30 citations · 119 across the 39 of their papers we have counts for
15 papers · 1 filter
Gaussian Process Subspace Regression for Model Reduction
Ruda Zhang, Simon Mak, David Dunson
Subspace-valued functions arise in a wide range of problems, including parametric reduced order modeling (PROM). In PROM, each parameter point can be associated with a subspace, wh…
Statistical Guarantees for Transformation Based Models with Applications to Implicit Variational Inference
Sean Plummer, Shuang Zhou, Anirban Bhattacharya +2
Transformation-based methods have been an attractive approach in non-parametric inference for problems such as unconditional and conditional density estimation due to their unique…
Targeted Random Projection for Prediction from High-Dimensional Features
Minerva Mukhopadhyay, David B. Dunson
We consider the problem of computationally-efficient prediction with high dimensional and highly correlated predictors when accurate variable selection is effectively impossible. D…
Geodesic Distance Estimation with Spherelets
Didong Li, David B Dunson
Many statistical and machine learning approaches rely on pairwise distances between data points. The choice of distance metric has a fundamental impact on performance of these proc…
Consistent Entropy Estimation for Stationary Time Series
Alexander L Young, David B Dunson
Entropy estimation, due in part to its connection with mutual information, has seen considerable use in the study of time series data including causality detection and information…
Random orthogonal matrices and the Cayley transform
Michael Jauch, Peter D. Hoff, David B. Dunson
Random orthogonal matrices play an important role in probability and statistics, arising in multivariate analysis, directional statistics, and models of physical systems, among oth…