More information can be found in my Google Scholar profile.
* denotes equal contributions.

Recent Preprints

• Towards Uncovering How Large Language Model Works: An Explainability Perspective
  Haiyan Zhao, Fan Yang, Bo Shen, Himabindu Lakkaraju, Mengnan Du
  arXiv:2402.10688, 2024

• Quantifying Multilingual Performance of Large Language Models Across Languages
  Zihao Li, Yucheng Shi, Zirui Liu, Fan Yang, Ninghao Liu, Mengnan Du
  arXiv:2404.11553, 2024

• The Impact of Reasoning Step Length on Large Language Models
  Mingyu Jin, Qinkai Yu, Dong shu, Haiyan Zhao, Wenyue Hua, Yanda Meng, Yongfeng Zhang, Mengnan Du
  arXiv:2401.04925, 2024

• Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective
  Haoyi Xiong, Xuhong Li, Xiaofei Zhang, Jiamin Chen, Xinhao Sun, Yuchen Li, Zeyi Sun, Mengnan Du
  arXiv:2401.04374, 2024

• Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era
  Xuansheng Wu, Haiyan Zhao, Yaochen Zhu, Yucheng Shi, Fan Yang, Tianming Liu, Xiaoming Zhai, Wenlin Yao, Jundong Li, Mengnan Du, Ninghao Liu
  arXiv:2403.08946, 2024

2024

• Mitigating Shortcuts in Language Models with Soft Label Encoding
  Zirui He, Huiqi Deng, Haiyan Zhao, Ninghao Liu, Mengnan Du
  COLING, 2024

• Unveiling Project-Specific Bias in Neural Code Models
  Zhiming Li, Yanzhou Li, Tianlin Li, Mengnan Du, Bozhi Wu, Yushi Cao, Xiaofei Xie, Yi Li, Yang Liu
  COLING, 2024

• An Embarrassingly Simple Key Prompt Protection Mechanism for Large Language Models
  Ruixiang Tang, Yu-Neng Chuang, Xuanting Cai, Mengnan Du, Xia Hu
  NAACL Findings, 2024

• Enhancing Fairness in In-Context Learning: Prioritizing Minority Samples in Demonstrations
  Jingyu Hu, Mengnan Du
  ICLR Tiny Papers Track, 2024

• Fair Machine Learning in Healthcare: A Review
  Qizhang Feng, Mengnan Du, Na Zou, and Xia Hu
  IEEE Transactions on Artificial Intelligence (TAI), 2024

• Understanding and Unifying Fourteen Attribution Methods with Taylor Interactions
  Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Ziwei Yang, Zheyang Li, Quanshi Zhang
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024

• Explaining Time Series via Contrastive and Locally Sparse Perturbations
  Zichuan Liu, Yingying Zhang, Tianchun Wang, Zefan Wang, Dongsheng Luo, Mengnan Du, Min Wu, Yi Wang, Chunlin Chen, Lunting Fan, Qingsong Wen
  The Twelfth International Conference on Learning Representations (ICLR), 2024

2023

• Explainability for Large Language Models: A Survey
  Haiyan Zhao, Hanjie Chen, Fan Yang, Ninghao Liu, Huiqi Deng, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Mengnan Du
  ACM Transactions on Intelligent Systems and Technology (TIST)   [Github]

• Shortcut Learning of Large Language Models in Natural Language Understanding
  Mengnan Du, Fengxiang He, Na Zou, Dacheng Tao, Xia Hu
  Communications of the ACM (CACM), 2023

• Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding
  Mengnan Du, Subho Mukherjee, Yu Cheng, Milad Shokouhi, Xia Hu and Ahmed Hassan Awadallah
  The 17th Annual Meeting of the European chapter of the Association for Computational Linguistics (EACL), 2023
  

• Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases
  Yingji Li, Mengnan Du, Xin Wang and Ying Wang
  The 61st Annual Meeting of the Association for Computational Linguistics (ACL), 2023
  

• Fairness via Group Contribution Matching
  Tianlin Li, Zhiming Li, Anran Li, Mengnan Du, Aishan Liu, Qing Guo, Guozhu Meng, Yang Liu
  The 32nd International Joint Conference on Artificial Intelligence (IJCAI), 2023
  

• FAIRER: Fairness as Decision Rationale Alignment
  Tianlin Li, Qing Guo, Aishan Liu, Mengnan Du, Zhiming Li, Yang Liu
  The 40th International Conference on Machine Learning (ICML), 2023
  

