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Sparse Gaussian Processes are a key component of high-throughput Bayesian optimisation (BO) loops -- an increasingly common setting where evaluation budgets are large and highly parallelised.
An exact algorithm for maximum entropy sampling
Chun-Wa Ko, Jon Lee, and Maurice Queyranne · 1995
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Gaussian processes in machine learning
Carl Edward Rasmussen · 2004
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Gaussian process optimization in the bandit setting: No regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham M Kakade, and Matthias Seeger · 2009
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Variational learning of inducing variables in sparse gaussian processes
Michalis Titsias · 2009
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Entropy search for information-efficient global optimization
Philipp Hennig and Christian J Schuler · 2012
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Determinantal point processes for machine learning
Alex Kulesza and Ben Taskar · 2012
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Deep gaussian processes
Andreas Damianou and Neil D Lawrence · 2013
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Gaussian processes for big data
James Hensman, Nicolo Fusi, and Neil D Lawrence · 2013
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Predictive entropy search for efficient global optimization of black-box functions
José Miguel Hernández-Lobato, Matthew W Hoffman, and Zoubin Ghahramani · 2014
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Exact sampling from determinantal point processes
Philipp Hennig and Roman Garnett · 2016
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Taking the human out of the loop: A review of Bayesian optimization
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P Adams, and Nando De Freitas · 2016
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Max-value entropy search for efficient bayesian optimization
Zi Wang and Stefanie Jegelka · 2017
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Rates of convergence for sparse variational gaussian process regression
David Burt, Carl Edward Rasmussen, and Mark Van Der Wilk · 2019
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Multi-fidelity bayesian optimization with max-value entropy search
Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, and Masayuki Karasuyama · 2019
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Mumbo: Multi-task max-value bayesian optimization
Henry B Moss, David S Leslie, and Paul Rayson · 2020
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Bayesian quantile and expectile optimisation
Léonard Torossian, Victor Picheny, and Nicolas Durrande · 2020
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Efficiently sampling functions from gaussian process posteriors
James Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, and Marc Deisenroth · 2020
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Fast greedy map inference for determinantal point process to improve recommendation diversity
Laming Chen, Guoxin Zhang, and Hanning Zhou · 2018
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Fast information-theoretic bayesian optimisation
Binxin Ru, Michael A Osborne, Mark McLeod, and Diego Granziol · 2018
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Gibbon: General-purpose information-based bayesian optimisation
Henry B Moss, David S Leslie, Javier Gonzalez, and Paul Rayson · 2021
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Scalable thompson sampling using sparse gaussian process models
Sattar Vakili, Henry Moss, Artem Artemev, Vincent Dutordoir, and Victor Picheny · 2021
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