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

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Showing 301–325 of 1963 papers

TitleStatusHype
A General Framework for Fair Regression—0
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes—0
Bayesian Deconditional Kernel Mean Embeddings—0
Controller Adaptation via Learning Solutions of Contextual Bayesian Optimization—0
Correcting Model Bias with Sparse Implicit Processes—0
Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots—0
A Three Spatial Dimension Wave Latent Force Model for Describing Excitation Sources and Electric Potentials Produced by Deep Brain Stimulation—0
A Gaussian Process Regression Model for Distribution Inputs—0
A temporal model of text periodicities using Gaussian Processes—0
A Taylor Series Approach to Correction of Input Errors in Gaussian Process Regression—0
A Gaussian Process Regression based Dynamical Models Learning Algorithm for Target Tracking—0
Active learning of neural response functions with Gaussian processes—0
Asynchronous Distributed Variational Gaussian Processes for Regression—0
Asymmetric kernel in Gaussian Processes for learning target variance—0
A Gaussian Process perspective on Convolutional Neural Networks—0
Correlated Dynamics in Marketing Sensitivities—0
Optimal Privacy-Aware Stochastic Sampling—0
Accelerating ABC methods using Gaussian processes—0
Convergence Rates of Constrained Expected Improvement—0
Contextual Combinatorial Multi-output GP Bandits with Group Constraints—0
A Statistical Machine Learning Approach to Yield Curve Forecasting—0
A Gaussian Process Model for Ordinal Data with Applications to Chemoinformatics—0
Active Learning of Linear Embeddings for Gaussian Processes—0
Associative embeddings for large-scale knowledge transfer with self-assessment—0
Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression—0
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Benchmark Results

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