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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1101–1150 of 1963 papers

TitleStatusHype
Sparse discovery of differential equations based on multi-fidelity Gaussian process—0
Sparse Gaussian Processes for Stochastic Differential Equations—0
Sparse Gaussian processes using pseudo-inputs—0
Sparse Gaussian Processes via Parametric Families of Compactly-supported Kernels—0
Sparse Gaussian Processes with Spherical Harmonic Features—0
Sparse Gaussian Processes with Spherical Harmonic Features Revisited—0
Scalable Grouped Gaussian Processes via Direct Cholesky Functional Representations—0
Sparse Kernel Gaussian Processes through Iterative Charted Refinement (ICR)—0
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning—0
Sparse Variational Contaminated Noise Gaussian Process Regression with Applications in Geomagnetic Perturbations Forecasting—0
Sparse Variational Student-t Processes—0
Sparse within Sparse Gaussian Processes using Neighbor Information—0
Sparsifying Suprema of Gaussian Processes—0
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs—0
Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows—0
Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features—0
Spatiotemporal Besov Priors for Bayesian Inverse Problems—0
Spatio-temporal DeepKriging for Interpolation and Probabilistic Forecasting—0
Spatio-temporal Gaussian processes modeling of dynamical systems in systems biology—0
Spatiotemporal modeling of European paleoclimate using doubly sparse Gaussian processes—0
Neural variational Data Assimilation with Uncertainty Quantification using SPDE priors—0
Spectral Angle Based Unary Energy Functions for Spatial-Spectral Hyperspectral Classification using Markov Random Fields—0
Spectral band selection for vegetation properties retrieval using Gaussian processes regression—0
Spectral complexity of deep neural networks—0
Spectral Mixture Kernels for Multi-Output Gaussian Processes—0
Compressible Spectral Mixture Kernels with Sparse Dependency Structures for Gaussian Processes—0
Spectrum Gaussian Processes Based On Tunable Basis Functions—0
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes—0
Spike and Slab Gaussian Process Latent Variable Models—0
Splitting Gaussian Process Regression for Streaming Data—0
srMO-BO-3GP: A sequential regularized multi-objective constrained Bayesian optimization for design applications—0
Stable spline identification of linear systems under missing data—0
STACI: Spatio-Temporal Aleatoric Conformal Inference—0
Stagewise Safe Bayesian Optimization with Gaussian Processes—0
State Space Gaussian Processes with Non-Gaussian Likelihood—0
State Space representation of non-stationary Gaussian Processes—0
Stationarity without mean reversion in improper Gaussian processes—0
Statistical abstraction for multi-scale spatio-temporal systems—0
Statistical Analysis of the LMS Algorithm for Proper and Improper Gaussian Processes—0
Statistical Deep Learning for Spatial and Spatio-Temporal Data—0
Steps Toward Deep Kernel Methods from Infinite Neural Networks—0
Stochastic data-driven model predictive control using Gaussian processes—0
Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes—0
Stochastic Inference of Plate Bending from Heterogeneous Data: Physics-informed Gaussian Processes via Kirchhoff-Love Theory—0
Stochastic Model Predictive Control Utilizing Bayesian Neural Networks—0
Stochastic MPC for energy hubs using data driven demand forecasting—0
Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processes—0
Stochastic Portfolio Theory: A Machine Learning Perspective—0
Stochastic Process Bandits: Upper Confidence Bounds Algorithms via Generic Chaining—0
Stochastic Variational Deep Kernel Learning—0
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Benchmark Results

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