New Preprints
- MOSAIC: Module Discovery via Sparse Additive Identifiable Causal Learning for Scientific Time Series [pdf]
Shicheng Fan, Nour Elhendawy, Jianle Sun, Ke Fang, Kun Zhang, Yihang Wang, Lu Cheng - Adaptive Stopping for Multi-Turn LLM Reasoning [pdf]
Xiaofan Zhou, Huy Nguyen, Bo Yu, Chenxi Liu, Lu Cheng - Revisiting NLI: Towards Cost-Effective and Human-Aligned Metrics for Evaluating LLMs in Question Answering [pdf]
Sai Shridhar Balamurali, Lu Cheng - A Lightweight Large Language Model-Based Multi-Agent System for 2D Frame Structural Analysis [pdf]
Ziheng Geng*, Jiachen Liu*, Ran Cao, Lu Cheng, Haifeng Wang, Minghui Cheng - What Shapes a Creative Machine Mind? Comprehensively Benchmarking Creativity in Foundation Models [pdf]
Zicong He*, Boxuan Zhang*, Weihao Liu*, Ruixiang Tang, Lu Cheng - SOM-1k: A thousand-problem benchmark dataset for strength of materials [pdf]
Qixin Wan, Zilong Wang, Jingwen Zhou, Wanting Wang, Ziheng Geng, Jiachen Liu, Ran Cao, Minghui Cheng, Lu Cheng - Credence Calibration Game: Calibrating Large Language Models through Structured Play [pdf]
Ke Fang, Tianyi Zhao, Lu Cheng
Books and Book Chapters
Accepted Papers
- Temporal Graph Prototype-conditioned Conformal Prediction for Fraud Detection
Xudong Chen, Shengbo Gong, Lu Cheng, Wei Jin. KDD’2026 - The Confidence Trap: Calibration Attacks for Graph Neural Networks
Dang Cao Cuong, Jiahao Zhang, HIEU TA QUANG, Dung D. Le, Lu Cheng, Suhang Wang. KDD’2026 - SafeCRS: Personalized Safety Alignment for LLM-Based Conversational Recommender Systems [pdf]
Haochang Hao*, Yifan Xu*, Yingqiang Ge, Lu Cheng. KDD’2026 - Diagnosing Multi-step Reasoning Failures in Black-box LLMs via Stepwise Confidence Attribution
Xiaoou Liu, Tiejin Chen, Dengjia Zhang, Yaqing Wang, Lu Cheng, Hua Wei. ICML’2026 - Differentiable Conformal Training for LLM Reasoning Factuality [pdf]
Nathan Hittesdorf, Marco Salzetta, Lu Cheng. ICML’2026 - MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs [pdf]
Huiyi Chen, Jiawei Peng, Dehai Min, Changchang Sun, Kaijie Chen, Yan Yan, Xu Yang, Lu Cheng. ICML’2026 - Latent Thoughts Tuning: Bridging Context and Reasoning with Fused Information in Latent Tokens [pdf]
Weihao Liu, Dehai Min, Lu Cheng. ICML’2026 - TRACE: Trajectory Recovery for Continuous Mechanism Evolution in Causal Representation Learning [pdf]
Shicheng Fan, Kun Zhang, Lu Cheng. ICML’2026 - Early Risk Prediction with Temporally and Contextually Grounded Clinical Language Processing [pdf]
Rochana Chaturvedi, Yue Zhou, Andrew Boyd, Brian T Layden, Mudassir Rashid, Lu Cheng, Ali Cinar, Barbara Di Eugenio. TACL’2026 - Post-Training Adaptive Conformal Prediction for Incomplete Time Series. [pdf]
Baiting Chen, Xiaofan Zhou, Lu Cheng. TMLR’2026 - QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation [pdf]
Dehai Min, Kailin Zhang, Tongtong Wu, Lu Cheng. ACL’2026 Findings. - Robust Uncertainty Quantification for Self-Evolving Large Language Models via Continual Domain Pretraining [pdf]
Xiaofan Zhou, Lu Cheng. ACL’2026 Findings. - When and What to Ask: AskBench and Rubric-Guided RLVR for LLM Clarification [pdf]
Jiale Zhao, Ke Fang, Lu Cheng. ACL’2026 Findings. - SELAUR: Self Evolving LLM Agent via Uncertainty-aware Rewards [pdf]
Dengjia Zhang, Xiaoou Liu, Lu Cheng, Yaqing Wang, Kenton Murray, Hua Wei. PAKDD’2026 - Evaluating Social Bias in RAG Systems: When External Context Helps and Reasoning Hurts [pdf]
