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Gaussian Processes

Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.

Source: Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization

Papers

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Showing 1901–1925 of 1963 papers

TitleStatusHype
Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models - a Gentle Tutorial—0
Input Warping for Bayesian Optimization of Non-stationary Functions—0
Accelerating ABC methods using Gaussian processes—0
EigenGP: Gaussian Process Models with Adaptive EigenfunctionsCode0
Associative embeddings for large-scale knowledge transfer with self-assessment—0
Multi-Task Bayesian Optimization—0
Bayesian optimization explains human active search—0
Gaussian Process Optimization with Mutual Information—0
Nonparametric Bayes dynamic modeling of relational data—0
Active Learning of Linear Embeddings for Gaussian Processes—0
GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes—0
Pseudo-Marginal Bayesian Inference for Gaussian Processes—0
A temporal model of text periodicities using Gaussian Processes—0
Reasoning about Probabilities in Dynamic Systems using Goal Regression—0
Modelling Annotator Bias with Multi-task Gaussian Processes: An Application to Machine Translation Quality Estimation—0
Infinite Mixtures of Multivariate Gaussian Processes—0
Bayesian Structured Prediction Using Gaussian ProcessesCode0
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC—0
Parallel Gaussian Process Regression with Low-Rank Covariance Matrix ApproximationsCode0
Evolution of Covariance Functions for Gaussian Process Regression using Genetic Programming—0
A dependent partition-valued process for multitask clustering and time evolving network modelling—0
Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes—0
Gaussian Processes for Nonlinear Signal Processing—0
Gaussian Process Kernels for Pattern Discovery and ExtrapolationCode0
Multiresolution Gaussian Processes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICKy, periodicRoot mean square error (RMSE)0.03—Unverified