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 1741–1750 of 1963 papers

TitleStatusHype
Optimal Detection of Faulty Traffic Sensors Used in Route Planning—0
Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning—0
A Gaussian Process Regression Model for Distribution Inputs—0
Multi-view Regularized Gaussian Processes—0
Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection—0
Machine Learning of Linear Differential Equations using Gaussian ProcessesCode0
Overlapping Cover Local Regression Machines—0
Bayesian model selection consistency and oracle inequality with intractable marginal likelihood—0
Lazily Adapted Constant Kinky Inference for Nonparametric Regression and Model-Reference Adaptive Control—0
Monte Carlo Structured SVI for Two-Level Non-Conjugate Models—0
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Benchmark Results

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