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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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). |