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Sparse Bayesian Optimization


Mar 03, 2022
Sulin Liu, Qing Feng, David Eriksson, Benjamin Letham, Eytan Bakshy


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Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces


Sep 22, 2021
Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy


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


Jun 25, 2021
David Eriksson, Pierce I-Jen Chuang, Samuel Daulton, Peng Xia, Akshat Shrivastava, Arun Babu, Shicong Zhao, Ahmed Aly, Ganesh Venkatesh, Maximilian Balandat

* To Appear at the 8th ICML Workshop on Automated Machine Learning, ICML 2021 

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A Nonmyopic Approach to Cost-Constrained Bayesian Optimization


Jun 10, 2021
Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias Seeger

* To appear in UAI 2021 

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Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020


Apr 20, 2021
Ryan Turner, David Eriksson, Michael McCourt, Juha Kiili, Eero Laaksonen, Zhen Xu, Isabelle Guyon


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High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces


Feb 27, 2021
David Eriksson, Martin Jankowiak


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Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization


Jun 19, 2020
Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner


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Efficient Rollout Strategies for Bayesian Optimization


Feb 26, 2020
Eric Hans Lee, David Eriksson, Bolong Cheng, Michael McCourt, David Bindel


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Scalable Constrained Bayesian Optimization


Feb 24, 2020
David Eriksson, Matthias Poloczek


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Scalable Global Optimization via Local Bayesian Optimization


Oct 28, 2019
David Eriksson, Michael Pearce, Jacob R Gardner, Ryan Turner, Matthias Poloczek

* Appears in NeurIPS 2019 as a spotlight presentation 

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


Jul 30, 2019
David Eriksson, David Bindel, Christine A. Shoemaker


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Scaling Gaussian Process Regression with Derivatives


Oct 29, 2018
David Eriksson, Kun Dong, Eric Hans Lee, David Bindel, Andrew Gordon Wilson

* Advances in Neural Information Processing Systems 32 (NIPS), 2018 
* Appears at Advances in Neural Information Processing Systems 32 (NIPS), 2018 

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Scalable Log Determinants for Gaussian Process Kernel Learning


Nov 09, 2017
Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew Gordon Wilson

* Advances in Neural Information Processing Systems 30 (NIPS), 2017 
* Appears at Advances in Neural Information Processing Systems 30 (NIPS), 2017 

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