Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach
Authors: Remi Lam, Karen Willcox, David H. Wolpert
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present numerical experiments showing that the resulting algorithm for optimization with a finite budget outperforms several popular Bayesian optimization algorithms. In Sec. 6, we numerically investigate the proposed algorithm and present our conclusions in Sec. 7. |
| Researcher Affiliation | Academia | Remi R. Lam Massachusetts Institute of Technology Cambridge, MA rlam@mit.edu Karen E. Willcox Massachusetts Institute of Technology Cambridge, MA kwillcox@mit.edu David H. Wolpert Santa Fe Institute Santa Fe, NM dhw@santafe.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper uses GP-generated objective functions and references a website for 'test functions' but does not provide concrete access information (link, DOI, repository, or formal citation with authors/year) for a specific dataset. |
| Dataset Splits | No | The paper describes how initial training points are generated and how the training set is augmented during the BO process, but it does not specify explicit training/validation/test dataset splits (e.g., percentages, sample counts) from a fixed dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We use a zero-mean GP with square-exponential kernel (hyper-parameters: maximum variance σ2 = 4, length scale L = 0.1, noise variance λ = 10 3) to generate 24 objective functions defined on X = [0, 1]2. All algorithms are given a budget of N = 15 evaluations. All algorithms use the same kernel and hyper-parameters as those used to generate the objective functions. parameters of the rolling horizon h {2, 3, 4, 5} and discount factor γ {0.5, 0.7, 0.9, 1.0}. |