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 381–390 of 1963 papers

TitleStatusHype
Chained Gaussian ProcessesCode0
Embarrassingly Parallel Inference for Gaussian ProcessesCode0
EigenGP: Gaussian Process Models with Adaptive EigenfunctionsCode0
Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector CalculusCode0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Calibrating Deep Convolutional Gaussian ProcessesCode0
Efficient Modeling of Latent Information in Supervised Learning using Gaussian ProcessesCode0
Calibrated Computation-Aware Gaussian ProcessesCode0
Adversarial Robustness Guarantees for Random Deep Neural NetworksCode0
The Debiased Spatial Whittle LikelihoodCode0
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Benchmark Results

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