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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 1726–1750 of 1963 papers

TitleStatusHype
Near-Optimal Active Learning of Multi-Output Gaussian ProcessesCode0
Recursive Estimation for Sparse Gaussian Process RegressionCode0
A switching Gaussian process latent force model for the identification of mechanical systems with a discontinuous nonlinearityCode0
Disentangling Uncertainties by Learning Compressed Data RepresentationCode0
Dirichlet-based Gaussian Processes for Large-scale Calibrated ClassificationCode0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
Continual Multi-task Gaussian ProcessesCode0
Contextual Combinatorial Bandits with Changing Action Sets via Gaussian ProcessesCode0
Sparse Algorithms for Markovian Gaussian ProcessesCode0
Neural Inference of Gaussian Processes for Time Series Data of QuasarsCode0
Direct loss minimization algorithms for sparse Gaussian processesCode0
Sparse Cholesky Factorization for Solving Nonlinear PDEs via Gaussian ProcessesCode0
A Bayesian Take on Gaussian Process NetworksCode0
A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process ModelCode0
Neural Network Gaussian Processes by Increasing DepthCode0
Constant-Time Predictive Distributions for Gaussian ProcessesCode0
Diffusion-aware Censored Gaussian Processes for Demand ModellingCode0
Heartbeat classification using various machine learning models: A comparative studyCode0
Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian OptimizationCode0
A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian ProcessesCode0
Heterogeneous Multi-output Gaussian Process PredictionCode0
Heterogeneous Multi-Task Gaussian Cox ProcessesCode0
Sparse Gaussian Neural ProcessesCode0
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential EquationsCode0
Neural Non-Stationary Spectral KernelCode0
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Benchmark Results

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