Robust Multi-Objective Bayesian Optimization Under Input Noise
Authors: Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we find that our approach significantly outperforms alternative methods and efficiently identifies optimal robust designs that will satisfy specifications across multiple metrics with high probability. |
| Researcher Affiliation | Collaboration | 1Meta 2University of Oxford 3Georgia Institute of Technology. Correspondence to: Samuel Daulton <sdaulton@fb.com>, Sait Cakmak <saitcakmak@fb.com>. |
| Pseudocode | No | The paper describes algorithms and methods in textual form but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is open-sourced at github.com/ facebookresearch/robust_mobo. |
| Open Datasets | Yes | Gaussian Mixture Model (GMM) (d = 2, M = 2, α = 0.9): This is a variant of the GMM problem from Fr ohlich et al. (2020)... Constrained Branin Currin (d = 2, M = 2, V = 1, α = 0.7): We subject this problem, which originates from Daulton et al. (2020)... Disc Brake (d = 4, M = 2, V = 4, α = 0.95): In this disc brake manufacturing problem... (Ray and Liew, 2002). Penicillin Production (d = 7, M = 3, α = 0.8): This problem considers optimizing the manufacturing process of penicillin (Liang and Lai, 2021). |
| Dataset Splits | No | The paper describes the number of function evaluations for Bayesian Optimization but does not specify explicit training/validation/test dataset splits for the black-box optimization problems themselves. |
| Hardware Specification | Yes | The experiments were timed on a shared cluster using 4 CPU cores, 1 GPU, and 16 GB of RAM. |
| Software Dependencies | Yes | We implemented all methods using the Bo Torch library (Balandat et al., 2020) (except for NSGA-II), leveraging the existing implementations of NEI and q NEHVI available at https://github.com/pytorch/botorch. We used the implementation of NSGA-II in the Py MOO library (Blank and Deb, 2020), which is available at https://github. com/anyoptimization/pymoo. |
| Experiment Setup | Yes | For all BO-methods, we begin by evaluating 2(d + 1) design points from a scrambled Sobol sequence. We use nξ = 32 samples... For all model-based methods, we model each objective and constraint with an independent GP with a Mat ern5 2 ARD kernel (Rasmussen, 2004). For all MC-based acquisition functions, we use NMC = 256 QMC samples from the GP posterior... For RFF-based methods, the approximate GP sample (using 512 random features)... We optimize all acquisition functions using multi-start optimization with L-BFGS-B (Zhu et al., 1997). |