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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 331–340 of 1963 papers

TitleStatusHype
Assessing Quality Estimation Models for Sentence-Level Prediction—0
A spectrum of physics-informed Gaussian processes for regression in engineering—0
Active Learning for Regression with Aggregated Outputs—0
A Sparse Gaussian Process Framework for Photometric Redshift Estimation—0
A Sparse Expansion For Deep Gaussian Processes—0
A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options—0
A Bulirsch-Stoer algorithm using Gaussian processes—0
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies—0
Correlated Product of Experts for Sparse Gaussian Process Regression—0
SBI: A Simulation-Based Test of Identifiability for Bayesian Causal Inference—0
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Benchmark Results

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