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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 201–225 of 1963 papers

TitleStatusHype
Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO AlgorithmCode0
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesCode0
Fast covariance parameter estimation of spatial Gaussian process models using neural networksCode0
Fast Approximate Multi-output Gaussian ProcessesCode0
How Good are Low-Rank Approximations in Gaussian Process Regression?Code0
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian OptimizationCode0
An accuracy-runtime trade-off comparison of scalable Gaussian process approximations for spatial dataCode0
Amortized Variational Inference: When and Why?Code0
Explainable Learning with Gaussian ProcessesCode0
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian ProcessesCode0
Evolving-Graph Gaussian ProcessesCode0
Evaluating Uncertainty in Deep Gaussian ProcessesCode0
Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian ProcessesCode0
Amortized Inference for Gaussian Process Hyperparameters of Structured KernelsCode0
Evaluating the squared-exponential covariance function in Gaussian processes with integral observationsCode0
Exact Gaussian Processes on a Million Data PointsCode0
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
Adaptive Cholesky Gaussian ProcessesCode0
Estimating Latent Demand of Shared Mobility through Censored Gaussian ProcessesCode0
Entropic Trace Estimates for Log DeterminantsCode0
Batch Bayesian Optimization via Local PenalizationCode0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Epistemic Uncertainty in Conformal Scores: A Unified ApproachCode0
Estimation of Dynamic Gaussian ProcessesCode0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
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Benchmark Results

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