: Gauthier Guinet, Saurabh Amin, Patrick Jaillet
NeurIPS 2022 | Venue PDF | 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 EMAIL Saurabh Amin MIT EMAIL Patrick Jaillet MIT EMAIL |
| 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. |