Gaussian Processes
Robustness Guarantees for Bayesian Inference with Gaussian Processes
... In this paper we presented a formal approach for safety anal- ysis of Bayesian inference with Gaussian process priors with respect to adversarial examples and invariance properties. As the properties considered in ...
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Optimality of Poisson Processes Intensity Learning with Gaussian Processes
... Several papers have explored nonparametric Bayesian approaches in this setting. An early reference is Møller et al. (1998), who study log-Gaussian priors. Gugushvili and Spreij (2013) recently considered ...
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Discriminative training for Convolved Multiple-Output Gaussian processes
... the Gaussian processes community in Machine learning has been the formulation of suitable covariance functions for describing multiple output processes as a joint Gaussian ...multi-task ...
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Extremes of threshold-dependent Gaussian processes
... of Gaussian processes with stationary increments with specific drift to more general family of Gaussian processes with general drift ...stationary Gaussian processes with general ...
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Gaussian Processes for Machine Learning
... One important distinction between different learning algorithms is how they relate to the question of universal consistency (see section 7.2.1). For example a linear regression model will be inconsistent if the function ...
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Near-Optimal Sensor Placements in Gaussian Processes
... in Gaussian processes, (ii) show the ex- act optimization in NP-complete, (iii) provide an approxima- tion algorithm that is within (1 − 1/e) of the maximum mu- tual information configuration, (iv) show how ...
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Bayesian Warped Gaussian Processes
... Warped Gaussian processes (WGP) [1] model output observations in regression tasks as a parametric nonlinear transformation of a Gaussian process ...
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Differentially Private Gaussian Processes
... A major challenge for machine learning is increasing the availability of data while respecting the privacy of individuals. Here we combine the provable privacy guarantees of the Differential Privacy framework with the ...
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A Churn prediction model based on gaussian processes
... This report is written in the context of IKT 590 Master thesis to fulfil a third semester requirement of master in information and communication technologies at the faculty of engineering and science at the University of ...
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Gaussian Processes for Blazar Variability Studies
... An elegant way to overcome this state of limited knowledge is by means of Bayesian inference. Specifically, this paper is concerned with the application of Gaussian processes in the field of blazar ...
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Gaussian Processes for Machine Learning (GPML) Toolbox
... Gaussian processes (GPs) (Rasmussen and Williams, 2006) have convenient properties for many modelling tasks in machine learning and statistics. They can be used to specify distributions over functions ...
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Computationally Efficient Convolved Multiple Output Gaussian Processes
... a Gaussian processes perspective, the problem reduces to spec- ifying an appropriate covariance function that, whilst being positive semi-definite, captures the dependencies between all the data points and ...
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Gaussian Processes in Machine Learning
... a possible advantage in ease of interpretability, but for complex data sets, simple parametric models may lack expressive power, and their more complex counter- parts (such as feed forward neural networks) may not be ...
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Embarrassingly Parallel Inference for Gaussian Processes
... partitioned Gaussian processes that is flexible and easily ...over Gaussian processes with block-diagonal covariance ...partitioned Gaussian processes sampled from the ...
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Deep Gaussian Processes and Variational Propagation of Uncertainty
... a Gaussian process regression prob- lem are partially ...with Gaussian processes. An auto-regressive Gaussian process is also defined as a special ...
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How Deep Are Deep Gaussian Processes?
... Gaussian processes have proved remarkably successful as a tool for various statistical infer- ence and machine learning tasks (Rasmussen and Williams, 2006; Kennedy and O’Hagan, 2001; Higdon et ...the ...
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Variational Fourier Features for Gaussian Processes
... in Gaussian processes: the variational approach to sparse approximation and the spectral representation of Gaussian ...the Gaussian process which exhibit almost-independent ...additive ...
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GPstuff: Bayesian Modeling with Gaussian Processes
... the Gaussian processes for Machine Learn- ing (GPML) (Rasmussen and Nickisch, 2010) and the flexible Bayesian modelling (FBM) (Neal, ...the Gaussian processes website ...
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Interpolation of intermolecular potentials using Gaussian processes
... distances. Gaussian processes are used to interpolate the data, using over-specified inverse molecular distances as covariates, greatly improving the ...
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Forecasting modeling with kernel function integration in gaussian processes
... a Gaussian process for finding and learning patterns from data attributes that fit the structure of time series ...In Gaussian processes, researchers do not need to modify the algorithm's layout at ...
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