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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 301–310 of 1963 papers

TitleStatusHype
Efficient Modeling of Latent Information in Supervised Learning using Gaussian ProcessesCode0
EigenGP: Gaussian Process Models with Adaptive EigenfunctionsCode0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Evaluating the squared-exponential covariance function in Gaussian processes with integral observationsCode0
Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel DerivativesCode0
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF SurrogatesCode0
A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process ModelCode0
Efficient Inference in Multi-task Cox Process ModelsCode0
Efficient dynamic modal load reconstruction using physics-informed Gaussian processes based on frequency-sparse Fourier basis functionsCode0
A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian ProcessesCode0
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Benchmark Results

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