6 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…
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…
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…
Measure Selection for Functional Linear Model
Su I Iao, Hans-Georg Müller
Advancements in modern science have led to an increased prevalence of functional data, which are usually viewed as elements of the space of square-integrable functions . Core…
Inference for Dispersion and Curvature of Random Objects
Wookyeong Song, Hans-Georg Müller
There are many open questions pertaining to the statistical analysis of random objects, which are increasingly encountered. A major challenge is the absence of linear operations in…
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…