Shweta Parihar, Lu Cheng. PAKDD’2026 - Smart Trial: Evaluating the Use of Large Language Models for Recruiting Clinical Trial Participants via Social Media [pdf]
Xiaofan Zhou, Zisu Wang, Janice Krieger, Mohan Zalake, Lu Cheng. PAKDD’2026 - A Large Language Model-Empowered Agent for Reliable and Robust Structural Analysis [pdf]
Jiachen Liu*, Ziheng Geng*, Ran Cao, Lu Cheng, Paolo Bocchini, Minghui Cheng. Journal of Structure and Infrastructure Engineering. 2026. - InsurAgent: A Large Language Model-Empowered Agent for Simulating Individual Behavior in Purchasing Flood Insurance [pdf]
Ziheng Geng; Jiachen Liu; Ran Cao;Lu Cheng; Dan M. Frangopol; Minghui Cheng. Journal of Risk and Uncertainty Analysis. 2026 - CP-Router: An Uncertainty-Aware Router Between LLM and LRM [pdf]
Jiayuan Su, Fulin Lin, Zhaopeng Feng, Han Zheng, Teng Wang, Zhenyu Xiao, Xinlong Zhao, Zuozhu Liu, Lu Cheng, Hongwei Wang. AAAI’26 (Oral) - Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks [pdf]
Qian Ma, Hongliang Chi, Hengrui Zhang, Kay Liu, Zhiwei Zhang, Lu Cheng, Suhang Wang, Philip S. Yu, Yao Ma. SIGKDD Exploration. 2025 - DemoShapley: Valuation of Demonstrations for In-Context Learning [pdf]
Shan Xie, Man Luo, Chadly Daniel Stern, Mengnan Du, Lu Cheng. IEEE Big Data’25 - FABLE: Fairness Attack in Abusive Language Detection [pdf]
Yueqing Liang, Lu Cheng, Ali Payani, and Kai Shu. In IEEE Big Data’25 - Beyond Accuracy: The Role of Calibration in Self-Improving Large Language Models [pdf]
Liangjie Huang, Dawei Li, Huan Liu, Lu Cheng. IEEE Big Data’25. - Accelerating Causal Network Discovery of Alzheimer’s Disease Biomarkers via Scientific Literature-based Retrieval Augmented Generation [pdf]
Xiaofan Zhou, Liangjie Huang, Pinyang Cheng, Wenpen Yin, Rui Zhang, Wenrui Hao, Lu Cheng. IEEE Big Data’25. - Adaptive Degree-based Conformal Prediction for Individual Treatment Effect Estimation on Networked Data [pdf]
Xiaofan Zhou, Jing Ma, Lu Cheng. IEEE Big Data’25. - Moral Self-correction is Not An Innate Capability in Language Models [pdf]
Guangliang Liu*, Zimo Qi*, Xitong Zhang, Lu Cheng, Kristen Marie Johnson. IJCNLP-AACL’25 (Findings) - Large Vision-Language Model Alignment and Misalignment: A Survey Through the Lens of Explainability [pdf]
Dong Shu, Haiyan Zhao, Jingyu Hu, Weirui Liu, Lu Cheng, Mengnan Du. EMNLP’25 (Findings) - From generation to judgment: Opportunities and challenges of llm-as-a-judge [pdf]
Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, Kai Shu, Lu Cheng, Huan Liu. EMNLP’25 (Main) - Interpretable named entity recognition via integrating logical rule learning with deep neural networks [pdf]
Yulin Chen, Bo Yuan, Beishui Liao, Lu Cheng, Dov Gabbay. Expert Systems With Applications’25. - Deconstructing the ethics of large language models from long-standing issues to new-emerging dilemmas [pdf]
Chengyuan Deng, Yiqun Duan, Xin Jin, Heng Chang, Yijun Tian, Han Liu, Henry Peng Zou, Yiqiao Jin, Yijia Xiao, Yichen Wang, Shenghao Wu, Zongxing Xie, Kuofeng Gao, Sihong He, Lu Cheng, Haohan Wang, Jun Zhuang. AI and Ethics’25 - Understanding the uncertainty of llm explanations: A perspective based on reasoning topology [pdf]
Longchao Da, Xiaoou Liu, Jiaxin Dai, Lu Cheng, Yaqing Wang, Hua Wei. COLM’25 - Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges [pdf]
