6 papers
Functional Causal Discovery via Conditional Covariance Ordering
Keyu Li, Ruoxu Tan
We study causal discovery where each node is a random function. Previous studies on this topic rely on structural assumptions, e.g., linearity or non-linearity, and distributional…
High-dimensional Semi-supervised Classification via the Fermat Distance
Ruoxu Tan, Yiming Zang
Semi-supervised classification, where unlabeled data are massive but labeled data are limited, often arises in machine learning applications. We address this challenge under high-d…
Semi-supervised Classification for Noisy Functional Data with Application to Astronomical Spectra
Ruoxu Tan, Mingjie Jian, Yiming Zang
Despite its extensive development for multivariate data, semi-supervised learning remains underdeveloped for functional data, especially under discrete and noisy observations. We d…
Supervised Manifold Learning for Functional Data
Ruoxu Tan, Yiming Zang
Classification is a core topic in functional data analysis. A large number of functional classifiers have been proposed in the literature, most of which are based on functional pri…
Delaunay Weighted Two-sample Test for High-dimensional Data by Incorporating Geometric Information
Jiaqi Gu, Ruoxu Tan, Guosheng Yin
Two-sample hypothesis testing is a fundamental problem with various applications, which faces new challenges in the high-dimensional context. To mitigate the issue of the curse of…
Causal Effect of Functional Treatment
Ruoxu Tan, Wei Huang, Zheng Zhang +1
We study the causal effect with a functional treatment variable, where practical applications often arise in neuroscience, biomedical sciences, etc. Previous research concerning th…