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 1091–1100 of 1963 papers

TitleStatusHype
Transforming Gaussian Processes With Normalizing FlowsCode1
Bayesian Variational Optimization for Combinatorial Spaces—0
Sample-efficient reinforcement learning using deep Gaussian processes—0
Learning in the Wild with Incremental Skeptical Gaussian ProcessesCode0
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian ProcessesCode0
Inter-domain Deep Gaussian Processes—0
Marginalised Gaussian Processes with Nested SamplingCode0
Matérn Gaussian Processes on Graphs—0
Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization—0
Hierarchical Gaussian Processes with Wasserstein-2 Kernels—0
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Benchmark Results

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