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..
BORE: Bayesian Optimization by Density-Ratio Estimation
Authors: Louis C Tiao, Aaron Klein, Matthias W Seeger, Edwin V. Bonilla, Cedric Archambeau, Fabio Ramos
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We describe the experiments conducted to empirically evaluate our method. To this end, we consider a variety of problems, ranging from automated machine learning (AUTOML), robotic arm control, to racing line optimization. We provide comparisons against a comprehensive selection of state-of-the-art baselines. |
| Researcher Affiliation | Collaboration | 1University of Sydney, Sydney, Australia 2CSIRO s Data61, Sydney, Australia 3Amazon, Berlin, Germany 4NVIDIA, Seattle, WA, USA. |
| Pseudocode | Yes | Algorithm 1: Bayesian optimization by densityratio estimation (BORE). |
| Open Source Code | Yes | Our open-source implementation is available at https://github.com/ltiao/bore. |
| Open Datasets | Yes | We consider four datasets: PROTEIN, NAVAL, PARKINSONS, and SLICE, and utilize HPOBench (Klein & Hutter, 2019)... We utilize NASBench201 (Dong & Yang, 2020), which tabulates precomputed results from all possible 56 = 15, 625 combinations for each of the three datasets: CIFAR-10, CIFAR-100 (Krizhevsky et al., 2009), and Image Net-16 (Chrabaszcz et al., 2017). |
| Dataset Splits | Yes | We consider four datasets: PROTEIN, NAVAL, PARKINSONS, and SLICE, and utilize HPOBench (Klein & Hutter, 2019) which tabulates, for each dataset, the MSEs resulting from all possible (62,208) configurations. Additional details are included in Appendix K.1... We utilize NASBench201 (Dong & Yang, 2020), which tabulates precomputed results from all possible 56 = 15, 625 combinations for each of the three datasets: CIFAR-10, CIFAR-100 (Krizhevsky et al., 2009), and Image Net-16 (Chrabaszcz et al., 2017). |
| Hardware Specification | No | The paper does not provide specific hardware details (like GPU/CPU models or types) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like XGBoost and L-BFGS, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We set γ = 1/3 across all variants and benchmarks. For candidate suggestion in the tree-based variants, we use RS with a function evaluation limit of 500 for problems with discrete domains, and DE with a limit of 2,000 for those with continuous domains. |