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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 1076–1100 of 1963 papers

TitleStatusHype
Wide Neural Networks as Gaussian Processes: Lessons from Deep Equilibrium Models—0
Wide neural networks: From non-gaussian random fields at initialization to the NTK geometry of training—0
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent—0
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes—0
Wiener Chaos in Kernel Regression: Towards Untangling Aleatoric and Epistemic Uncertainty—0
Wilsonian Renormalization of Neural Network Gaussian Processes—0
Bayesian Optimization using Deep Gaussian Processes—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
Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models—0
Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes—0
Experimental Data-Driven Model Predictive Control of a Hospital HVAC System During Regular Use—0
Experimentally implemented dynamic optogenetic optimization of ATPase expression using knowledge-based and Gaussian-process-supported models—0
Experiment Design with Gaussian Process Regression with Applications to Chance-Constrained Control—0
Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classification—0
Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration—0
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Benchmark Results

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