Matching in Selective and Balanced Representation Space for Treatment Effects Estimation. - Zhixuan Chu; Stephen L. Rathbun; Sheng Li 001 (CIKM, 2020)

Causal Inference Meets Machine Learning. - Peng Cui 001; Zheyan Shen; Sheng Li 001; Liuyi Yao; Yaliang Li; Zhixuan Chu; Jing Gao 004 (KDD, 2020)

Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data. - Zhixuan Chu; Stephen L. Rathbun; Sheng Li 001 (KDD, 2021)

A Survey on Causal Inference. - Liuyi Yao; Zhixuan Chu; Sheng Li 001; Yaliang Li; Jing Gao 004; Aidong Zhang (ACM Trans. Knowl. Discov. Data, 2021)

Multi-Task Adversarial Learning for Treatment Effect Estimation in Basket Trials. - Zhixuan Chu; Stephen L. Rathbun; Sheng Li 001 (CHIL, 2022)

Hierarchical Capsule Prediction Network for Marketing Campaigns Effect. - Zhixuan Chu; Hui Ding; Guang Zeng 001; Yuchen Huang; Tan Yan; Yulin Kang; Sheng Li 001 (CIKM, 2022)

Incorporating Casual Analysis into Diversified and Logical Response Generation. - Jiayi Liu 004; Wei Wei 002; Zhixuan Chu; Xing Gao; Ji Zhang 011; Tan Yan; Yulin Kang (COLING, 2022)

Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data. - Zhixuan Chu; Stephen L. Rathbun; Sheng Li 001 (SDM, 2022)

Knowledge-Guided Article Embedding Refinement for Session-Based News Recommendation. - Heng-Shiou Sheu; Zhixuan Chu; Daiqing Qi; Sheng Li 001 (IEEE Trans. Neural Networks Learn. Syst., 2022)

Continual Treatment Effect Estimation: Challenges and Opportunities. - Zhixuan Chu; Sheng Li 001 (AAAI Bridge Program, 2023)

Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data. - Zhichao Chen 001; Leilei Ding; Zhixuan Chu; Yucheng Qi; Jianmin Huang; Hao Wang 049 (CIKM, 2023)

Unsupervised Anomaly Detection & Diagnosis: A Stein Variational Gradient Descent Approach. - Zhichao Chen 001; Leilei Ding; Jianmin Huang; Zhixuan Chu; Qingyang Dai; Hao Wang 049 (CIKM, 2023)

Continual Learning in Predictive Autoscaling. - Hongyan Hao; Zhixuan Chu; Shiyi Zhu; Gangwei Jiang; Yan Wang 002; Caigao Jiang; James Y. Zhang; Wei Jiang; Siqiao Xue; Jun Zhou 011 (CIKM, 2023)

Continual Causal Inference with Incremental Observational Data. - Zhixuan Chu; Ruopeng Li; Stephen L. Rathbun; Sheng Li 001 (ICDE, 2023)

Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference. - Yan Wang 002; Zhixuan Chu; Tao Zhou; Caigao Jiang; Hongyan Hao; Minjie Zhu; Xindong Cai; Qing Cui; Longfei Li; James Y. Zhang; Siqiao Xue; Jun Zhou 011 (ICDM (Workshops), 2023)

Fair Attribute Completion on Graph with Missing Attributes. - Dongliang Guo 002; Zhixuan Chu; Sheng Li 001 (ICLR, 2023)

pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting. - Yunyi Zhou; Zhixuan Chu; Yijia Ruan; Ge Jin; Yuchen Huang; Sheng Li 001 (IJCAI, 2023)

Prompt-augmented Temporal Point Process for Streaming Event Sequence. - Siqiao Xue; Yan Wang 002; Zhixuan Chu; Xiaoming Shi 001; Caigao Jiang; Hongyan Hao; Gangwei Jiang; Xiaoyun Feng; James Zhang; Jun Zhou 011 (NeurIPS, 2023)

Estimating Propensity Scores with Deep Adaptive Variable Selection. - Zhixuan Chu; Mechelle Claridy; José Cordero; Sheng Li 001; Stephen L. Rathbun (SDM, 2023)

Causal Effect Estimation: Recent Advances, Challenges, and Opportunities. - Zhixuan Chu; Jianmin Huang; Ruopeng Li; Wei Chu; Sheng Li 001 (CoRR, 2023)

EasyTPP: Towards Open Benchmarking the Temporal Point Processes. - Siqiao Xue; Xiaoming Shi 001; Zhixuan Chu; Yan Wang 002; Fan Zhou 012; Hongyan Hao; Caigao Jiang; Chen Pan; Yi Xu; James Y. Zhang; Qingsong Wen; Jun Zhou 011; et al. (CoRR, 2023)

Continual Learning in Predictive Autoscaling. - Hongyan Hao; Zhixuan Chu; Shiyi Zhu; Gangwei Jiang; Yan Wang 002; Caigao Jiang; James Zhang; Wei Jiang; Siqiao Xue; Jun Zhou 011 (CoRR, 2023)

