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 751–775 of 1963 papers

TitleStatusHype
Adaptive Cholesky Gaussian ProcessesCode0
Invariance Learning in Deep Neural Networks with Differentiable Laplace ApproximationsCode1
Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces—0
A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes—0
Supervising the Multi-Fidelity Race of Hyperparameter ConfigurationsCode1
Nonstationary multi-output Gaussian processes via harmonizable spectral mixtures—0
A Statistical Learning View of Simple KrigingCode0
Fast Inverter Control by Learning the OPF Mapping using Sensitivity-Informed Gaussian Processes—0
The Schrödinger Bridge between Gaussian Measures has a Closed Form—0
Multi-model Ensemble Analysis with Neural Network Gaussian Processes—0
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning—0
Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes—0
Incorporating Sum Constraints into Multitask Gaussian ProcessesCode0
Variational Nearest Neighbor Gaussian Process—0
A Kernel-Based Approach for Modelling Gaussian Processes with Functional Information—0
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder—0
Online Time Series Anomaly Detection with State Space Gaussian Processes—0
A visual exploration of Gaussian Processes and Infinite Neural Networks—0
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—0
Modeling Human Driver Interactions Using an Infinite Policy Space Through Gaussian Processes—0
Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders—0
Sum-of-Squares Program and Safe Learning On Maximizing the Region of Attraction of Partially Unknown Systems—0
When are Iterative Gaussian Processes Reliably Accurate?Code0
How Infinitely Wide Neural Networks Can Benefit from Multi-task Learning -- an Exact Macroscopic CharacterizationCode0
Bayesian Optimization of Function NetworksCode1
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Benchmark Results

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