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 1376–1400 of 1963 papers

TitleStatusHype
Kalman Filtering with Gaussian Processes Measurement Noise—0
Differentially Private Regression and Classification with Sparse Gaussian Processes—0
No-Regret Learning in Unknown Games with Correlated PayoffsCode0
Bayesian Optimisation with Gaussian Processes for Premise Selection—0
Compositional uncertainty in deep Gaussian processesCode0
Band-Limited Gaussian Processes: The Sinc Kernel—0
Coarse-scale PDEs from fine-scale observations via machine learning—0
Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version—0
Finite size corrections for neural network Gaussian processes—0
Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal DynamicsCode0
Using Contextual Information to Improve Blood Glucose Prediction—0
Mixture-based Multiple Imputation Model for Clinical Data with a Temporal DimensionCode0
Stochastic data-driven model predictive control using Gaussian processes—0
Gaussian Process Models of Sound Change in Indo-Aryan Dialectology—0
Sequential Learning of Active SubspacesCode0
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs—0
Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators—0
Structured Variational Inference in Unstable Gaussian Process State Space ModelsCode0
The Use of Gaussian Processes in System Identification—0
Gaussian Processes for Analyzing Positioned Trajectories in Sports—0
The Debiased Spatial Whittle LikelihoodCode0
Learning GPLVM with arbitrary kernels using the unscented transformationCode0
Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches—0
Gaussian Mixture Marginal Distributions for Modelling Remaining Pipe Wall Thickness of Critical Water Mains in Non-Destructive Evaluation—0
Spatio-thermal depth correction of RGB-D sensors based on Gaussian Processes in real-timeCode0
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Benchmark Results

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