Enhancing Recommender Systems with Large Language Model Reasoning Graphs. - Yan Wang 002; Zhixuan Chu; Xin Ouyang; Simeng Wang; Hongyan Hao; Yue Shen; Jinjie Gu; Siqiao Xue; James Y. Zhang; Qing Cui; Longfei Li; Jun Zhou 011; et al. (CoRR, 2023)

Leveraging Large Language Models for Pre-trained Recommender Systems. - Zhixuan Chu; Hongyan Hao; Xin Ouyang; Simeng Wang; Yan Wang 002; Yue Shen; Jinjie Gu; Qing Cui; Longfei Li; Siqiao Xue; James Y. Zhang; Sheng Li 001 (CoRR, 2023)

Trustworthy Representation Learning Across Domains. - Ronghang Zhu; Dongliang Guo 002; Daiqing Qi; Zhixuan Chu; Xiang Yu 002; Sheng Li 001 (CoRR, 2023)

Prompt-augmented Temporal Point Process for Streaming Event Sequence. - Siqiao Xue; Yan Wang 002; Zhixuan Chu; Xiaoming Shi 001; Caigao Jiang; Hongyan Hao; Gangwei Jiang; Xiaoyun Feng; James Y. Zhang; Jun Zhou 011 (CoRR, 2023)

Data-Centric Financial Large Language Models. - Zhixuan Chu; Huaiyu Guo; Xinyuan Zhou; Yijia Wang; Fei Yu; Hong Chen; Wanqing Xu; Xin Lu; Qing Cui; Longfei Li; Jun Zhou 011; Sheng Li 001 (CoRR, 2023)

Intelligent Virtual Assistants with LLM-based Process Automation. - Yanchu Guan; Dong Wang 062; Zhixuan Chu; Shiyu Wang 001; Feiyue Ni; Ruihua Song; Longfei Li; Jinjie Gu; Chenyi Zhuang (CoRR, 2023)

Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction. - Zhixuan Chu; Mengxuan Hu; Qing Cui; Longfei Li; Sheng Li 001 (AAAI, 2024)

LLMRG: Improving Recommendations through Large Language Model Reasoning Graphs. - Yan Wang 002; Zhixuan Chu; Xin Ouyang; Simeng Wang; Hongyan Hao; Yue Shen; Jinjie Gu; Siqiao Xue; James Zhang; Qing Cui; Longfei Li; Jun Zhou 011; et al. (AAAI, 2024)

Self-Para-Consistency: Improving Reasoning Tasks at Low Cost for Large Language Models. - Wenqing Chen; Weicheng Wang; Zhixuan Chu; Kui Ren 001; Zibin Zheng; Zhichao Lu (ACL (Findings), 2024)

A Causal Explainable Guardrails for Large Language Models. - Zhixuan Chu; Yan Wang 002; Longfei Li; Zhibo Wang 001; Zhan Qin; Kui Ren 001 (CCS, 2024)

Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload Forecasting. - Shiyu Wang 001; Zhixuan Chu; Yinbo Sun; Yu Liu 071; Yuliang Guo; Yang Chen; Huiyang Jian; Lintao Ma; Xingyu Lu; Jun Zhou 011 (CIKM, 2024)

Causal Interventional Prediction System for Robust and Explainable Effect Forecasting. - Zhixuan Chu; Hui Ding; Guang Zeng 001; Shiyu Wang 001; Yiming Li 004 (CIKM, 2024)

VMFTransformer: An Angle-Preserving and Auto-Scaling Machine for Multi-Horizon Probabilistic Forecasting. - Yunyi Zhou; Ruohan Gao; Xinping Zheng; Yuchen Huang; Zhixuan Chu (ECAI, 2024)

Enhancing Event Sequence Modeling with Contrastive Relational Inference. - Yan Wang 002; Zhixuan Chu; Tao Zhou; Caigao Jiang; Hongyan Hao; Minjie Zhu; Xindong Cai; Qing Cui; Longfei Li; James Y. Zhang; Siqiao Xue; Jun Zhou 011 (ICASSP, 2024)

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models. - Ming Jin 005; Shiyu Wang 001; Lintao Ma; Zhixuan Chu; James Y. Zhang; Xiaoming Shi 001; Pin-Yu Chen; Yuxuan Liang; Yuan-Fang Li; Shirui Pan; Qingsong Wen (ICLR, 2024)

EasyTPP: Towards Open Benchmarking Temporal Point Processes. - Siqiao Xue; Xiaoming Shi 001; Zhixuan Chu; Yan Wang 002; Hongyan Hao; Fan Zhou 012; Caigao Jiang; Chen Pan; James Y. Zhang; Qingsong Wen; Jun Zhou 011; Hongyuan Mei (ICLR, 2024)

Intelligent Agents with LLM-based Process Automation. - Yanchu Guan; Dong Wang 062; Zhixuan Chu; Shiyu Wang 001; Feiyue Ni; Ruihua Song; Chenyi Zhuang (KDD, 2024)

Invariant Graph Learning for Causal Effect Estimation. - Yongduo Sui; Caizhi Tang; Zhixuan Chu; Junfeng Fang; Yuan Gao 020; Qing Cui; Longfei Li; Jun Zhou 011; Xiang Wang 010 (WWW, 2024)

LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation. - Zhixuan Chu; Yan Wang 002; Qing Cui; Longfei Li; Wenqing Chen; Sheng Li 001; Zhan Qin; Kui Ren 001 (CoRR, 2024)

Professional Agents - Evolving Large Language Models into Autonomous Experts with Human-Level Competencies. - Zhixuan Chu; Yan Wang 002; Feng Zhu 011; Lu Yu 006; Longfei Li; Jinjie Gu (CoRR, 2024)

GSINA: Improving Subgraph Extraction for Graph Invariant Learning via Graph Sinkhorn Attention. - Fangyu Ding; Haiyang Wang; Zhixuan Chu; Tianming Li; Zhaoping Hu; Junchi Yan (CoRR, 2024)

Bridging Causal Discovery and Large Language Models: A Comprehensive Survey of Integrative Approaches and Future Directions. - Guangya Wan; Yuqi Wu; Mengxuan Hu; Zhixuan Chu; Sheng Li 001 (CoRR, 2024)

Sora Detector: A Unified Hallucination Detection for Large Text-to-Video Models. - Zhixuan Chu; Lei Zhang 006; Yichen Sun; Siqiao Xue; Zhibo Wang 001; Zhan Qin; Kui Ren 001 (CoRR, 2024)

Towards Real World Debiasing: A Fine-grained Analysis On Spurious Correlation. - Zhibo Wang 001; Peng Kuang; Zhixuan Chu; Jingyi Wang 004; Kui Ren 001 (CoRR, 2024)

A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions. - Lei Liu; Xiaoyan Yang; Junchi Lei; Xiaoyang Liu; Yue Shen; Zhiqiang Zhang 012; Peng Wei; Jinjie Gu; Zhixuan Chu; Zhan Qin; Kui Ren 001 (CoRR, 2024)

DB-GPT-Hub: Towards Open Benchmarking Text-to-SQL Empowered by Large Language Models. - Fan Zhou 012; Siqiao Xue; Danrui Qi; Wenhui Shi; Wang Zhao 005; Ganglin Wei; Hongyang Zhang; Caigai Jiang; Gangwei Jiang; Zhixuan Chu; Faqiang Chen (CoRR, 2024)

Prompt-Consistency Image Generation (PCIG): A Unified Framework Integrating LLMs, Knowledge Graphs, and Controllable Diffusion Models. - Yichen Sun; Zhixuan Chu; Zhan Qin; Kui Ren 001 (CoRR, 2024)

FAMMA: A Benchmark for Financial Domain Multilingual Multimodal Question Answering. - Siqiao Xue; Tingting Chen; Fan Zhou 012; Qingyang Dai; Zhixuan Chu; Hongyuan Mei (CoRR, 2024)

Explainable Behavior Cloning: Teaching Large Language Model Agents through Learning by Demonstration. - Yanchu Guan; Dong Wang 062; Yan Wang 002; Haiqing Wang; Renen Sun; Chenyi Zhuang; Jinjie Gu; Zhixuan Chu (CoRR, 2024)

JailbreakLens: Interpreting Jailbreak Mechanism in the Lens of Representation and Circuit. - Zeqing He; Zhibo Wang 001; Zhixuan Chu; Huiyu Xu; Rui Zheng; Kui Ren 001; Chun Chen 001 (CoRR, 2024)

DTRNet: Precisely Correcting Selection Bias in Individual-Level Continuous Treatment Effect Estimation by Reweighted Disentangled Representation. - Mengxuan Hu; Zhixuan Chu; Sheng Li 001 (Trans. Mach. Learn. Res., 2024)

A Survey of Trustworthy Representation Learning Across Domains. - Ronghang Zhu; Dongliang Guo 002; Daiqing Qi; Zhixuan Chu; Xiang Yu 002; Sheng Li 001 (ACM Trans. Knowl. Discov. Data, 2024)

Mitigating Social Bias in Large Language Models: A Multi-Objective Approach Within a Multi-Agent Framework. - Zhenjie Xu; Wenqing Chen; Yi Tang; Xuanying Li; Cheng Hu; Zhixuan Chu; Kui Ren 001; Zibin Zheng; Zhichao Lu (AAAI, 2025)

TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis. - Shiyu Wang 001; Jiawei Li 017; Xiaoming Shi 001; Zhou Ye 001; Baichuan Mo; Wenze Lin; Shengtong Ju; Zhixuan Chu; Ming Jin 005 (ICLR, 2025)

Probe before You Talk: Towards Black-box Defense against Backdoor Unalignment for Large Language Models. - Biao Yi; Tiansheng Huang; Sishuo Chen; Tong Li 011; Zheli Liu; Zhixuan Chu; Yiming Li 004 (ICLR, 2025)

Understanding and Mitigating Overrefusal in LLMs from an Unveiling Perspective of Safety Decision Boundary. - Licheng Pan; Yongqi Tong; Xin Zhang; Xiaolu Zhang; Jun Zhou 011; Zhixuan Chu (CoRR, 2025)