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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1051–1100 of 1963 papers

TitleStatusHype
Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference—0
Disentangling Derivatives, Uncertainty and Error in Gaussian Process ModelsCode1
Mapping Leaf Area Index with a Smartphone and Gaussian Processes—0
Physics-Aware Gaussian Processes in Remote Sensing—0
Deep Gaussian Processes for geophysical parameter retrieval—0
Spectral band selection for vegetation properties retrieval using Gaussian processes regression—0
Nonlinear Distribution Regression for Remote Sensing Applications—0
Understanding Climate Impacts on Vegetation with Gaussian Processes in Granger Causality—0
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes—0
Task-Agnostic Amortized Inference of Gaussian Process HyperparametersCode1
Stochastic Deep Gaussian Processes over GraphsCode1
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data—0
Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes—0
Variance based sensitivity analysis for Monte Carlo and importance sampling reliability assessment with Gaussian processes—0
Equivalence of Convergence Rates of Posterior Distributions and Bayes Estimators for Functions and Nonparametric Functionals—0
Exploration in Online Advertising Systems with Deep Uncertainty-Aware LearningCode1
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
All You Need is a Good Functional Prior for Bayesian Deep Learning—0
Gaussian Processes for Traffic Speed Prediction at Different Aggregation Levels—0
Building 3D Morphable Models from a Single ScanCode1
Decoupled Sparse Gaussian Processes Components]Decoupled Sparse Gaussian Processes Components : Separating Decision Making from Data Manifold Fitting—0
Gaussian Process Latent Variable Flows for Massively Missing Data—0
Neural Networks as Inter-Domain Inducing Points—0
The Gaussian Process Latent Autoregressive Model—0
Functional Priors for Bayesian Neural Networks through Wasserstein Distance Minimization to Gaussian Processes—0
Neural Linear Models with Functional Gaussian Process Priors—0
Model-based Reinforcement Learning for Continuous Control with Posterior SamplingCode0
Design of Experiments for Verifying Biomolecular Networks—0
The Impact of Data on the Stability of Learning-Based Control- Extended Version—0
Safe model-based design of experiments using Gaussian processes—0
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian ProcessesCode0
Cluster-Specific Predictions with Multi-Task Gaussian ProcessesCode0
Entropic regularization of Wasserstein distance between infinite-dimensional Gaussian measures and Gaussian processes—0
Sparse within Sparse Gaussian Processes using Neighbor Information—0
Learning ODE Models with Qualitative Structure Using Gaussian ProcessesCode0
Pathwise Conditioning of Gaussian ProcessesCode1
Gaussian Processes with Skewed Laplace Spectral Mixture Kernels for Long-term Forecasting—0
Bayesian Nonparametric Dimensionality Reduction of Categorical Data for Predicting Severity of COVID-19 in Pregnant Women—0
Continuous surrogate-based optimization algorithms are well-suited for expensive discrete problems—0
Uncertainty Quantification of Darcy Flow through Porous Media using Deep Gaussian Process—0
Transforming Gaussian Processes With Normalizing FlowsCode1
Bayesian Variational Optimization for Combinatorial Spaces—0
Sample-efficient reinforcement learning using deep Gaussian processes—0
Learning in the Wild with Incremental Skeptical Gaussian ProcessesCode0
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian ProcessesCode0
Inter-domain Deep Gaussian Processes—0
Marginalised Gaussian Processes with Nested SamplingCode0
Matérn Gaussian Processes on Graphs—0
Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization—0
Hierarchical Gaussian Processes with Wasserstein-2 Kernels—0
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Benchmark Results

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