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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 31–40 of 1963 papers

TitleStatusHype
Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations—0
STRIDE: Sparse Techniques for Regression in Deep Gaussian Processes—0
Convergence Rates of Constrained Expected Improvement—0
A Fast Kernel-based Conditional Independence test with Application to Causal Discovery—0
Fairness-aware Bayes optimal functional classification—0
Probabilistic Wind Power Forecasting via Non-Stationary Gaussian Processes—0
Identifying Causal Direction via Variational Bayesian Compression—0
Improving Random Forests by Smoothing—0
The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning—0
A Practitioner's Guide to Automatic Kernel Search for Gaussian Processes in Battery Applications—0
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Benchmark Results

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