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 601–650 of 1963 papers

TitleStatusHype
Environmental Modeling Framework using Stacked Gaussian Processes—0
Epidemiological Model Calibration via Graybox Bayesian Optimization—0
Blitzkriging: Kronecker-structured Stochastic Gaussian Processes—0
Equivalence of Convergence Rates of Posterior Distributions and Bayes Estimators for Functions and Nonparametric Functionals—0
BOIS: Bayesian Optimization of Interconnected Systems—0
Estimating 2-Sinkhorn Divergence between Gaussian Processes from Finite-Dimensional Marginals—0
Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations—0
Estimating activity cycles with probabilistic methods II. The Mount Wilson Ca H&K data—0
BOP-Elites, a Bayesian Optimisation algorithm for Quality-Diversity search—0
Estimation of Riemannian distances between covariance operators and Gaussian processes—0
Branching Gaussian Processes with Applications to Spatiotemporal Reconstruction of 3D Trees—0
Evaluating Hospital Case Cost Prediction Models Using Azure Machine Learning Studio—0
BrowNNe: Brownian Nonlocal Neurons & Activation Functions—0
Gaussian process based nonlinear latent structure discovery in multivariate spike train data—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
Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces—0
Evolution of Covariance Functions for Gaussian Process Regression using Genetic Programming—0
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty—0
Application of machine learning to gas flaring—0
Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels—0
CAiRE\_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
Exact Simulation of Noncircular or Improper Complex-Valued Stationary Gaussian Processes using Circulant Embedding—0
Functional Causal Bayesian Optimization—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
Functional Gaussian processes for regression with linear PDE models—0
Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classification—0
Gaussian Process Accelerated Feldman-Cousins Approach for Physical Parameter Inference—0
Efficient modeling of sub-kilometer surface wind with Gaussian processes and neural networks—0
BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems—0
Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature—0
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning—0
A Driver Behavior Modeling Structure Based on Non-parametric Bayesian Stochastic Hybrid Architecture—0
Exponentially Stable Projector-based Control of Lagrangian Systems with Gaussian Processes—0
Extended and Unscented Gaussian Processes—0
Fully Scalable Gaussian Processes using Subspace Inducing Inputs—0
Functional Priors for Bayesian Neural Networks through Wasserstein Distance Minimization to Gaussian Processes—0
Extrinsic Bayesian Optimizations on Manifolds—0
Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes—0
Facility Deployment Decisions through Warp Optimizaton of Regressed Gaussian Processes—0
Fairness-aware Bayes optimal functional classification—0
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning—0
Fast Adaptation with Linearized Neural Networks—0
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes—0
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning—0
Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes—0
Gaussian processes for Bayesian inverse problems associated with linear partial differential equations—0
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Benchmark Results

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