Gaussian processes as response surfaces for optimization
Adversarially Robust Optimization with Gaussian Processes
... Introduction Gaussian processes (GP) provide a powerful means for sequentially optimizing a black-box function f that is costly to evaluate and for which noisy point evaluations are ...the ...
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A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes
... this optimization task de- pends on the dimension of feature space, approximation methods are needed to speed up the learning ...single-fidelity optimization approaches as a subroutine, so that one can take ...
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Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm
... the optimization of expensive, black-box functions involving multiple conflicting criteria, such that commonly used methods like multiobjective genetic algorithms are ...uses Gaussian processes as ...
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Efficient Global Optimization using Deep Gaussian Processes
... This Gaussian distribution is used as input in the layer l and the mean and covariance of the non-Gaussian output distribution at the layer l is obtained using the GPLVM prediction formula ...be ...
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Optimization of the SHiP Spectrometer Tracker geometry using the Bayesian Optimization with Gaussian Processes
... eter Tracker geometry optimization using Bayesian optimization with Gaussian 13.. processes in considered.[r] ...
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Faster Multi-Objective Optimization: Cumulating Gaussian Processes, Preference Point and Parallelism
... gradient on Gaussian Process (GP) mean (Zerbinati et al. [18]), the family of Bayesian MOO (EHI, SMS, SUR, EMI – GPareto [1], Wagner et al. [16] –), constrained EHI (Feliot et al. [4]). They target the entire ...
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Gaussian process emulation for discontinuous response surfaces with applications for cardiac electrophysiology models
... used Gaussian Processes (GPs) to statistically model the output response surfaces of the ...discontinuous response surfaces, as we show in ...
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Deep Gaussian Processes
... In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a deep belief net- work based on Gaussian process mappings. The data is modeled as the output of a multivariate GP. The inputs to ...
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Data-driven Demand Response Modeling and Control of Buildings with Gaussian Processes
... The authors are with the Automatic Control Laboratory, École Polytech- nique Fédérale de Lausanne, Lausanne, Switzerland. and air-conditioning (HVAC) systems of the buildings as the HVAC system is often the largest ...
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Gaussian processes for computer experiments
... sequential optimization procedure may be either seen as maximizing the sum of the outputs (or rewards) received at each iteration, that is to minimize the cumulative regret widely used in bandit problems, or as ...
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Regularized Sparse Gaussian Processes
... sparse Gaussian pro- cesses and sparse latent Gaussian ...global optimization in model fitting and achieves better model ...latent Gaussian processes, the use of regularization is also ...
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Physical layer authentication based on channel response tracking using Gaussian processes
... In this paper, we propose a technique based on Gaus- sian processes (GPs) that solves two issues found in the cur- rent hypothesis-test based methods. First, they typically only serve as a detector for intruders, ...
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Theoretical enhancement of the Gaussian filtering of engineering surfaces
... abrasive processes such as grinding often had a Gaussian form, which introduced a level of symmetry between the amplitude and wavelength/frequency characteristics of the ...
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Gaussian Processes for Blazar Variability Studies
... physical processes of ultracompact jets, like the de-projected distance between the core (at each frequency) and the jet base, as well as the strength of the magnetic field along the flow ...unit-opacity ...
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Geodesic on surfaces of constant Gaussian curvature
... The Gaussian curvature can tell us a lot about a ...the Gaussian curvature would not be a geometric invariant and, therefore, would not be as helpful in studying ...
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Detecting periodicities with Gaussian processes
... ‘Quantifying the Periodicity t’ introduces a new criterion for measuring the periodicity of the signal. Finally, the last section illustrates the proposed approach on a biological case study where we detect, amongst the ...
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CONVEX BODIES AND GAUSSIAN PROCESSES
... (Accepted October 11, 2009) ABSTRACT For several decades, the topics of the title have had a fruitful interaction. This survey will describe some of these connections, including the GB/GC classification of convex bodies, ...
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Sparse Online Gaussian Processes
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Functional quantization of Gaussian processes
... stochastic processes ðX t Þ tA½0;1 viewed as L 2 ð½0; 1; dtÞ-valued random ...For Gaussian vectors and the L 2 -error we present detailed results for stationary and optimal ...
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Kernel regression and gaussian processes
... of Gaussian distribution In order to introduce Gaussian processes and how they can be exploited for regression, let us first provide a short reminder on some properties of multivariate ...