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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 1891–1900 of 1963 papers

TitleStatusHype
Functional Gaussian processes for regression with linear PDE models—0
Bayesian Multi-Scale Optimistic Optimization—0
Avoiding pathologies in very deep networksCode0
Manifold Gaussian Processes for RegressionCode0
Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data—0
The Random Forest Kernel and other kernels for big data from random partitions—0
Automatic Construction and Natural-Language Description of Nonparametric Regression ModelsCode0
Student-t Processes as Alternatives to Gaussian Processes—0
Gaussian Process Volatility Model—0
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable ModelsCode0
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Benchmark Results

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