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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 331–340 of 1963 papers

TitleStatusHype
Amortized Bayesian Local Interpolation NetworK: Fast covariance parameter estimation for Gaussian Processes—0
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data—0
A spectral mixture representation of isotropic kernels to generalize random Fourier features—0
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference—0
Residual Deep Gaussian Processes on ManifoldsCode0
Robust Gaussian Processes via Relevance Pursuit—0
Inferring the Morphology of the Galactic Center Excess with Gaussian ProcessesCode0
Omics-driven hybrid dynamic modeling of bioprocesses with uncertainty estimation—0
Learning signals defined on graphs with optimal transport and Gaussian process regression—0
BI-EqNO: Generalized Approximate Bayesian Inference with an Equivariant Neural Operator Framework—0
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Benchmark Results

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