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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 721–730 of 1963 papers

TitleStatusHype
A piece-wise constant approximation for non-conjugate Gaussian Process modelsCode0
Inducing Gaussian Process Networks—0
Active Learning with Weak Supervision for Gaussian ProcessesCode0
Gaussian Processes for Missing Value ImputationCode1
PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations—0
GP-BART: a novel Bayesian additive regression trees approach using Gaussian processesCode1
Discovering and forecasting extreme events via active learning in neural operators—0
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process PriorsCode1
Autoencoder Attractors for Uncertainty EstimationCode0
INSPIRE: Distributed Bayesian Optimization for ImproviNg SPatIal REuse in Dense WLANs—0
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Benchmark Results

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