Process-constrained batch Bayesian optimisation
Authors: Pratibha Vellanki, Santu Rana, Sunil Gupta, David Rubin, Alessandra Sutti, Thomas Dorin, Murray Height, Paul Sanders, Svetha Venkatesh
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance of both pc-BO(basic) and pc-BO(nested) by optimising benchmark test functions, tuning hyper-parameters of the SVM classifier, optimising the heat-treatment process for an Al-Sc alloy, and optimising the short polymer fibre production process. We conducted a set of experiments using both synthetic data and real data to demonstrate the performance of pc-BO(basic) and pc-BO(nested). |
| Researcher Affiliation | Academia | 1Centre for Pattern Recognition and Data Analytics Deakin University, Geelong, Australia [pratibha.vellanki, santu.rana, sunil.gupta, svetha.venkatesh@deakin.edu.au] 2Institute for Frontier Materials, GTP Research Deakin University, Geelong, Australia [d.rubindecelisleal, alessandra.sutti, thomas.dorin, murray.height@deakin.edu.au] 3Materials Science and Engineering, Michigan Technological University, USA [sanders@mtu.edu] |
| Pseudocode | Yes | Algorithm 1 pc-BO(basic): Basic process-constrained pure exploration batch Bayesian optimisation algorithm. Algorithm 2 pc-BO(nested): Nested process-constrained batch Bayesian optimisation algorithm. |
| Open Source Code | No | The paper states "The code is implemented in MATLAB" but does not provide any statement about making the code open source or available, nor does it provide a link to a repository. |
| Open Datasets | Yes | We use our algorithms to tune both the hyper-parameters C and γ, at each batch only varying γ, but not C. This is demonstrated on the classification using SVM problem using two datasets downloaded from UCI machine learning repository: Breast cancer dataset (BCW) and Bio-degradation dataset (QSAR). |
| Dataset Splits | No | The paper mentions using datasets but does not provide specific details on training, validation, or test splits (e.g., percentages, sample counts, or explicit cross-validation schemes) for reproducibility. |
| Hardware Specification | Yes | The code is implemented in MATLAB and all the experiments are run on an Intel CPU E5-2640 v3 @2.60GHz machine. |
| Software Dependencies | No | The paper mentions "The code is implemented in MATLAB" but does not provide a specific version number for MATLAB or any other software dependencies. |
| Experiment Setup | No | The paper describes the general setup of experiments (e.g., optimizing SVM hyperparameters, heat treatment process) but does not provide specific details such as learning rates, batch sizes, or other hyperparameter values. |