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 451–475 of 1963 papers

TitleStatusHype
Uncertainty Informed Optimal Resource Allocation with Gaussian Process based Bayesian Inference—0
Spatiotemporal Besov Priors for Bayesian Inverse Problems—0
Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems—0
SEAL: Simultaneous Exploration and Localization in Multi-Robot SystemsCode1
Sampling from Gaussian Process Posteriors using Stochastic Gradient DescentCode1
A Bayesian Take on Gaussian Process NetworksCode0
Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural NetworksCode0
Spatio-temporal DeepKriging for Interpolation and Probabilistic Forecasting—0
Time-Varying Transition Matrices with Multi-task Gaussian Processes—0
Amortized Inference for Gaussian Process Hyperparameters of Structured KernelsCode0
Functional Causal Bayesian Optimization—0
Monte Carlo inference for semiparametric Bayesian regression—0
Representing and Learning Functions Invariant Under Crystallographic Groups—0
Training-Free Neural Active Learning with Initialization-Robustness GuaranteesCode0
Memory-Based Dual Gaussian Processes for Sequential LearningCode1
Graph Classification Gaussian Processes via Spectral Features—0
Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processes—0
Global universal approximation of functional input maps on weighted spacesCode0
Constrained Causal Bayesian OptimizationCode1
Taylorformer: Probabilistic Modelling for Random Processes including Time SeriesCode0
Graph Neural Processes for Spatio-Temporal ExtrapolationCode1
A Learning-based Nonlinear Model Predictive Controller for a Real Go-Kart based on Black-box Dynamics Modeling through Gaussian Processes—0
On Neural Networks as Infinite Tree-Structured Probabilistic Graphical ModelsCode0
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks—0
Gaussian Processes with State-Dependent Noise for Stochastic Control—0
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Benchmark Results

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