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 1326–1350 of 1963 papers

TitleStatusHype
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
On the relationship between multitask neural networks and multitask Gaussian Processes—0
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax—0
lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal dataCode0
Warped Input Gaussian Processes for Time Series ForecastingCode0
Scalable Bayesian Preference Learning for CrowdsCode0
Numerical Gaussian process Kalman filtering—0
Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes—0
Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes—0
Safety Guarantees for Planning Based on Iterative Gaussian Processes—0
A Multilayered Block Network Model to Forecast Large Dynamic Transportation Graphs: an Application to US Air Transport—0
Efficient Approximate Inference with Walsh-Hadamard Variational Inference—0
Machine Learning for a Low-cost Air Pollution Network—0
Learning of Weighted Multi-layer Networks via Dynamic Social Spaces, with Application to Financial Interbank TransactionsCode0
Fleet Control using Coregionalized Gaussian Process Policy IterationCode0
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