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 1421–1430 of 1963 papers

TitleStatusHype
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control—0
Streaming Variational Monte CarloCode0
CAiRE\_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
Bayesian Deconditional Kernel Mean Embeddings—0
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes—0
Neural Likelihoods for Multi-Output Gaussian Processes—0
Deep Bayesian Optimization on Attributed GraphsCode0
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes—0
Monotonic Gaussian Process FlowCode0
Non-linear Multitask Learning with Deep Gaussian Processes—0
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Benchmark Results

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