Abstract. This paper presents a novel distributed batch Gaussian process upper confidence bound (DB-GP-UCB) algorithm for performing batch Bayesian optimization ...
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This paper presents a novel distributed batch. Gaussian process upper confidence bound. (DB-GP-UCB) algorithm for performing batch. Bayesian optimization ...
This paper presents a novel distributed batch Gaussian process upper confidence bound (DB-GP-UCB) algorithm for performing batch Bayesian optimization (BO) ...
Distributed predictive control based on Gaussian process models
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A suboptimal approach to distributed NMPC is proposed based on Gaussian process models of the interconnected systems dynamics and taking into account the ...
GPyTorch makes it possible to train/perform inference with a batch of Gaussian processes in parallel. This can be useful for a number of applications: Modeling ...
In this paper, we propose a new approach for parallelizing. Bayesian optimization by modeling the diversity of a batch via Determinantal point processes (DPPs) ...
In this paper, we propose ensemble Bayesian optimization (EBO) to address three current challenges in BO simul- taneously: (1) large-scale observations; (2) ...
In this paper, we introduce BBKB (Batch Budgeted Kernel Bandits), the first no-regret GP optimization algorithm that provably runs in near-linear time and ...
Composite Bayesian Optimization with Multi-Task Gaussian Processes¶. In this tutorial, we'll be describing how to perform multi-task Bayesian optimization ...
Distributed batch Gaussian process optimization. In Proc. ICML, 951-960. Decentralized highdimensional Bayesian optimization with factor graphs. Jan 2018.