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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
An Analysis of Ensemble Sampling
Authors: Chao Qin, Zheng Wen, Xiuyuan Lu, Benjamin Van Roy
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 16,781 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,207 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we establish a regret bound that ensures desirable behavior when ensemble sampling is applied to the linear bandit problem. This represents the ο¬rst rigorous regret analysis of ensemble sampling and is made possible by leveraging information-theoretic concepts and novel analytic techniques that may prove useful beyond the scope of this paper. We offer in this paper the ο¬rst rigorous regret analysis of ES. Like Lu and Van Roy [2017], we study ES applied to linear-Gaussian bandits. This serves as a simple sanity check for the approach. We establish a Bayesian regret bound (Theorem 1) that consists of two terms. |
| Researcher Affiliation | Collaboration | Chao Qin Columbia University EMAIL Zheng Wen Xiuyuan Lu Benjamin Van Roy Deep Mind EMAIL |
| Pseudocode | Yes | Algorithm 1 Ensemble Sampling |
| Open Source Code | No | This paper does not include any experimental results. |
| Open Datasets | No | This paper does not include any experimental results. |
| Dataset Splits | No | This paper does not include any experimental results. |
| Hardware Specification | No | This paper does not include any experimental results. |
| Software Dependencies | No | This paper does not include any experimental results. |
| Experiment Setup | No | This paper does not include any experimental results. |