A Portfolio Approach to Massively Parallel Bayesian Optimization
Authors: Mickael Binois, Nicholson Collier, Jonathan Ozik
JAIR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare the approach with related methods on noisy functions, for mono and multi-objective optimization tasks. These experiments show orders of magnitude speed improvements over existing methods with similar or better performance. |
| Researcher Affiliation | Academia | Mickaël Binois EMAIL Inria, Université Côte d Azur, CNRS, LJAD Sophia Antipolis, France; Nicholson Collier EMAIL Jonathan Ozik EMAIL Argonne National Laboratory, Lemont, IL, USA Consortium for Advanced Science and Engineering, University of Chicago Chicago, IL, USA |
| Pseudocode | Yes | Algorithm 1 Pseudo-code for batch BO; Algorithm 2 Pseudo-code for batch BO with q HSRI |
| Open Source Code | Yes | The R code (R Core Team, 2023) of the approach is available as supplementary material. |
| Open Datasets | Yes | We consider the training of a convolutional neural network (CNN) used for the classification of digits based on the MNIST data (Le Cun et al., 1998); The R code (R Core Team, 2023) of the approach and the City COVID data are available as supplementary material. |
| Dataset Splits | Yes | CNN used for the classification of digits based on the MNIST data (Le Cun et al., 1998), with 70,000 handwritten digits (including 10,000 for testing). |
| Hardware Specification | Yes | Results have been obtained in parallel on dual-Xeon Skylake SP Silver 4114 @ 2.20GHz (20 cores) and 192 GB RAM (or similar nodes). Lunar lander tests have been run on a laptop. The CNN training is performed on Ge Force GTX 1080 Ti GPUs. |
| Software Dependencies | Yes | The R package het GP (Binois & Gramacy, 2021) is used for noisy GP modeling. Dice Optim (Picheny et al., 2021), or the approximated one from Binois (2015), q AEI. pso (Bendtsen, 2012) (population of size 200) is conducted too. NSGA-II (Deb et al., 2002) from mco (Mersmann, 2020) is used to find P. The R package Dice Kriging (Roustant et al., 2012) is used for deterministic GP modeling. |
| Experiment Setup | Yes | The six variables of the CNN are given in Table 3. The architecture is composed of two 2D convolutional + max pooling layers, before two dense layers with dropout. The reference point used for hypervolume computations is [0, 150]. The CNN training is performed on Ge Force GTX 1080 Ti GPUs. The nine variables of the simulator are given in Table 4. |