SOTAVerified

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 1231–1240 of 1963 papers

TitleStatusHype
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck—0
Gaussian Process Manifold Interpolation for Probabilistic Atrial Activation Maps and Uncertain Conduction Velocity—0
What do you Mean? The Role of the Mean Function in Bayesian OptimisationCode0
Local Model Feature Transformations—0
Adversarial Robustness Guarantees for Random Deep Neural NetworksCode0
Reinforcement Learning via Gaussian Processes with Neural Network Dual Kernels—0
Deep Manifold Prior—0
Direct loss minimization algorithms for sparse Gaussian processesCode0
Online Constrained Model-based Reinforcement Learning—0
On Negative Transfer and Structure of Latent Functions in Multi-output Gaussian Processes—0
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Benchmark Results

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