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 726–750 of 1963 papers

TitleStatusHype
Gaussian Process Classification with Privileged Information by Soft-to-Hard Labeling Transfer—0
Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data—0
Gaussian Process Constraint Learning for Scalable Chance-Constrained Motion Planning from Demonstrations—0
Gaussian Process Convolutional Dictionary Learning—0
Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis—0
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences—0
Gaussian Processes and Reproducing Kernels: Connections and Equivalences—0
Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces—0
Gaussian Process on the Product of Directional Manifolds—0
A Perspective on Gaussian Processes for Earth Observation—0
Gaussian Processes for Analyzing Positioned Trajectories in Sports—0
Gaussian processes for Bayesian inverse problems associated with linear partial differential equations—0
Bayesian Variational Optimization for Combinatorial Spaces—0
Constrained Bayesian Optimization under Bivariate Gaussian Process with Application to Cure Process Optimization—0
Gaussian processes for dynamics learning in model predictive control—0
A computationally lightweight safe learning algorithm—0
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder—0
Gaussian Processes for Music Audio Modelling and Content Analysis—0
Gaussian Processes for Natural Language Processing—0
Gaussian Processes for Nonlinear Signal Processing—0
Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations—0
Gaussian Processes for Survival Analysis—0
Gaussian Processes for Traffic Speed Prediction at Different Aggregation Levels—0
Gaussian Processes indexed on the symmetric group: prediction and learning—0
Efficient Global Optimization using Deep Gaussian Processes—0
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Benchmark Results

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