Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
High Dimensional Bayesian Optimization with Elastic Gaussian Process
Authors: Santu Rana, Cheng Li, Sunil Gupta, Vu Nguyen, Svetha Venkatesh
ICML 2017 | Venue PDF | 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. |