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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 276–300 of 1963 papers

TitleStatusHype
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian ProcessesCode0
Environmental Sensor Placement with Convolutional Gaussian Neural ProcessesCode0
Fast Approximate Multi-output Gaussian ProcessesCode0
Functional Regularisation for Continual Learning with Gaussian ProcessesCode0
Gaussian Process Kernels for Pattern Discovery and ExtrapolationCode0
Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processesCode0
Evaluating the squared-exponential covariance function in Gaussian processes with integral observationsCode0
Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian ProcessesCode0
Evaluating Uncertainty in Deep Gaussian ProcessesCode0
Estimating Latent Demand of Shared Mobility through Censored Gaussian ProcessesCode0
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
Estimation of Dynamic Gaussian ProcessesCode0
Evolving-Graph Gaussian ProcessesCode0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Entropic Trace Estimates for Log DeterminantsCode0
A switching Gaussian process latent force model for the identification of mechanical systems with a discontinuous nonlinearityCode0
Active Learning of Molecular Data for Task-Specific ObjectivesCode0
Embarrassingly Parallel Inference for Gaussian ProcessesCode0
Epistemic Uncertainty in Conformal Scores: A Unified ApproachCode0
A Statistical Learning View of Simple KrigingCode0
Efficient Modeling of Latent Information in Supervised Learning using Gaussian ProcessesCode0
Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural NetworksCode0
Efficient Inference in Multi-task Cox Process ModelsCode0
Efficiently Computable Safety Bounds for Gaussian Processes in Active LearningCode0
The Debiased Spatial Whittle LikelihoodCode0
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Benchmark Results

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