Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
Authors: Shangda Yang, Vitaly Zankin, Maximilian Balandat, Stefan Scherer, Kevin Thomas Carlberg, Neil Walton, Kody J. H. Law
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our findings are verified numerically and the benefits of MLMC for BO are illustrated on several benchmark examples. Code is available at https://github.com/Shangda-Yang/MLMCBO. and Section 5 Numerical Results describes experiments on synthetic functions, showing figures and tables of performance. |
| Researcher Affiliation | Collaboration | 1Department of Mathematics, University of Manchester 2Meta Platforms, Inc. 3Durham University Business School. |
| Pseudocode | Yes | Algorithm 1 Bayesian optimization and Algorithm 2 Single Design with MLMC acquisition |
| Open Source Code | Yes | Code is available at https://github.com/Shangda-Yang/MLMCBO. |
| Open Datasets | No | The paper states it uses various synthetic functions from BoTorch (Balandat et al., 2020) and lists function names like Ackley, Drop Wave, Branin, Hartmann6, Cosine8. While these are well-known benchmark functions, no explicit link, DOI, or specific citation for generated datasets or their public availability is provided. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits, nor does it refer to predefined standard splits for the synthetic functions used. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run its experiments. |
| Software Dependencies | No | The paper mentions BoTorch (Balandat et al., 2020) but does not provide specific version numbers for BoTorch or any other software dependencies, libraries, or solvers used in the experiments. |
| Experiment Setup | Yes | Here, we use ε = 0.2 for Figure 3(a), 3(b), 3(c), 3(d) and 3(f), and ε = 0.15 for Figure 3(e). and The initial BO run starts with 2d observations. and The number of samples in MC is N = M = 1/ε2. The number of samples in MLMC for Nl and Ml, related to ε, is chosen according to the formula in Theorem 1. |