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 1141–1150 of 1963 papers

TitleStatusHype
Design of Experiments for Verifying Biomolecular Networks—0
Safe model-based design of experiments using Gaussian processes—0
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian ProcessesCode0
Cluster-Specific Predictions with Multi-Task Gaussian ProcessesCode0
Entropic regularization of Wasserstein distance between infinite-dimensional Gaussian measures and Gaussian processes—0
Learning ODE Models with Qualitative Structure Using Gaussian ProcessesCode0
Sparse within Sparse Gaussian Processes using Neighbor Information—0
Gaussian Processes with Skewed Laplace Spectral Mixture Kernels for Long-term Forecasting—0
Bayesian Nonparametric Dimensionality Reduction of Categorical Data for Predicting Severity of COVID-19 in Pregnant Women—0
Continuous surrogate-based optimization algorithms are well-suited for expensive discrete problems—0
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Benchmark Results

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