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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 661–670 of 1963 papers

TitleStatusHype
Adaptive RKHS Fourier Features for Compositional Gaussian Process ModelsCode0
Multi-output Gaussian Processes for Uncertainty-aware Recommender SystemsCode0
Federated Causal Inference from Observational DataCode0
Bayesian Learning-Based Adaptive Control for Safety Critical SystemsCode0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Multi-task Learning for Aggregated Data using Gaussian ProcessesCode0
Few-Shot Speech Deepfake Detection Adaptation with Gaussian ProcessesCode0
Near-Optimal Active Learning of Multi-Output Gaussian ProcessesCode0
FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear Systems under Uncertain DisturbancesCode0
Global Safe Sequential Learning via Efficient Knowledge TransferCode0
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Benchmark Results

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