Usman Gohar, Zeyu Tang, Jialu Wang, Kun Zhang, Peter L Spirtes, Yang Liu, Lu Cheng. TMLR’25 - Conformal Prediction: A Data Perspective [pdf]
Xiaofan Zhou*, Baiting Chen*, Yu Gui, Lu Cheng. ACM Computing Survey’25 - DBR: Divergence-Based Regularization for Debiasing Natural Language Understanding Models [pdf]
Zihao Li, Ruixiang Tang, Lu Cheng, Shuaiqiang Wang, Dawei Yin, Mengnan Du. In SIGKDD Explorations’25. - Threshold Filtering Packing for Supervised Fine-Tuning: Training Related Samples within Packs [pdf]
Jiancheng Dong, Lei Jiang, Wei Jin, Lu Cheng. In NAACL’25 Main Conference - Towards Trustworthy Knowledge Graph Reasoning: An Uncertainty Aware Perspective [pdf]
Bo Ni, Yu Wang, Lu Cheng, Erik Blasch, Tyler Derr. In AAAI’25 - Unveiling Performance Challenges of Large Language Models in Low-Resource Healthcare: A Demographic Fairness Perspective [pdf]
Yue Zhou, Barbara Di Eugenio, and Lu Cheng. In COLING’25 - Assessing Empathy in Large Language Models with Real-World Physician-Patient Interactions [pdf]
Man Luo, Christopher J Warren, Lu Cheng, Haidar M Abdul-Muhsin, Imon Banerjee. In IEEE Big Data’24 - On Demonstration Selection for Improving Fairness in Language Models [pdf]
Song Wang, Peng Wang, Yushun Dong, Tong Zhou, Lu Cheng, Yangfeng Ji, Jundong Li. In NeurIPS’24 SoLaR Workshop. (Spotlight) - Different Horses for Different Courses: Comparing Bias Mitigation Algorithms in ML [pdf]
Prakhar Ganesh, Usman Gohar, Lu Cheng, Golnoosh Farnadi. In NeurIPS’24 Algorithmic Fairness through the Lens of Metrics and Evaluation Workshop. (Spotlight) - Conformalized Time Series with Semantic Features [pdf]
Baiting Chen, Zhimei Ren, Lu Cheng. In NeurIPS’24 (Main Track) - ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees [pdf]
Zhiyuan Wang, Jinhao Duan, Lu Cheng, Yue Zhang, Qingni Wang, Xiaoshuang Shi, Kaidi Xu, Heng Tao Shen, Xiaofeng Zhu. In EMNLP’24 (Findings). - LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing [pdf]
Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi LIU, Renze Lou, Henry Peng Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Ranran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing et al. In EMNLP’24 (Main Track) - API Is Enough: Conformal Prediction for Large Language Models Without Logit-Access [pdf]
Jiayuan Su, Jing Luo, Hongwei Wang, Lu Cheng. In EMNLP’24 (Findings). - Large Language Models for Data Annotation: A Survey [pdf]
Zhen Tan, Dawei Li, Song Wang, Alimohammad Beigi, Bohan Jiang, Amrita Bhattacharjee, Mansooreh Karami, Jundong Li, Lu Cheng, Huan Liu. In EMNLP’24 (Main Track) - Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media [pdf]
Bohan Jiang, Lu Cheng, Zhen Tan, Ruocheng Guo, Huan Liu. In DSAA’24 - Evaluating LLMs Capabilities Towards Understanding Social Dynamics [pdf]
Anique Tahir, Lu Cheng, Manuel Sandoval, Yasin Silva, Deborah Hall and Huan Liu. In ASONAM’24 - Robust Stance Detection: Understanding Public Perceptions in Social Media [pdf]
Nayoung Kim, David Mosallanezhad, Lu Cheng, Michelle Mancenido and Huan Liu. In ASONAM’24 - JORA: JAX Tensor-Parallel LoRA Library for Retrieval Augmented Fine-Tuning [pdf]
Anique Tahir, Lu Cheng, Huan Liu. In ACL’24 System Demo Track. - A Survey on Safe Multi-Modal Learning System [pdf]
