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 591–600 of 1963 papers

TitleStatusHype
How Good are Low-Rank Approximations in Gaussian Process Regression?Code0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Implementation and Analysis of GPU Algorithms for Vecchia ApproximationCode0
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesCode0
Bias-Free Scalable Gaussian Processes via Randomized TruncationsCode0
Federated Causal Inference from Observational DataCode0
From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior ApproximationCode0
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite KernelCode0
Entropic Trace Estimates for Log DeterminantsCode0
Gaussian Process Behaviour in Wide Deep Neural NetworksCode0
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Benchmark Results

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