Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Bandits for BMO Functions
Authors: Tianyu Wang, Cynthia Rudin
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 19,222 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,376 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We deploy Algorithms 1 and 2 on the Himmelblau’s function and the Styblinski-Tang function (arm space normalized to [0, 1)2, function range rescaled to [0, 10]). The results are in Figure 3. We measure performance using traditional regret and δ-regret. Traditional regret can be measured because both functions are continuous, in addition to being BMO. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Duke University, Durham, NC, USA. Correspondence to: Tianyu Wang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Bandit-BMO-Partition (Bandit-BMO-P) ... Algorithm 2 Bandit-BMO-Zooming (Bandit-BMO-Z) |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper uses well-known mathematical functions (Himmelblau’s function and the Styblinski-Tang function) for experiments, which are defined by equations and not typical datasets requiring external access. Therefore, no concrete access information for a dataset is provided. |
| Dataset Splits | No | The paper does not provide specific details about training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For the Bandit-BMO-P algorithm, we use ϵ = 0.01, η = 0.001, total number of trials T = 10000. For Bandit-BMO-Z algorithm, we use α = 1, ϵ = 0.01, η = 0.001, number of episodes T = 2500, with four arm trials in each episode. |