Tianyi Zhao, Liangliang Zhang, Yao Mao, Lu Cheng. In KDD’24. - Conformalized Link Prediction on Graph Neural Networks [pdf]
Tianyi Zhao, Jian Kang, Lu Cheng. In KDD’24. - Interpreting Pretrained Language Models via Concept Bottlenecks (Best Paper) [pdf]
Zhen Tan, Lu Cheng, Song Wang, Bo Yuan, Jundong Li, Huan Liu. In PAKDD’24 and IJCAI’25 Best Paper Track. - sUrban: Stable Prediction for Unseen Urban Data from Location-based Sensors [pdf]
Qianru Wang, Bing Guo, Lu Cheng, Zhiwen Yu. In UbiComp’23. - Equal Opportunity of Coverage in Fair Regression [pdf][code]
Fangxin Wang, Lu Cheng, Ruocheng Guo, Kay Liu, Philip S. Yu. In NeurIPS’23. New Orleans, US. Dec 10, 2023 – Dec 16, 2023. - Unveiling the Role of Message Passing in Dual-Privacy Preservation on GNNs [pdf]
Tianyi Zhao, Hui Hu, Lu Cheng. In CIKM’23. University of Birmingham and Eastside Rooms, UK. 21 - 25 October 2023. - Fairness through Aleatoric Uncertainty [pdf]
Anique Tahir, Lu Cheng, Huan Liu. In CIKM’23. University of Birmingham and Eastside Rooms, UK. 21 - 25 October 2023. - Fair Few-shot Learning with Auxiliary Sets [pdf]
Song Wang, Jing Ma, Lu Cheng, Jundong Li. In ECAI’23. 09.30 - 10.04, 2023, Kraków, Poland - Intersectionality and Testimonial Injustice in Medical Records [pdf]
Kenya Andrews, Bhuvani Shah, and Lu Cheng. In ACL’23 Clinical NLP Workshop. Toronto, Canada. July 9th to July 14th, 2023. - A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges [pdf][Media Coverage]
Usman Gohar and Lu Cheng. In IJCAI’23 Survey Track. Macao, China. 19th-25th August, 2023 - CausalSE: Understanding Varied Spatial Effects with Missing Data Toward Adding New Bike-sharing Stations [pdf]
Qianru Wang, Bing Guo, Lu Cheng, Zhiwen Yu, and Huan Liu. In TKDD’23 - Causal Disentanglement for Implicit Recommendations with Network Information [pdf]
Paras Sheth, Ruocheng Guo, Lu Cheng, Kasim Selcuk Candan, and Huan Liu. In TKDD’23 - Distributional Shift Adaptation using Domain-Specific Features [pdf]
Anique Tahir, Lu Cheng, Ruocheng Guo, and Huan Liu. In IEEE BigData’22 - Nothing Stands Alone: Leveraging News Relations through a Hypergraph for Fake News Detection [pdf]
Ujun Jeong, Kaize Ding, Lu Cheng, Ruocheng Guo, Kai Shu, and Huan Liu. In IEEE BigData’22 - Debiasing Word Embeddings with Nonlinear Geometry [pdf][code]
Lu Cheng, Nayoung Kim, and Huan Liu. In the 29th International Conference on Computational Linguistics (COLING). Hybrid. October 12-17, 2022. - Classifying COVID-19 related Meta Ads using Discourse Representation through Hypergraph
Ujun Jeong, Zeyad Alghamdi, Kaize Ding, Lu Cheng, Baoxin Li, and Huan Liu. In the 15th SBP-BRiMS. Pittsburg. Sept. 20-22. 2022 - Improving Vaccine Stance Detection by Combining Online and Offline Data [pdf]
Anique Tahir, Lu Cheng, Paras Sheth, and Huan Liu. In the 15th SBP-BRiMS. Pittsburg. Sept. 20-22. 2022 - Bridging the Gap: Commonality and Differences between Online and Offline COVID-19 Data [pdf]
Nayoung Kim, Ahmadreza Mosallanezhad, Lu Cheng, Baoxin Li, and Huan Liu. In the 15th SBP-BRiMS. Pittsburg. Sept. 20-22. 2022 - Causal Disentanglement with Network Information for Debiased Recommendations [pdf]
Paras Sheth, Ruocheng Guo, Kaize Ding, Lu Cheng, K. Selcuk Candan, Huan Liu. IEEE SISAP 2022 - Bias Mitigation for Toxicity Detection via Sequential Decisions [pdf]
