Batch Bayesian optimisation via density-ratio estimation with guarantees
Authors: Rafael Oliveira, Louis Tiao, Fabio T. Ramos
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section presents experiments assessing the theoretical results and demonstrating the practical performance of batch BORE on a series of global optimisation benchmarks. We compared our methods against GP-based BO baselines in both experiments sets. Additional experimental results, including the sequential setting (Appendix E), a description of the experiments setup (Appendix E), and further discussions on theoretical aspects can be found in the supplementary material.6 |
| Researcher Affiliation | Collaboration | Rafael Oliveira1,2 rafael.oliveira@sydney.edu.au Louis C. Tiao3 louis.tiao@sydney.edu.au Fabio Ramos3,4 fabio.ramos@sydney.edu.au 1Brain and Mind Centre, the University of Sydney, Australia 2ARC Training Centre in Data Analytics for Resources and Environments, Australia 3School of Computer Science, the University of Sydney, Australia 4NVIDIA, USA |
| Pseudocode | Yes | Algorithm 1: BORE 1 for t {1, . . . , T} do 2 τ := ˆΦ 1 t 1(γ) 3 zi := I[yi τ], i {1, . . . , t 1} 4 Dt 1 := {xi, zi}t 1 i=1 5 ˆπt argminπ L[π| Dt 1] 6 xt argmaxx X ˆπt 1(x) 7 yt := f(xt) + ϵt 8 end |
| Open Source Code | No | Code will be made available at https://github.com/rafaol/batch-bore-with-guarantees |
| Open Datasets | Yes | Real-data benchmarks. Lastly, we compared the sequential version of BORE++ against BORE and other baselines, including traditional BO methods, such as GP-UCB and GP-EI [1], the Treestructured Parzen Estimator (TPE) [15], and random search, on real-data benchmarks. In particular, we assessed the algorithms on some of the same benchmarks present in the original BORE paper [9]. ... neural architecture search on MNIST data |
| Dataset Splits | No | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See supplement. |
| Hardware Specification | No | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [No] Our focus is on theory assessments rather than computational comparisons. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See supplement. |