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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 1601–1610 of 1963 papers

TitleStatusHype
Neural Generative Models for Global Optimization with Gradients—0
Variational Learning on Aggregate Outputs with Gaussian ProcessesCode0
Heterogeneous Multi-output Gaussian Process PredictionCode0
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesCode0
Index Set Fourier Series Features for Approximating Multi-dimensional Periodic Kernels—0
Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian ProcessesCode0
Bayesian active learning for choice models with deep Gaussian processes—0
Gaussian Process Behaviour in Wide Deep Neural NetworksCode0
Convergence and Concentration of Empirical Measures under Wasserstein Distance in Unbounded Functional Spaces—0
Efficiently Learning Nonstationary Gaussian Processes for Real World Impact—0
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Benchmark Results

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