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 981–990 of 1963 papers

TitleStatusHype
Rate of Convergence of Polynomial Networks to Gaussian Processes—0
Reactive Multi-Robot Navigation in Outdoor Environments Through Uncertainty-Aware Active Learning of Human Preference Landscape—0
Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models—0
Real-Time Regression with Dividing Local Gaussian Processes—0
Reasoning about Probabilities in Dynamic Systems using Goal Regression—0
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes—0
Reconstructing the Hubble parameter with future Gravitational Wave missions using Machine Learning—0
The Schrödinger Bridge between Gaussian Measures has a Closed Form—0
Rectified Pessimistic-Optimistic Learning for Stochastic Continuum-armed Bandit with Constraints—0
Recurrent Memory for Online Interdomain Gaussian Processes—0
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Benchmark Results

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