Lu Cheng, Ahmadreza Mosallanezhad, Yasin Silva, Deborah Hall, and Huan Liu. In SIGIR. Madrid. July 11-15, 2022. - Evaluation Methods and Measures for Causal Learning Algorithms [pdf]
Lu Cheng, Ruocheng Guo, Raha Moraffah, Paras Sheth, K. Selcuk Candan, and Huan Liu. In IEEE Transactions on Artificial Intelligence (TAI). 2022. - Learning Privacy-Preserving Graph Convolutional Network with Partially Observed Sensitive Attributes [pdf] [code]
Hui Hu, Lu Cheng, Jayden Parker Vap, and Mike Borowczak
In Proceedings of the ACM 2022 International World Wide Web Conferences (TheWebConf). Lyon, France. April 25-29. 2022. - Causal Mediation Analysis with Hidden Confounders [pdf]
Lu Cheng, Ruocheng Guo, and Huan Liu
In Proceedings of the 2022 ACM International Conference on Web Search and Data Mining (WSDM). Online Virtual Event. Feb. 21-25. 2022. - Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies [pdf]
Lu Cheng, Ruocheng Guo, and Huan Liu
In Proceedings of the 2022 ACM International Conference on Web Search and Data Mining (WSDM). Online Virtual Event. Feb. 21-25. 2022. - Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication [pdf] [data]
Lu Cheng, Ruocheng Guo, Kasim Selcuk Candan, and Huan Liu
In Proceedings of the 16th International AAAI Conference on Web and Social Media (ICWSM). Atlanta, Georgia. June 6-9. 2022. - Learning Shared Mobility-aware Knowledge for Multiple Urban Travel Demands [pdf]
Qianru Wang, Bin Guo; Yi Ouyang, Lu Cheng, Liang Wang, Zhiwen Yu, and Huan Liu
IEEE Internet of Things Journal. 2021. - Mechanisms and Attributes of Echo Chambers in Social Media [pdf]
Bohan Jiang, Mansooreh Karami, Lu Cheng, Tyler Black, and Huan Liu
SBP-BRiMS Working Paper. 2021. - Socially Responsible AI Algorithms: Issues, Purposes, and Challenges [pdf]
Lu Cheng, Kush R. Varshney, and Huan Liu
Journal of Artificial Intelligence Research. 2021. - Causal Understanding of Fake News Dissemination on Social Media [pdf] [code]
Lu Cheng, Ruocheng Guo, Kai Shu, and Huan Liu
In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’21). Online Virtual Event. August 14-18, 2021. - Mitigating Bias in Session-based Cyberbullying Detection: A Non-Compromising Approach [pdf] [code]
Lu Cheng*, Ahmadreza Mosallanezhad*, Yasin Silva, Deborah Hall, and Huan Liu
In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL), Bangkok, Thailand. August 1-6, 2021. - Causal Learning for Socially Responsible AI [pdf]
Lu Cheng, Ahmadreza Mosallanezhad*, Paras Sheth*, and Huan Liu
In Proceedings of the 2021 International Joint Conferences on Artificial Intelligence (IJCAI2021), August 21-26, Montreal, Canada. - Automated Meta-Analysis in Medical Research: A Causal Learning Perspective [poster]
Lu Cheng, Dmitriy Katz-rogozhnikov, Ioana Baldini, and Kush R. Varshney
In the 2021 ACM Conference on Health, Inference, and Learning Workshop. April 8-9, 2021. - Improving Cyberbullying Detection with User Interaction [pdf] [code]
Suyu Ge, Lu Cheng, and Huan Liu
In Proceedings of the 2021 International World Wide Web Conference, Ljubljana, Slovenia. April 19-23, 2021. - Modeling Temporal Patterns of Cyberbullying Detection with Hierarchical Attention Networks [pdf] [code]
Lu Cheng, Ruocheng Guo, Yasin Silva, Deborah Hall, and Huan Liu
ACM/IMS Transactions on Data Science. 2021. - Long-Term Effect Estimation with Surrogate Representation [pdf] [code]
