Multi-Fidelity Bayesian Optimization via Deep Neural Networks
Authors: Shibo Li, Wei Xing, Robert Kirby, Shandian Zhe
NeurIPS 2020 | Conference 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 shibo@cs.utah.edu Wei Xing Scientific Computing and Imaging Institute University of Utah Salt Lake City, UT 84112 wxing@sci.utah.edu Robert M. Kirby School of Computing University of Utah Salt Lake City, UT 84112 kirby@cs.utah.edu Shandian Zhe School of Computing University of Utah Salt Lake City, UT 84112 zhe@cs.utah.edu |
| 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. |