47 citations · 111 across the 25 of their papers we have counts for
5 papers · 1 filter
Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization
Wesley Maddox, Qing Feng, Max Balandat
Physical simulation-based optimization is a common task in science and engineering. Many such simulations produce image- or tensor-based outputs where the desired objective is a fu…
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs
Raul Astudillo, Daniel R. Jiang, Maximilian Balandat +2
Bayesian optimization (BO) is a sample-efficient approach to optimizing costly-to-evaluate black-box functions. Most BO methods ignore how evaluation costs may vary over the optimi…
Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces
Samuel Daulton, David Eriksson, Maximilian Balandat +1
Many real world scientific and industrial applications require optimizing multiple competing black-box objectives. When the objectives are expensive-to-evaluate, multi-objective Ba…
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization
David Eriksson, Pierce I-Jen Chuang, Samuel Daulton +7
When tuning the architecture and hyperparameters of large machine learning models for on-device deployment, it is desirable to understand the optimal trade-offs between on-device l…
Bayesian Optimization with High-Dimensional Outputs
Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson +1
Bayesian Optimization is a sample-efficient black-box optimization procedure that is typically applied to problems with a small number of independent objectives. However, in practi…