Lu Cheng, Ruocheng Guo, and Huan Liu
In Proceedings of the 2021 ACM International Conference on Web Search and Data Mining (WSDM), Online, March 08-12, 2021. - Session-based Cyberbullying Detection: Problems and Challenges [pdf]
Lu Cheng, Yasin Silva, Deborah Hall, and Huan Liu
IEEE Internet Computing, Special Issue on Cyber-Social Health: Promoting Good and Countering Harm on Social Media, 2021 - Unsupervised Cyberbullying Detection via Time-Informed Gaussian Mixture Model [pdf] [code]
Lu Cheng, Kai Shu, Siqi Wu, Yasin N. Silva, Deborah Hall, and Huan Liu
In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM’20), Oct. 19-23, 2020, Online. - BullyBlocker: Integrating Data, Computer, and Psychological Science to Identify Cyberbullying on Social Media [poster]
Brittany Wheeler, Lu Cheng, Deborah Hall, and Yasin N. Silva
In 2020 Women in Statistics and Data Science Conference (WSDS’20), Oct. 01-03, 2020, Pittsburgh Marriott City Center, Pittsburgh, PA, USA. - A Survey of Learning Causality with Data: Problems and Methods [pdf]
Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, and Huan Liu
In ACM Computing Surveys (CSUR’20). - Representation Learning for Imbalanced Cross-Domain Classification [pdf]
Lu Cheng, Ruocheng Guo, K.S. Candan, and Huan Liu
In Proceedings of the 2020 SIAM International Conferene on Data Mining (SDM’20), May 07-09, 2020, Cincinnati, Ohio, USA. - Tracking Disaster Footprints with Social Stream Data [pdf]
Lu Cheng, Jundong Li, K.S. Candan, and Huan Liu
In Proceedings of 34th AAAI Conference on Artificial Intelligence (AAAI’20), Feb. 07-12, 2020, New York, New York, USA. - A Practical Data Repository for Causal Learning with Big Data [pdf] [slides]
Lu Cheng, Ruocheng Guo*, Raha Moraffah*, K.S. Candan, Adrienne Raglin, and Huan Liu
In Proceedings of the 2019 BenchCouncil International Symposium on Benchmarking, Measuring and Optimizing (Bench’19), Nov. 14-16, 2019, Denver, Colorado, USA. - PI-Bully: Personalized Cyberbullying Detection with Peer Influence [pdf]
Lu Cheng, Jundong Li, Yasin N. Silva, Deborah Hall, and Huan Liu
In Proceedings of the 2019 International Joint Conferences on Artificial Intelligence (IJCAI2019), Aug. 10-16, Macao, China. - Robust Cyberbullying Detection with Causal Interpretation [pdf] [slides]
Lu Cheng, Ruocheng Guo, and Huan Liu
In Proceedings of the WWW’19 CyberSafety Workshop, May 13-17, San Francisco, U.S. - Hierarchical Attention Networks for Cyberbullying Detection on the Instagram Social Network [pdf] [slides] [media coverage]
Lu Cheng, Ruocheng Guo, Yasin N. Silva, Deborah Hall, and Huan Liu
In Proceedings of the 2019 SIAM International Conferene on Data Mining (SDM19), Calgary, Cananda, May 2-4, 2019 - XBully: Cyberbullying Detection within a Multi-Modal Context. [pdf]
Lu Cheng, Jundong Li, Yasin N. Silva, Deborah Hall, and Huan Liu
In Proceedings of the 2019 ACM International Conference on Web Search and Data Mining (WSDM), Melbourne, Australia, Feb 11-15, 2019 - Personazlied Learning for Cyberbullying Detection [pdf]
Lu Cheng, Yasin N. Silva, Deborah Hall, and Huan Liu
SBP-BRiMS Doctoral Consortium, 2018. - A Multi-objective Immune Genetic Algorithm for Project Scheduling on Multi-skill Resources
Lu Cheng, Guangrui Liao, Zhenyuan Liu
Applied Mechanics and Materials. 2014, Vol. 719-720, p1268-1274. 7p.
