16 citations · 30 across the 3 of their papers we have counts for
3 papers
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
Samuel Daulton, Xingchen Wan, David Eriksson +3
Optimizing expensive-to-evaluate black-box functions of discrete (and potentially continuous) design parameters is a ubiquitous problem in scientific and engineering applications.…
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…