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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 931–940 of 1963 papers

TitleStatusHype
Federated Estimation of Causal Effects from Observational DataCode1
Deconditional Downscaling with Gaussian ProcessesCode0
Inferring power system dynamics from synchrophasor data using Gaussian processes—0
GPy-ABCD: A Configurable Automatic Bayesian Covariance Discovery ImplementationCode1
Hierarchical Non-Stationary Temporal Gaussian Processes With L^1-Regularization—0
Nonlinear Hawkes Process with Gaussian Process Self Effects—0
Relative Positional Encoding for Transformers with Linear ComplexityCode1
Probabilistic Robust Linear Quadratic Regulators with Gaussian ProcessesCode0
Priors in Bayesian Deep Learning: A Review—0
Value-at-Risk Optimization with Gaussian Processes—0
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Benchmark Results

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