109 citations · 212 across the 14 of their papers we have counts for
14 papers
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…
Causal Coordinated Concurrent Reinforcement Learning
Tim Tse, Isaac Chan, Zhitang Chen
In this work, we propose a novel algorithmic framework for data sharing and coordinated exploration for the purpose of learning more data-efficient and better performing policies u…
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…
gCastle: A Python Toolbox for Causal Discovery
Keli Zhang, Shengyu Zhu, Marcus Kalander +4
is an end-to-end Python toolbox for causal structure learning. It provides functionalities of generating data from either simulator or real-world dataset, learni…
Ordering-Based Causal Discovery with Reinforcement Learning
Xiaoqiang Wang, Yali Du, Shengyu Zhu +4
It is a long-standing question to discover causal relations among a set of variables in many empirical sciences. Recently, Reinforcement Learning (RL) has achieved promising result…
On Low Rank Directed Acyclic Graphs and Causal Structure Learning
Zhuangyan Fang, Shengyu Zhu, Jiji Zhang +3
Despite several advances in recent years, learning causal structures represented by directed acyclic graphs (DAGs) remains a challenging task in high dimensional settings when the…
A Graph Autoencoder Approach to Causal Structure Learning
Ignavier Ng, Shengyu Zhu, Zhitang Chen +1
Causal structure learning has been a challenging task in the past decades and several mainstream approaches such as constraint- and score-based methods have been studied with theor…
Masked Gradient-Based Causal Structure Learning
Ignavier Ng, Shengyu Zhu, Zhuangyan Fang +3
This paper studies the problem of learning causal structures from observational data. We reformulate the Structural Equation Model (SEM) with additive noises in a form parameterize…
Causal Discovery by Kernel Intrinsic Invariance Measure
Zhitang Chen, Shengyu Zhu, Yue Liu +1
Reasoning based on causality, instead of association has been considered as a key ingredient towards real machine intelligence. However, it is a challenging task to infer causal re…
Asymptotically Optimal One- and Two-Sample Testing with Kernels
Shengyu Zhu, Biao Chen, Zhitang Chen +1
We characterize the asymptotic performance of nonparametric one- and two-sample testing. The exponential decay rate or error exponent of the type-II error probability is used as th…
Causal Discovery with Reinforcement Learning
Shengyu Zhu, Ignavier Ng, Zhitang Chen
Discovering causal structure among a set of variables is a fundamental problem in many empirical sciences. Traditional score-based casual discovery methods rely on various local he…
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…