Failure-Aware Gaussian Process Optimization with Regret Bounds
Authors: Shogo Iwazaki, Shion Takeno, Tomohiko Tanabe, Mitsuru Irie
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of F-GP-UCB in several benchmark functions, including the simulation function motivated by material synthesis experiments. |
| Researcher Affiliation | Collaboration | Shogo Iwazaki MI-6 Ltd., Tokyo, Japan iwazaki@mi-6.co.jp Shion Takeno RIKEN AIP, Tokyo, Japan shion.takeno@riken.jp Tomohiko Tanabe MI-6 Ltd., Tokyo, Japan tanabe@mi-6.co.jp Mitsuru Irie MI-6 Ltd., Tokyo, Japan irie@mi-6.co.jp |
| Pseudocode | Yes | Algorithm 1 The F-GP-UCB algorithm ... Algorithm 4 in Appendix E.3 shows the pseudo-code of our modified strategy. |
| Open Source Code | No | The paper does not provide an explicit link to open-source code for the methodology presented in this paper. It mentions GPy (a third-party library) in its references, but no code for their specific F-GP-UCB implementation. |
| Open Datasets | Yes | Synthetic experiments with the Branin function. ... We perform a numerical experiment involving quasicrystals in the Al-Cu-Mn system. ... The feasible region is the composition values that form quasicrystals based on data [16]. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits or cross-validation details for the experiments. It describes how the GP model is updated with successful observations during the optimization process. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments (e.g., GPU models, CPU models, memory). |
| Software Dependencies | No | The paper mentions software like GPy and NLopt, but does not provide specific version numbers for these or other libraries, which are necessary for reproducible software dependencies. |
| Experiment Setup | Yes | We set βt = 2 ln(2t) as in [24] for GP-UCB and F-GP-UCB. The other parameters in F-GP-UCB are fixed as described in Sec. 5. For example, we set w = 0.75, hσ = 0.02, q = 3, θmin = 0.0001, and θmax = 0.5. ... We also fix the noise variance hyperparameter of the GP model for f to 0.0001. |