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 1341–1350 of 1963 papers

TitleStatusHype
A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process ModelCode0
Incremental Learning of Motion Primitives for Pedestrian Trajectory Prediction at Intersections—0
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness—0
Online learning-based Model Predictive Control with Gaussian Process Models and Stability Guarantees—0
GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models—0
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO—0
Modelling Uncertainty in Collaborative Document Quality Assessment—0
Statistical Model Aggregation via Parameter MatchingCode0
Continual Multi-task Gaussian ProcessesCode0
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Benchmark Results

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