109 citations · 353 across the 25 of their papers we have counts for
25 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…
Optimizing Hierarchical Queries for the Attribution Reporting API
Matthew Dawson, Badih Ghazi, Pritish Kamath +8
We study the task of performing hierarchical queries based on summary reports from the {\em Attribution Reporting API} for ad conversion measurement. We demonstrate that methods fr…
RIS-Assisted Joint Uplink Communication and Imaging: Phase Optimization and Bayesian Echo Decoupling
Shengyu Zhu, Zehua Yu, Qinghua Guo +3
Achieving integrated sensing and communication (ISAC) via uplink transmission is challenging due to the unknown waveform and the coupling of communication and sensing echoes. In th…
GFlowCausal: Generative Flow Networks for Causal Discovery
Wenqian Li, Yinchuan Li, Shengyu Zhu +3
Causal discovery aims to uncover causal structure among a set of variables. Score-based approaches mainly focus on searching for the best Directed Acyclic Graph (DAG) based on a pr…
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…
ZIN: When and How to Learn Invariance Without Environment Partition?
Yong Lin, Shengyu Zhu, Lu Tan +1
It is commonplace to encounter heterogeneous data, of which some aspects of the data distribution may vary but the underlying causal mechanisms remain constant. When data are divid…
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems
Yan Lyu, Sunhao Dai, Peng Wu +7
Accurate recommendation and reliable explanation are two key issues for modern recommender systems. However, most recommendation benchmarks only concern the prediction of user-item…
Universality of parametric Coupling Flows over parametric diffeomorphisms
Junlong Lyu, Zhitang Chen, Chang Feng +5
Invertible neural networks based on Coupling Flows CFlows) have various applications such as image synthesis and data compression. The approximation universality for CFlows is of p…
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…
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…
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…
Distributed Detection in Ad Hoc Networks Through Quantized Consensus
Shengyu Zhu, Biao Chen
We study asymptotic performance of distributed detection in large scale connected sensor networks. Contrasting to the canonical parallel network where a single node has access to l…
Distributed Average Consensus with Bounded Quantizer and Unbounded Input
Shengyu Zhu, Biao Chen
This paper considers distributed average consensus using finite-bit bounded quantizer with possibly unbounded data. Under the framework of the alternating direction method of multi…
Quantized Consensus ADMM for Multi-Agent Distributed Optimization
Shengyu Zhu, Mingyi Hong, Biao Chen
Multi-agent distributed optimization over a network minimizes a global objective formed by a sum of local convex functions using only local computation and communication. We develo…
Quantized Consensus by the ADMM: Probabilistic versus Deterministic Quantizers
Shengyu Zhu, Biao Chen
This paper develops efficient algorithms for distributed average consensus with quantized communication using the alternating direction method of multipliers (ADMM). We first study…