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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 1126–1150 of 1963 papers

TitleStatusHype
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
All You Need is a Good Functional Prior for Bayesian Deep Learning—0
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
Gaussian Processes for Traffic Speed Prediction at Different Aggregation Levels—0
Gaussian Process Latent Variable Flows for Massively Missing Data—0
Neural Networks as Inter-Domain Inducing Points—0
Neural Linear Models with Functional Gaussian Process Priors—0
Functional Priors for Bayesian Neural Networks through Wasserstein Distance Minimization to Gaussian Processes—0
Decoupled Sparse Gaussian Processes Components]Decoupled Sparse Gaussian Processes Components : Separating Decision Making from Data Manifold Fitting—0
The Gaussian Process Latent Autoregressive Model—0
Model-based Reinforcement Learning for Continuous Control with Posterior SamplingCode0
The Impact of Data on the Stability of Learning-Based Control- Extended Version—0
Design of Experiments for Verifying Biomolecular Networks—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
Learning ODE Models with Qualitative Structure Using Gaussian ProcessesCode0
Sparse within Sparse Gaussian Processes using Neighbor Information—0
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
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Benchmark Results

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