6 citations · 12 across the 13 of their papers we have counts for
15 papers
Deep Single-Index Fréchet Regression
Muqing Cui, Yidong Zhou, Su I Iao +1
Predicting outputs that are located in non-Euclidean spaces, such as probability distributions, networks, and symmetric positive-definite matrices, is becoming increasingly importa…
Sensitivity Analysis when Generalizing Causal Effects from Multiple Studies to a Target Population: Motivation from the ECHO Program
Bolun Liu, Trang Quynh Nguyen, Elizabeth A. Stuart +22
Unobserved effect modifiers can induce bias when generalizing causal effect estimates to target populations. In this work, we extend a sensitivity analysis framework assessing the…
Fréchet Geodesic Boosting
Yidong Zhou, Su I Iao, Hans-Georg Müller
Gradient boosting has become a cornerstone of machine learning, enabling base learners such as decision trees to achieve exceptional predictive performance. While existing algorith…
End-to-End Deep Learning for Predicting Metric Space-Valued Outputs
Yidong Zhou, Su I Iao, Hans-Georg Müller
Many modern applications involve predicting structured, non-Euclidean outputs such as probability distributions, networks, and symmetric positive-definite matrices. These outputs a…
Regression Discontinuity Designs for Functional Data and Random Objects in Geodesic Spaces
Daisuke Kurisu, Yidong Zhou, Taisuke Otsu +1
Regression discontinuity designs (RDDs) are widely used for causal inference in observational studies with cutoff-based treatment assignment, primarily for Euclidean outcomes. We p…
Wasserstein Transfer Learning
Kaicheng Zhang, Sinian Zhang, Doudou Zhou +1
Transfer learning is a powerful paradigm for leveraging knowledge from source domains to enhance learning in a target domain. However, traditional transfer learning approaches ofte…
Geodesic Synthetic Control Methods for Random Objects and Functional Data
Daisuke Kurisu, Yidong Zhou, Taisuke Otsu +1
We introduce a geodesic synthetic control method for causal inference with panel outcomes that are random objects in a geodesic metric space. Examples include distributions, compos…
Geodesic Difference-in-Differences
Yidong Zhou, Daisuke Kurisu, Taisuke Otsu +1
Difference-in-differences (DID) is a widely used quasi-experimental design for causal inference, traditionally applied to scalar or Euclidean outcomes, while extensions to outcomes…
Deep Fréchet Regression
Su I Iao, Yidong Zhou, Hans-Georg Müller
Advancements in modern science have led to the increasing availability of non-Euclidean data in metric spaces. This paper addresses the challenge of modeling relationships between…
Geodesic Causal Inference
Daisuke Kurisu, Yidong Zhou, Taisuke Otsu +1
Adjusting for confounding and imbalance when establishing statistical relationships is an increasingly important task, and causal inference methods have emerged as the most popular…
Wasserstein Regression with Empirical Measures and Density Estimation for Sparse Data
Yidong Zhou, Hans-Georg Müller
The problem of modeling the relationship between univariate distributions and one or more explanatory variables has found increasing interest. Traditional functional data methods c…
Sliced Wasserstein Regression
Han Chen, Yidong Zhou, Hans-Georg Müller
While statistical modeling of distributional data has gained increased attention, the case of multivariate distributions has been somewhat neglected despite its relevance in variou…
Dynamic Modeling of Sparse Longitudinal Data and Functional Snippets With Stochastic Differential Equations
Yidong Zhou, Hans-Georg Müller
Sparse functional/longitudinal data have attracted widespread interest due to the prevalence of such data in social and life sciences. A prominent scenario where such data are rout…
Learning delay dynamics for multivariate stochastic processes, with application to the prediction of the growth rate of COVID-19 cases in the United States
Paromita Dubey, Yaqing Chen, Álvaro Gajardo +5
Delay differential equations form the underpinning of many complex dynamical systems. The forward problem of solving random differential equations with delay has received increasin…
Network Regression with Graph Laplacians
Yidong Zhou, Hans-Georg Müller
Network data are increasingly available in various research fields, motivating statistical analysis for populations of networks where a network as a whole is viewed as a data point…