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].
Max-value Entropy Search for Efficient Bayesian Optimization
Authors: Zi Wang, Stefanie Jegelka
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we probe the empirical performance of MES and add-MES on a variety of tasks. |
| Researcher Affiliation | Academia | 1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Massachusetts, USA. Correspondence to: Zi Wang <EMAIL>, Stefanie Jegelka <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Max-value Entropy Search (MES) |
| Open Source Code | Yes | Our Matlab code and test functions are available at https://github.com/ zi-w/Max-value-Entropy-Search/. |
| Open Datasets | Yes | We test regression on the Boston housing dataset and classification on the breast cancer dataset (Bache & Lichman, 2013). |
| Dataset Splits | Yes | Both of the datasets were randomly split into train/validation/test sets. |
| Hardware Specification | Yes | All the timing experiments were run exclusively on an Intel(R) Xeon(R) CPU E5-2680 v4 @ 2.40GHz. |
| Software Dependencies | No | The paper mentions "Our Matlab code" but does not specify the Matlab version or any other software dependencies with specific version numbers. |
| Experiment Setup | Yes | The parameter for GP-UCB was set according to Theorem 2 in (Srinivas et al., 2010); the parameter for PI was set to be the observation noise σ. For the functions with unknown GP hyper-parameters, every 10 iterations, we learn the GP hyper-parameters using the same approach as was used by PES (Hern andez-Lobato et al., 2014). |