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 1351–1360 of 1963 papers

TitleStatusHype
Recovering BanditsCode0
Function-Space Distributions over KernelsCode0
Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation—0
Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei—0
Implicit Posterior Variational Inference for Deep Gaussian ProcessesCode0
We Know Where We Don't Know: 3D Bayesian CNNs for Credible Geometric UncertaintyCode0
Approximate Sampling using an Accelerated Metropolis-Hastings based on Bayesian Optimization and Gaussian Processes—0
Interpretable User Models via Decision-rule Gaussian Processes: Preliminary Results on Energy Storage—0
Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes—0
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation—0
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Benchmark Results

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