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 1461–1470 of 1963 papers

TitleStatusHype
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Exact Gaussian Processes on a Million Data PointsCode0
High-Dimensional Bernoulli Autoregressive Process with Long-Range Dependence—0
Deep Gaussian Processes for Multi-fidelity ModelingCode0
Pairwise Comparisons with Flexible Time-DynamicsCode0
Functional Variational Bayesian Neural NetworksCode0
Financial Applications of Gaussian Processes and Bayesian Optimization—0
Learning Gaussian Policies from Corrective Human Feedback—0
Scalable Grouped Gaussian Processes via Direct Cholesky Functional Representations—0
Active learning for enumerating local minima based on Gaussian process derivatives—0
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Benchmark Results

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