Bayesian Optimization of Composite Functions
Authors: Raul Astudillo, Peter Frazier
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments show that our approach dramatically outperforms standard Bayesian optimization benchmarks, reducing simple regret by several orders of magnitude. |
| Researcher Affiliation | Collaboration | 1School of Operations Research and Information Engineering, Cornell University, Ithaca, NY, USA 2Uber, San Francisco, CA, USA. |
| Pseudocode | Yes | Algorithm 1 Computation of EI-CF |
| Open Source Code | Yes | Our code is available at Astudillo (2019). URL https://github. com/Raul Astudillo06/BOCF. |
| Open Datasets | No | The paper mentions 'GP-generated test problems' and 'standard benchmark functions' (Langermann, Rosenbrock, environmental model function). While these are known functions, the paper does not provide access information (link, citation for a specific dataset instance with author/year, or repository) for pre-existing datasets used for training. |
| Dataset Splits | No | The paper states, 'For all problems and methods, an initial stage of evaluations is performed using 2(d + 1) points chosen uniformly at random over X.' However, it does not specify explicit train/validation/test dataset splits, but rather refers to sequential function evaluations. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'independent GP prior distributions' and 'ARD squared exponential covariance function' and 'averaged version of the acquisition function', but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For all problems and methods, an initial stage of evaluations is performed using 2(d + 1) points chosen uniformly at random over X. As proposed in Snoek et al. (2012), for all methods we use an averaged version of the acquisition function, obtained by first drawing 10 samples of the GP hyperparameters, computing the acquisition function conditioned on each of these hyperparameters, and then averaging the results. |