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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 801–810 of 1963 papers

TitleStatusHype
The Fixed-b Limiting Distribution and the ERP of HAR Tests Under Nonstationarity—0
State-space deep Gaussian processes with applicationsCode1
Improved Inverse-Free Variational Bounds for Sparse Gaussian Processes—0
Transfer Learning with Gaussian Processes for Bayesian OptimizationCode0
Positional Encoder Graph Neural Networks for Geographic DataCode1
Temporal Knowledge Graph Embedding based on Multivariate Gaussian Process—0
Accounting for Gaussian Process Imprecision in Bayesian OptimizationCode0
Non-separable Spatio-temporal Graph Kernels via SPDEs—0
Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes—0
Optimizing Bayesian acquisition functions in Gaussian Processes—0
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Benchmark Results

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