• Black-box Backdoor Defense via Zero-shot Image Purification
  Yucheng Shi, Mengnan Du, Xuansheng Wu, Zihan Guan, Ninghao Liu
  Thirty-Seventh Annual Conference on Neural Information Processing Systems (NeurIPS), 2023

• M⁴: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities and Models
  Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai, Himabindu Lakkaraju, Haoyi Xiong
  NeurIPS Datasets and Benchmarks track, 2023

• Deep Serial Number: Computational Watermarking for DNN Intellectual Property Protection
  Ruixiang Tang, Mengnan Du, and Xia Hu
  Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), 2023

• Mitigating Algorithmic Bias with Limited Annotations
  Guanchu Wang, Mengnan Du, Ninghao Liu, Na Zou, Xia Hu
  Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), 2023

• XGBD: Explanation-Guided Graph Backdoor Detection
  Zihan Guan, Mengnan Du, Ninghao Liu
  26th European Conference on Artificial Intelligence (ECAI), 2023

• Attacking Neural Networks with Neural Networks: Towards Deep Synchronization for Backdoor Attacks
  Zihan Guan, Lichao Sun, Mengnan Du, Ninghao Liu
  Conference on Information and Knowledge Management (CIKM), 2023

• Exposing Model Theft: A Robust and Transferable Watermark for Thwarting Model Extraction Attacks
  Ruixiang Tang, Hongye Jin, Mengnan Du, Curtis Wigington, Rajiv Jain, Xia Hu
  Conference on Information and Knowledge Management (CIKM), short paper, 2023

• Error Detection on Knowledge Graphs with Triple Embedding
  Yezi Liu, Qinggang Zhang, Mengnan Du, Xiao Huang, and Xia Hu
  The 31st European Signal Processing Conference (EUSIPCO), 2023
  

• Proportionate Diversification of Top-k LLM Results using Database Queries
  Thinh On, Subhodeep Ghosh, Mengnan Du, Senjuti Basu Roy
  1st International Workshop on Databases and Large Language Models at VLDB, 2023

2022

• Towards Debiasing DNN Models From Spurious Feature Influence
  Mengnan Du, Ruixiang Tang, Weijie Fu, and Xia Hu
  Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2022
  

• Algorithmic Fairness in Machine Learning
  Mengnan Du, Lu Cheng, Dejing Dou
  Book Chapter, 2022
  

• Accelerating Shapley Explanation via Contributive Cooperator Selection
  Guanchu Wang*, Yu-Neng Chuang*, Mengnan Du, Fan Yang, Quan Zhou, Pushkar Tripathi, Xuanting Cai and Xia Hu
  The 39th International Conference on Machine Learning (ICML), 2022
  

• Learning Disentangled Representations for Time Series
  Yuening Li, Zhengzhang Chen, Daochen Zha, Mengnan Du, Denghui Zhang, Haifeng Chen, Xia Hu
  The 28rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2022
  

• DEGREE: Decomposition Based Explanation for Graph Neural Networks
  Qizhang Feng, Ninghao Liu, Fan Yang, Ruixiang Tang, Mengnan Du, Xia Hu
  The Tenth International Conference on Learning Representations (ICLR), 2022
  

• Generating Consistent Multimodal Dialogue Responses with Emoji Context Model
  Xiangwu Zuo, Xiao Yu, Mengnan Du, Qingquan Song
  in 5th International Conference on Artificial Intelligence and Big Data (ICAIBD), 2022
  

2021

• Fairness via Representation Neutralization
  Mengnan Du, Subhabrata Mukherjee, Guanchu Wang, Ruixiang Tang, Ahmed Hassan Awadallah, Xia Hu
  Thirty-Fifth Conference on Neural Information Processing Systems (NeurIPS), 2021 Code

• Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU models
  Mengnan Du, Varun Manjunatha, Rajiv Jain, Ruchi Deshpande, Franck Dernoncourt, Jiuxiang Gu, Tong Sun and Xia Hu
  North American Chapter of the Association for Computational Linguistics (NAACL), 2021

• Towards Structured NLP Interpretation via Graph Explainers
  Hao Yuan*, Fan Yang*, Mengnan Du*, Shuiwang Ji, Xia Hu
  Applied AI Letters, 2021

• Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution
  Huiqi Deng, Na Zou, Weifu Chen, Guocan Feng, Mengnan Du, and Xia Hu
  The 27rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2021

