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 1326–1350 of 1963 papers

TitleStatusHype
Intrinsic Gaussian processes on complex constrained domains—0
Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics—0
Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images—0
Introduction and Exemplars of Uncertainty Decomposition—0
Is SGD a Bayesian sampler? Well, almost—0
Joint Emotion Analysis via Multi-task Gaussian Processes—0
Joint Gaussian Processes for Biophysical Parameter Retrieval—0
A Joint introduction to Gaussian Processes and Relevance Vector Machines with Connections to Kalman filtering and other Kernel Smoothers—0
Causal Modeling of Policy Interventions From Sequences of Treatments and Outcomes—0
Kalman Filtering with Gaussian Processes Measurement Noise—0
Kernel Conditional Density Operators—0
Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness—0
Kernel Distillation for Fast Gaussian Processes Prediction—0
Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression—0
Knot Selection in Sparse Gaussian Processes—0
Know Thy Student: Interactive Learning with Gaussian Processes—0
Koopman-Equivariant Gaussian Processes—0
Kriging and Gaussian Process Interpolation for Georeferenced Data Augmentation—0
Kullback-Leibler and Renyi divergences in reproducing kernel Hilbert space and Gaussian process settings—0
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes—0
Large Scale Multi-Task Bayesian Optimization with Large Language Models—0
Large-width functional asymptotics for deep Gaussian neural networks—0
Latent Map Gaussian Processes for Mixed Variable Metamodeling—0
Latent Variable Double Gaussian Process Model for Decoding Complex Neural Data—0
Lateral land movement prediction from GNSS position time series in a machine learning aided algorithm—0
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Benchmark Results

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