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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 701–750 of 1963 papers

TitleStatusHype
Functional Causal Bayesian Optimization—0
Functional Gaussian processes for regression with linear PDE models—0
Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective—0
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models—0
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference—0
A Bulirsch-Stoer algorithm using Gaussian processes—0
A Sparse Gaussian Process Framework for Photometric Redshift Estimation—0
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes—0
Fusion of Gaussian Processes Predictions with Monte Carlo Sampling—0
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes—0
Symbolic Regression on Sparse and Noisy Data with Gaussian Processes—0
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes—0
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation—0
Efficiently Learning Nonstationary Gaussian Processes for Real World Impact—0
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction—0
Gaussian Control Barrier Functions : A Non-Parametric Paradigm to Safety—0
Gaussian-Dirichlet Random Fields for Inference over High Dimensional Categorical Observations—0
Gaussian Experts Selection using Graphical Models—0
aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture—0
Gaussian Mixture Marginal Distributions for Modelling Remaining Pipe Wall Thickness of Critical Water Mains in Non-Destructive Evaluation—0
Gaussian Process Optimization with Mutual Information—0
Gaussian-Process-based Adaptive Tracking Control with Dynamic Active Learning for Autonomous Ground Vehicles—0
Gaussian process based nonlinear latent structure discovery in multivariate spike train data—0
Gaussian Process-Based Nonlinear Moving Horizon Estimation—0
Bayesian Warped Gaussian Processes—0
Gaussian Process Classification with Privileged Information by Soft-to-Hard Labeling Transfer—0
Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data—0
Gaussian Process Constraint Learning for Scalable Chance-Constrained Motion Planning from Demonstrations—0
Gaussian Process Convolutional Dictionary Learning—0
Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis—0
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences—0
Gaussian Processes and Reproducing Kernels: Connections and Equivalences—0
Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces—0
Gaussian Process on the Product of Directional Manifolds—0
A Perspective on Gaussian Processes for Earth Observation—0
Gaussian Processes for Analyzing Positioned Trajectories in Sports—0
Gaussian processes for Bayesian inverse problems associated with linear partial differential equations—0
Bayesian Variational Optimization for Combinatorial Spaces—0
Constrained Bayesian Optimization under Bivariate Gaussian Process with Application to Cure Process Optimization—0
Gaussian processes for dynamics learning in model predictive control—0
A computationally lightweight safe learning algorithm—0
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder—0
Gaussian Processes for Music Audio Modelling and Content Analysis—0
Gaussian Processes for Natural Language Processing—0
Gaussian Processes for Nonlinear Signal Processing—0
Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations—0
Gaussian Processes for Survival Analysis—0
Gaussian Processes for Traffic Speed Prediction at Different Aggregation Levels—0
Gaussian Processes indexed on the symmetric group: prediction and learning—0
Efficient Global Optimization using Deep Gaussian Processes—0
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Benchmark Results

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