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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 891–900 of 1963 papers

TitleStatusHype
Scaling Gaussian Processes with Derivative Information Using Variational Inference—0
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning—0
Harnessing Heterogeneity: Learning from Decomposed Feedback in Bayesian Modeling—0
Deep Gaussian Process Emulation using Stochastic ImputationCode1
Random Neural Networks in the Infinite Width Limit as Gaussian Processes—0
Scale Mixtures of Neural Network Gaussian ProcessesCode0
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization—0
Personalized Federated Learning with Gaussian ProcessesCode1
Evolving-Graph Gaussian ProcessesCode0
Variance Reduction for Matrix Computations with Applications to Gaussian Processes—0
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Benchmark Results

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