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 931–940 of 1963 papers

TitleStatusHype
Bayesian Meta-Learning Through Variational Gaussian ProcessesCode0
Computational Graph Completion—0
Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning—0
On Estimating the Probabilistic Region of Attraction for Partially Unknown Nonlinear Systems: An Sum-of-Squares Approach—0
Adversarial Attacks on Gaussian Process BanditsCode0
Inferring Manifolds From Noisy Data Using Gaussian ProcessesCode0
Function-space Inference with Sparse Implicit ProcessesCode0
Incremental Ensemble Gaussian Processes—0
On out-of-distribution detection with Bayesian neural networksCode0
LazyPPL: laziness and types in non-parametric probabilistic programs—0
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Benchmark Results

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