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 471–480 of 1963 papers

TitleStatusHype
Meta-Learning Mean Functions for Gaussian Processes—0
Amortized variance reduction for doubly stochastic objectives—0
Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data—0
Aggregation Models with Optimal Weights for Distributed Gaussian Processes—0
Deep Neural Networks as Point Estimates for Deep Gaussian Processes—0
Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments—0
Distributed Gaussian Processes—0
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification—0
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
Combining Gaussian processes and polynomial chaos expansions for stochastic nonlinear model predictive control—0
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Benchmark Results

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