activity
20242026
most citedMulti-Agent Causal Discovery Using Large Language Models

7 citations · 7 across the 7 of their papers we have counts for

collaborators

8 papers

stat.ML2026

Jigsaw-CRL: Recovering Global Latent Causal Order from Fragmented Multi-Client Interventions

Haijie Xu, Chen Zhang

Causal representation learning (CRL) aims to recover latent causal variables and their structural relations from high-dimensional observations. Existing CRL methods typically assum…

cs.LG2026

Combinatorial Bandit Bayesian Optimization for Tensor Outputs

Jingru Huang, Haijie Xu, Jie Guo +2

Bayesian optimization (BO) has been widely used to optimize expensive and black-box functions across various domains. However, existing BO methods have not addressed tensor-output…

stat.ML2025

Function-on-Function Bayesian Optimization

Jingru Huang, Haijie Xu, Manrui Jiang +1

Bayesian optimization (BO) has been widely used to optimize expensive and gradient-free objective functions across various domains. However, existing BO methods have not addressed…

stat.ML2025

Design of Experiment for Discovering Directed Mixed Graph

Haijie Xu, Chen Zhang

We study the design of interventions for causal discovery in simple structural causal models whose causal graphs are directed mixed graphs (DMGs) that may contain directed cycles a…

stat.ML2025

Quickest Causal Change Point Detection by Adaptive Intervention

Haijie Xu, Chen Zhang

We propose an algorithm for change point monitoring in linear causal models that accounts for interventions. Through a special centralization technique, we can concentrate the chan…

cs.AI2024★ 7 cited

Multi-Agent Causal Discovery Using Large Language Models

Hao Duong Le, Xin Xia, Haijie Xu +1

Causal discovery aims to identify causal relationships between variables and is a fundamental problem across the sciences. Traditional statistical causal discovery (SCD) methods re…

stat.ML2024

Functional-Edged Network Modeling

Haijie Xu, Chen Zhang

Contrasts with existing works which all consider nodes as functions and use edges to represent the relationships between different functions. We target at network modeling whose ed…

stat.ML2024

Partially-Observable Sequential Change-Point Detection for Autocorrelated Data via Upper Confidence Region

Haijie Xu, Xiaochen Xian, Chen Zhang +1

Sequential change point detection for multivariate autocorrelated data is a very common problem in practice. However, when the sensing resources are limited, only a subset of varia…