High Dimensional Bayesian Optimization with Elastic Gaussian Process
Authors: Santu Rana, Cheng Li, Sunil Gupta, Vu Nguyen, Svetha Venkatesh
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | experiments clearly demonstrate the utility of the proposed method on both benchmark test functions and real-world case studies. We evaluate our method on three different benchmark test functions and two real-world applications including training cascaded classifiers and for alloy composition optimization. |
| Researcher Affiliation | Academia | 1Centre for Pattern Recognition and Data Analytics (PRa DA), Deakin University, Australia. |
| Pseudocode | Yes | Algorithm 1 High Dimensional Bayesian Optimization with Elastic Gaussian Process. Algorithm 2 Optimizing acquistion function using EGP. |
| Open Source Code | No | The code is available on request. |
| Open Datasets | Yes | Training cascade classifier on three real datasets from UCI repository (Blake and Merz, 1998): Ionosphere, German and IJCNN1. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits (e.g., percentages, counts, or specific cross-validation schemes) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | The computer used is a Xeon Quad-core PC running at 2.6 GHz, with 16 GB of RAM. |
| Software Dependencies | No | The paper mentions software like NLopt and Matlab but does not provide specific version numbers for these or other key libraries/dependencies. |
| Experiment Setup | Yes | We use the target length-scale lτ = 0.1, lmax = d and lmin = 10 5. The number of initial observations are set at d + 1. The optimization time for all these high-dimensional optimization problem is set as Topt = 0.1 d sec. |