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..
Bootstrapping Upper Confidence Bound
Authors: Botao Hao, Yasin Abbasi Yadkori, Zheng Wen, Guang Cheng
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results demonstrate significant regret reductions by our method, in comparison with several baselines in a range of multi-armed and linear bandit problems. |
| Researcher Affiliation | Collaboration | Botao Hao Purdue University EMAIL Yasin Abbasi-Yadkori Vin AI EMAIL Zheng Wen Deepmind EMAIL Guang Cheng Purdue University EMAIL |
| Pseudocode | Yes | Algorithm 1 Bootstrapped UCB |
| Open Source Code | No | The paper does not provide any links to or explicit statements about the release of its source code. |
| Open Datasets | No | The paper describes generating data from distributions (e.g., truncated-normal, standard Gaussian, Bernoulli, Beta) rather than using specific, publicly available datasets with citations or links. |
| Dataset Splits | No | The paper does not provide specific training/test/validation dataset splits, sample counts, or cross-validation methods. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions the |
| Experiment Setup | Yes | To be fair, we choose the confidence level α = 1/(1 + t) for both UCB1 and bootstrapped UCB, and δ = 0.1 in (2.5). All algorithms above require knowledge of an upper bound on the noise standard deviation. The number of bootstrap repetitions is B = 200, and the number of arms is K = 5. |