109 citations · 353 across the 25 of their papers we have counts for
6 papers · 1 filter
Causal Discovery by Kernel Deviance Measures with Heterogeneous Transforms
Tim Tse, Zhitang Chen, Shengyu Zhu +1
The discovery of causal relationships in a set of random variables is a fundamental objective of science and has also recently been argued as being an essential component towards r…
Reframed GES with a Neural Conditional Dependence Measure
Xinwei Shen, Shengyu Zhu, Jiji Zhang +2
In a nonparametric setting, the causal structure is often identifiable only up to Markov equivalence, and for the purpose of causal inference, it is useful to learn a graphical rep…
Out-of-distribution Generalization with Causal Invariant Transformations
Ruoyu Wang, Mingyang Yi, Zhitang Chen +1
In real-world applications, it is important and desirable to learn a model that performs well on out-of-distribution (OOD) data. Recently, causality has become a powerful tool to t…
A Local Method for Identifying Causal Relations under Markov Equivalence
Zhuangyan Fang, Yue Liu, Zhi Geng +2
Causality is important for designing interpretable and robust methods in artificial intelligence research. We propose a local approach to identify whether a variable is a cause of…
Exponentially Consistent Kernel Two-Sample Tests
Shengyu Zhu, Biao Chen, Zhitang Chen
Given two sets of independent samples from unknown distributions and , a two-sample test decides whether to reject the null hypothesis that . Recent attention has focus…
Universal Hypothesis Testing with Kernels: Asymptotically Optimal Tests for Goodness of Fit
Shengyu Zhu, Biao Chen, Pengfei Yang +1
We characterize the asymptotic performance of nonparametric goodness of fit testing. The exponential decay rate of the type-II error probability is used as the asymptotic performan…