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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 911–920 of 1963 papers

TitleStatusHype
Last Layer Marginal Likelihood for Invariance LearningCode0
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian ProcessesCode1
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process PerspectiveCode0
Measuring the robustness of Gaussian processes to kernel choice—0
Learning Nonparametric Volterra Kernels with Gaussian ProcessesCode0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
Scalable Variational Gaussian Processes via Harmonic Kernel DecompositionCode1
Probabilistic Forecasting of Imbalance Prices in the Belgian Context—0
Multi-output Gaussian Processes for Uncertainty-aware Recommender SystemsCode0
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs—0
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Benchmark Results

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