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 1676–1700 of 1963 papers

TitleStatusHype
Emerging Statistical Machine Learning Techniques for Extreme Temperature Forecasting in U.S. Cities—0
Emulating dynamic non-linear simulators using Gaussian processes—0
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)—0
Meta-models for transfer learning in source localisation—0
End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian Optimization—0
Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization—0
Enhancing Mean-Reverting Time Series Prediction with Gaussian Processes: Functional and Augmented Data Structures in Financial Forecasting—0
Enhancing RSS-Based Visible Light Positioning by Optimal Calibrating the LED Tilt and Gain—0
Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning—0
Ensemble Multi-task Gaussian Process Regression with Multiple Latent Processes—0
Entropic regularization of Wasserstein distance between infinite-dimensional Gaussian measures and Gaussian processes—0
Entropy of Overcomplete Kernel Dictionaries—0
Environmental Modeling Framework using Stacked Gaussian Processes—0
Epidemiological Model Calibration via Graybox Bayesian Optimization—0
Equivalence of Convergence Rates of Posterior Distributions and Bayes Estimators for Functions and Nonparametric Functionals—0
Estimating 2-Sinkhorn Divergence between Gaussian Processes from Finite-Dimensional Marginals—0
Estimating activity cycles with probabilistic methods II. The Mount Wilson Ca H&K data—0
Estimation of Riemannian distances between covariance operators and Gaussian processes—0
Evaluating Hospital Case Cost Prediction Models Using Azure Machine Learning Studio—0
Evaluation of Deep Gaussian Processes for Text Classification—0
Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems—0
Evaluation of Rarity of Fingerprints in Forensics—0
Evolution of Covariance Functions for Gaussian Process Regression using Genetic Programming—0
Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels—0
Exact Simulation of Noncircular or Improper Complex-Valued Stationary Gaussian Processes using Circulant Embedding—0
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Benchmark Results

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