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 576–600 of 1963 papers

TitleStatusHype
A spectrum of physics-informed Gaussian processes for regression in engineering—0
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian ManifoldsCode0
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes—0
Convolutional Deep Kernel MachinesCode0
Data-driven Modeling and Inference for Bayesian Gaussian Process ODEs via Double Normalizing FlowsCode0
Modelling Irrational Behaviour of Residential End Users using Non-Stationary Gaussian Processes—0
Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple KernelCode0
On Distributed and Asynchronous Sampling of Gaussian Processes for Sequential Binary Hypothesis Testing—0
Scalable Model-Based Gaussian Process Clustering—0
Promises of Deep Kernel Learning for Control Synthesis—0
Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables—0
Data-driven Bayesian Control of Port-Hamiltonian Systems—0
A computationally lightweight safe learning algorithm—0
Distributionally Robust Model-based Reinforcement Learning with Large State Spaces—0
Les Houches Lectures on Deep Learning at Large & Infinite Width—0
Latent Variable Multi-output Gaussian Processes for Hierarchical DatasetsCode0
Heterogeneous Multi-Task Gaussian Cox ProcessesCode0
Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes—0
Improve in-situ life prediction and classification performance by capturing both the present state and evolution rate of battery aging—0
Federated Causal Inference from Observational DataCode0
Fast Risk Assessment in Power Grids through Novel Gaussian Process and Active Learning—0
Gaussian Process Regression for Maximum Entropy Distribution—0
Emerging Statistical Machine Learning Techniques for Extreme Temperature Forecasting in U.S. Cities—0
Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy—0
Current Methods for Drug Property Prediction in the Real World—0
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Benchmark Results

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