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].
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. |