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 131–140 of 1963 papers

TitleStatusHype
Physics-informed Gaussian Processes as Linear Model Predictive Controller—0
FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear Systems under Uncertain DisturbancesCode0
L4acados: Learning-based models for acados, applied to Gaussian process-based predictive controlCode2
Privacy Preserving Federated Unsupervised Domain Adaptation with Application to Age Prediction from DNA Methylation DataCode0
A Generalized Unified Skew-Normal Process with Neural Bayes Inference—0
Robust Bayesian Optimization via Localized Online Conformal PredictionCode0
Gaussian Process Priors for Boundary Value Problems of Linear Partial Differential EquationsCode0
Sparsifying Suprema of Gaussian Processes—0
Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems—0
Constructing Gaussian Processes via Samplets—0
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Benchmark Results

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