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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 361–370 of 1963 papers

TitleStatusHype
Amortized Variational Inference for Deep Gaussian Processes—0
Conformal Prediction for Manifold-based Source Localization with Gaussian Processes—0
Decomposing Gaussians with Unknown CovarianceCode0
Probabilistic Spatiotemporal Modeling of Day-Ahead Wind Power Generation with Input-Warped Gaussian Processes—0
Predicting Electricity Consumption with Random Walks on Gaussian Processes—0
Inference for Large Scale Regression Models with Dependent Errors—0
Heartbeat classification using various machine learning models: A comparative studyCode0
Multi-Task Combinatorial Bandits for Budget Allocation—0
Safe Bayesian Optimization for Complex Control Systems via Additive Gaussian Processes—0
Bayesian optimization of atomic structures with prior probabilities from universal interatomic potentialsCode0
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Benchmark Results

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