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 1026–1050 of 1963 papers

TitleStatusHype
Bayesian Optimization Assisted Meal Bolus Decision Based on Gaussian Processes Learning and Risk-Sensitive Control—0
A Renormalization Group Approach to Connect Discrete- and Continuous-Time Descriptions of Gaussian Processes—0
Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control—0
Improved active output selection strategy for noisy environments—0
Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models—0
Structured Machine Learning Tools for Modelling Characteristics of Guided Waves—0
Using BART to Perform Pareto Optimization and Quantify its Uncertainties—0
Gauss-Legendre Features for Gaussian Process Regression—0
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning—0
Deep Ensemble Kernel Learning—0
Optimal Designs of Gaussian Processes with Budgets for Hyperparameter Optimization—0
Time Series Counterfactual Inference with Hidden Confounders—0
DAG-GPs: Learning Directed Acyclic Graph Structure For Multi-Output Gaussian Processes—0
Activation-level uncertainty in deep neural networks—0
Fast covariance parameter estimation of spatial Gaussian process models using neural networksCode0
A Tutorial on Sparse Gaussian Processes and Variational Inference—0
Point-Based Value Iteration and Approximately Optimal Dynamic Sensor Selection for Linear-Gaussian Processes—0
Learning Structures in Earth Observation Data with Gaussian Processes—0
Gaussian Process Regression constrained by Boundary Value Problems—0
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior—0
Parameter Identification for Digital Fabrication: A Gaussian Process Learning Approach—0
Active Learning for Deep Gaussian Process SurrogatesCode0
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian ProcessesCode1
Structured learning of rigid-body dynamics: A survey and unified view from a robotics perspective—0
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes—0
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Benchmark Results

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