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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 101–110 of 1963 papers

TitleStatusHype
Active Testing: Sample-Efficient Model EvaluationCode1
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decompositionCode1
Actually Sparse Variational Gaussian ProcessesCode1
Causal Discovery via Bayesian OptimizationCode1
High-Dimensional Gaussian Process Inference with DerivativesCode1
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage GuaranteesCode1
Accounting for Input Noise in Gaussian Process Parameter RetrievalCode1
Building 3D Morphable Models from a Single ScanCode1
On Feature Collapse and Deep Kernel Learning for Single Forward Pass UncertaintyCode1
A tutorial on learning from preferences and choices with Gaussian ProcessesCode1
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Benchmark Results

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