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 1711–1720 of 1963 papers

TitleStatusHype
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation—0
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive ControlCode0
Learning to Detect Sepsis with a Multitask Gaussian Process RNN ClassifierCode0
Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian ProcessesCode0
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes—0
Multiple Kernel Learning and Automatic Subspace Relevance Determination for High-dimensional Neuroimaging Data—0
Large Linear Multi-output Gaussian Process LearningCode0
Efficient Modeling of Latent Information in Supervised Learning using Gaussian ProcessesCode0
Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processesCode0
Doubly Stochastic Variational Inference for Deep Gaussian ProcessesCode0
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Benchmark Results

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