Multi-Fidelity Bayesian Optimization via Deep Neural Networks
Authors: Shibo Li, Wei Xing, Robert Kirby, Shandian Zhe
NeurIPS 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the advantages of our method in both synthetic benchmark datasets and real-world applications in engineering design. |
| Researcher Affiliation | Academia | Shibo Li School of Computing University of Utah Salt Lake City, UT 84112 EMAIL Wei Xing Scientific Computing and Imaging Institute University of Utah Salt Lake City, UT 84112 EMAIL Robert M. Kirby School of Computing University of Utah Salt Lake City, UT 84112 EMAIL Shandian Zhe School of Computing University of Utah Salt Lake City, UT 84112 EMAIL |
| Pseudocode | Yes | Algorithm 1 DNN-MFBO (D, M, T, {λm}M m=1 ) |
| Open Source Code | No | The paper provides links to the implementations of competing methods (e.g., 'https://github.com/kirthevasank/ mf-gp-ucb') but does not state that the code for DNN-MFBO is open-source or provide a link to its repository. |
| Open Datasets | Yes | We first evaluated DNN-MFBO in three popular synthetic benchmark tasks. (1) Branin function (Forrester et al., 2008; Perdikaris et al., 2017)... (2) Park1 function (Park, 1991)... (3) Levy function (Laguna and Martí, 2005)... |
| Dataset Splits | Yes | To identify the architecture of the neural network in each fidelity and learning rate, we first ran the Auto ML tool SMAC3 (https://github.com/automl/SMAC3) on the initial training dataset (we randomly split the data into half for training and the other half for test, and repeated multiple times to obtain a cross-validation accuracy to guide the search) and then manually tuned these hyper-parameters. |
| Hardware Specification | Yes | For a fair comparison, we ran all the methods on a Linux workstation with a 16-core Intel(R) Xeon(R) CPU E5-2670 and 16GB RAM. |
| Software Dependencies | No | The paper mentions software like TensorFlow, Matlab, Python, and Numpy, but does not provide specific version numbers for these or other libraries. |
| Experiment Setup | Yes | The depth and width of each network were chosen from [2, 12] and [32, 512], and the learning rate [10 5, 10 1]. We used ADAM (Kingma and Ba, 2014) for stochastic training. The number of epochs was set to 5, 000, which is enough for convergence. |