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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 1151–1160 of 1963 papers

TitleStatusHype
Convergence of Sparse Variational Inference in Gaussian Processes RegressionCode1
Random Forests for dependent dataCode1
Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciencesCode1
Multioutput Gaussian Processes with Functional Data: A Study on Coastal Flood Hazard AssessmentCode0
DeepKriging: Spatially Dependent Deep Neural Networks for Spatial PredictionCode1
Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks—0
MAGMA: Inference and Prediction with Multi-Task Gaussian ProcessesCode0
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian ProcessesCode1
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
Causal Inference using Gaussian Processes with Structured Latent Confounders—0
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Benchmark Results

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