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Found 13 papers, 2 papers with code
Gaussian process aided function comparison using noisy scattered data
This work proposes a new nonparametric method to compare the underlying mean functions given by two noisy datasets.
Methodology Applications
Differentially Private Change-Point Detection
The change-point detection problem seeks to identify distributional changes at an unknown change-point k* in a stream of data.
Uncertainty Quantification for Bayesian Optimization
Bayesian optimization is a class of global optimization techniques.
Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features
Despite their success, kernel methods suffer from a massive computational cost in practice.
Projection Pursuit Gaussian Process Regression
We show that dimension expansion can help approximate more complex functions.
High-Dimensional Non-Parametric Density Estimation in Mixed Smooth Sobolev Spaces
Density estimation plays a key role in many tasks in machine learning, statistical inference, and visualization.
The temporal overfitting problem with applications in wind power curve modeling
Using existing model selection methods, like cross validation, results in model overfitting in presence of temporal autocorrelation.
High-Dimensional Simulation Optimization via Brownian Fields and Sparse Grids
High-dimensional simulation optimization is notoriously challenging.
A Sparse Expansion For Deep Gaussian Processes
In this work, we use Deep Gaussian Processes (DGPs) as statistical surrogates for stochastic processes with complex distributions.
Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations
We develop an exact and scalable algorithm for one-dimensional Gaussian process regression with Mat\'ern correlations whose smoothness parameter $\nu$ is a half-integer.
Renewing Iterative Self-labeling Domain Adaptation with Application to the Spine Motion Prediction
The area of transfer learning comprises supervised machine learning methods that cope with the issue when the training and testing data have different input feature spaces or distributions.
Privacy-aware Gaussian Process Regression
The key idea of the proposed method is to add synthetic noise to the data until the predictive variance of the Gaussian process model reaches a prespecified privacy level.
A General Theory for Kernel Packets: from state space model to compactly supported basis
It is well known that the state space (SS) model formulation of a Gaussian process (GP) can lower its training and prediction time both to $\CalO(n)$ for $n$ data points.
