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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 701–725 of 1963 papers

TitleStatusHype
Functional Causal Bayesian Optimization—0
Functional Gaussian processes for regression with linear PDE models—0
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction—0
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models—0
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference—0
A Bulirsch-Stoer algorithm using Gaussian processes—0
A Sparse Gaussian Process Framework for Photometric Redshift Estimation—0
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes—0
Fusion of Gaussian Processes Predictions with Monte Carlo Sampling—0
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes—0
aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture—0
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes—0
Gaussian Processes Over Graphs—0
Bayesian Warped Gaussian Processes—0
Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data—0
Gaussian Control Barrier Functions : A Non-Parametric Paradigm to Safety—0
Gaussian-Dirichlet Random Fields for Inference over High Dimensional Categorical Observations—0
Gaussian Experts Selection using Graphical Models—0
Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes—0
Gaussian Mixture Marginal Distributions for Modelling Remaining Pipe Wall Thickness of Critical Water Mains in Non-Destructive Evaluation—0
Gaussian Processes on Graphs via Spectral Kernel Learning—0
Gaussian-Process-based Adaptive Tracking Control with Dynamic Active Learning for Autonomous Ground Vehicles—0
Gaussian process based nonlinear latent structure discovery in multivariate spike train data—0
Gaussian Process-Based Nonlinear Moving Horizon Estimation—0
A Perspective on Gaussian Processes for Earth Observation—0
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Benchmark Results

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