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 991–1000 of 1963 papers

TitleStatusHype
Kernel Interpolation for Scalable Online Gaussian ProcessesCode1
Hierarchical Inducing Point Gaussian Process for Inter-domain ObservationsCode0
Similarity measure for sparse time course data based on Gaussian processesCode0
The Promises and Pitfalls of Deep Kernel Learning—0
SBI: A Simulation-Based Test of Identifiability for Bayesian Causal Inference—0
On Feature Collapse and Deep Kernel Learning for Single Forward Pass UncertaintyCode1
Large-width functional asymptotics for deep Gaussian neural networks—0
Output-Weighted Sampling for Multi-Armed Bandits with Extreme PayoffsCode0
Non-asymptotic approximations of neural networks by Gaussian processes—0
Using Distance Correlation for Efficient Bayesian Optimization—0
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Benchmark Results

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