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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1261–1270 of 1963 papers

TitleStatusHype
A Framework for Interdomain and Multioutput Gaussian ProcessesCode2
Stable behaviour of infinitely wide deep neural networksCode0
Solving Dynamic Discrete Choice Models Using Smoothing and Sieve Methods—0
Mixed Strategies for Robust Optimization of Unknown Objectives—0
Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning via Gaussian Processes—0
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process ModelsCode0
Near-linear Time Gaussian Process Optimization with Adaptive Batching and ResparsificationCode1
Knot Selection in Sparse Gaussian Processes—0
Efficiently Sampling Functions from Gaussian Process PosteriorsCode1
Deep Sigma Point Processes—0
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Benchmark Results

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