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03/03/2023
Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings
We consider the problem of optimizing expensive black-box functions over...
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10/20/2022
Discovering Many Diverse Solutions with Bayesian Optimization
Bayesian optimization (BO) is a popular approach for sample-efficient op...
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10/18/2022
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
Optimizing expensive-to-evaluate black-box functions of discrete (and po...
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03/03/2022
Sparse Bayesian Optimization
Bayesian optimization (BO) is a powerful approach to sample-efficient op...
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09/22/2021
Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces
The ability to optimize multiple competing objective functions with high...
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06/22/2021
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization
When tuning the architecture and hyperparameters of large machine learni...
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06/10/2021
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization
Bayesian optimization (BO) is a popular method for optimizing expensive-...
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04/20/2021
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020
This paper presents the results and insights from the black-box optimiza...
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02/27/2021
High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces
Bayesian optimization (BO) is a powerful paradigm for efficient optimiza...
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06/19/2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Matrix square roots and their inverses arise frequently in machine learn...
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02/24/2020
Efficient Rollout Strategies for Bayesian Optimization
Bayesian optimization (BO) is a class of sample-efficient global optimiz...
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02/20/2020
Scalable Constrained Bayesian Optimization
The global optimization of a high-dimensional black-box function under b...
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10/03/2019
Scalable Global Optimization via Local Bayesian Optimization
Bayesian optimization has recently emerged as a popular method for the s...
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07/30/2019
pySOT and POAP: An event-driven asynchronous framework for surrogate optimization
This paper describes Plumbing for Optimization with Asynchronous Paralle...
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10/29/2018
Scaling Gaussian Process Regression with Derivatives
Gaussian processes (GPs) with derivatives are useful in many application...
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11/09/2017
