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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 1901–1910 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
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Benchmark Results

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