Bandits for BMO Functions
Authors: Tianyu Wang, Cynthia Rudin
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 <tianyu@cs.duke.edu>. |
| 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. |