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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 281–290 of 1963 papers

TitleStatusHype
EigenGP: Gaussian Process Models with Adaptive EigenfunctionsCode0
The Debiased Spatial Whittle LikelihoodCode0
Embarrassingly Parallel Inference for Gaussian ProcessesCode0
Efficiently Computable Safety Bounds for Gaussian Processes in Active LearningCode0
Efficient Modeling of Latent Information in Supervised Learning using Gaussian ProcessesCode0
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF SurrogatesCode0
Model-based Reinforcement Learning for Continuous Control with Posterior SamplingCode0
Efficient Inference in Multi-task Cox Process ModelsCode0
A switching Gaussian process latent force model for the identification of mechanical systems with a discontinuous nonlinearityCode0
Active Learning of Molecular Data for Task-Specific ObjectivesCode0
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Benchmark Results

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