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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 811–820 of 1963 papers

TitleStatusHype
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian ProcessesCode0
Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes—0
Dual Parameterization of Sparse Variational Gaussian ProcessesCode0
Empirical analysis of representation learning and exploration in neural kernel banditsCode0
Rate of Convergence of Polynomial Networks to Gaussian Processes—0
Scalable mixed-domain Gaussian process modeling and model reduction for longitudinal dataCode0
Spatio-Temporal Variational Gaussian ProcessesCode1
Bayes-Newton Methods for Approximate Bayesian Inference with PSD GuaranteesCode1
End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian Optimization—0
Bayesian optimization of distributed neurodynamical controller models for spatial navigation—0
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Benchmark Results

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