David Eriksson | DeepAI

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Andrew Gordon Wilson
92 publications
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Eytan Bakshy
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Geoff Pleiss
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Martin Jankowiak
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Matthias Seeger
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Hannes Nickisch
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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...

0 Aryan Deshwal, et al. ∙

∙ 10/20/2022

Discovering Many Diverse Solutions with Bayesian Optimization

Bayesian optimization (BO) is a popular approach for sample-efficient op...

0 Natalie Maus, et al. ∙

∙ 10/18/2022

Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization

Optimizing expensive-to-evaluate black-box functions of discrete (and po...

16 Samuel (Sam) Daulton, et al. ∙

∙ 03/03/2022

Sparse Bayesian Optimization

Bayesian optimization (BO) is a powerful approach to sample-efficient op...

0 Sulin Liu, et al. ∙

∙ 09/22/2021

Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces

The ability to optimize multiple competing objective functions with high...

0 Samuel (Sam) Daulton, et al. ∙

∙ 06/22/2021

Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization

When tuning the architecture and hyperparameters of large machine learni...

10 David Eriksson, et al. ∙

∙ 06/10/2021

A Nonmyopic Approach to Cost-Constrained Bayesian Optimization

Bayesian optimization (BO) is a popular method for optimizing expensive-...

0 Eric Hans Lee, et al. ∙

∙ 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...

19 Ryan Turner, et al. ∙

∙ 02/27/2021

High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces

Bayesian optimization (BO) is a powerful paradigm for efficient optimiza...

0 David Eriksson, et al. ∙

∙ 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...

9 Geoff Pleiss, et al. ∙

∙ 02/24/2020

Efficient Rollout Strategies for Bayesian Optimization

Bayesian optimization (BO) is a class of sample-efficient global optimiz...

13 Eric Hans Lee, et al. ∙

∙ 02/20/2020

Scalable Constrained Bayesian Optimization

The global optimization of a high-dimensional black-box function under b...

0 David Eriksson, et al. ∙

∙ 10/03/2019

Scalable Global Optimization via Local Bayesian Optimization

Bayesian optimization has recently emerged as a popular method for the s...

0 David Eriksson, et al. ∙

∙ 07/30/2019

pySOT and POAP: An event-driven asynchronous framework for surrogate optimization

This paper describes Plumbing for Optimization with Asynchronous Paralle...

3 David Eriksson, et al. ∙

∙ 10/29/2018

Scaling Gaussian Process Regression with Derivatives

Gaussian processes (GPs) with derivatives are useful in many application...

6 David Eriksson, et al. ∙

∙ 11/09/2017

Scalable Log Determinants for Gaussian Process Kernel Learning

For applications as varied as Bayesian neural networks, determinantal po...

0 Kun Dong, et al. ∙

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