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 1151–1160 of 1963 papers

TitleStatusHype
Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking—0
Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization—0
String and Membrane Gaussian Processes—0
String Gaussian Process Kernels—0
Structure and Distribution Metric for Quantifying the Quality of Uncertainty: Assessing Gaussian Processes, Deep Neural Nets, and Deep Neural Operators for Regression—0
Structure-Aware Random Fourier Kernel for Graphs—0
Structured learning of rigid-body dynamics: A survey and unified view from a robotics perspective—0
Structured Machine Learning Tools for Modelling Characteristics of Guided Waves—0
Structured Variational Inference for Coupled Gaussian Processes—0
Structure-Preserving Learning Using Gaussian Processes and Variational Integrators—0
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Benchmark Results

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