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 1411–1420 of 1963 papers

TitleStatusHype
A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot Interaction—0
SBI: A Simulation-Based Test of Identifiability for Bayesian Causal Inference—0
A Sparse Expansion For Deep Gaussian Processes—0
A Sparse Gaussian Process Framework for Photometric Redshift Estimation—0
A spectrum of physics-informed Gaussian processes for regression in engineering—0
Assessing Quality Estimation Models for Sentence-Level Prediction—0
Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression—0
Associative embeddings for large-scale knowledge transfer with self-assessment—0
A Statistical Machine Learning Approach to Yield Curve Forecasting—0
Optimal Privacy-Aware Stochastic Sampling—0
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Benchmark Results

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