• Mitigating Gender Bias in Captioning Systems
  Ruixiang Tang, Mengnan Du, Yuening Li, Zirui Liu and Xia Hu
  The Web Conference (WWW), 2021 Code

• A Unified Taylor Framework for Revisiting Attribution Methods
  Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Xia Hu
  AAAI Conference on Artificial Intelligence (AAAI), 2021

• Sub-Architecture Ensemble Pruning in Neural Architecture Search
  Yijun Bian, Qingquan Song, Mengnan Du, Jun Yao, Huanhuan Chen and Xia Hu
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021

• Differentiated Explanation of Deep Neural Networks with Skewed Distributions
  Weijie Fu, Meng Wang, Mengnan Du, Ninghao Liu, Shijie Hao and Xia Hu
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021 Code

• Understanding Social Biases behind Location Names in Contextual Word Embedding Models
  Fangsheng Wu, Mengnan Du, Chao Fan, Ruixiang Tang, Yang Yang, Ali Mostafavi, Xia Hu
  IEEE Transactions on Computational Social Systems (TCSS), 2021

• Adversarial Attacks and Defenses: An Interpretation Perspective
  Ninghao Liu, Mengnan Du, Ruocheng Guo, Huan Liu and Xia Hu
  SIGKDD Explorations, 2021

• Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation
  Fan Yang, Ninghao Liu, Mengnan Du and Xia Hu
  SIGKDD Explorations, 2021

• Machine Learning Explanations to Prevent Overtrust in Fake News Detection
  Sina Mohseni, Fan Yang, Shiva Pentyala, Mengnan Du, Yi Liu, Nic Lupfer, Xia Hu, Shuiwang Ji, and Eric Ragan
  International AAAI Conference on Web and Social Media (ICWSM), 2021

2020

• Techniques for Interpretable Machine Learning
  Mengnan Du, Ninghao Liu, and Xia Hu
  Communications of the ACM (CACM), 2020 Video
  Highlighted on the cover page of the January 2020 issue.

• Fairness in Deep Learning: A Computational Perspective
  Mengnan Du, Fan Yang, Na Zou, and Xia Hu
  IEEE Intelligent Systems, 2020

• Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder
  Mengnan Du*, Shiva Pentyala*, Yuening Li, Xia Hu
  The 29th ACM International Conference on Information and Knowledge Management (CIKM), 2020

• Learning Credible DNNs via Incorporating Prior Knowledge and Model Local Explanation
  Mengnan Du, Ninghao Liu, Fan Yang, and Xia Hu
  Knowledge and Information Systems (KAIS), 2020

• An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks
  Ruixiang Tang, Mengnan Du, Ninghao Liu, Fan Yang, and Xia Hu
  The 26rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020 Code

• Deep Neural Networks with Knowledge Instillation
  Fan Yang, Ninghao Liu, Mengnan Du, Kaixiong Zhou, Shuiwang Ji and Xia Hu
  SIAM International Conference on Data Mining (SDM), 2020

• A Deep Learning Approach for Identifying Cancer Survivors Living with Post-Traumatic Stress Disorder on Twitter
  Nur Hafieza Ismail, Ninghao Liu, Mengnan Du, Zhe He and Xia Hu
  BMC Medical Informatics and Decision Making (BMC), 2020

• Trust Evolution Over Time in Explainable AI for Fake News Detection
  Sina Mohseni, Fan Yang, Shiva Pentyala, Mengnan Du, Yi Liu, Nic Lupfer, Xia Hu, Shuiwang Ji, Eric Ragan
  In CHI Workshop on Fair & Responsible AI, 2020

• Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
  Haofan Wang, Zifan Wang, Mengnan Du, Fan Yang, Zijian Zhang, Sirui Ding, Piotr Mardziel, Xia Hu
  In IEEE CVPR Workshop on Fair, Data-Efficient and Trusted Computer Vision, 2020 Code

2019

• Learning Credible Deep Neural Networks with Rationale Regularization
  Mengnan Du, Ninghao Liu, Fan Yang, and Xia Hu
  IEEE International Conference on Data Mining (ICDM), 2019
  Best Paper Candidate.

• On Attribution of Recurrent Neural Network Predictions via Additive Decomposition
  Mengnan Du, Ninghao Liu, Fan Yang, Shuiwang Ji, and Xia Hu
  The Web Conference (WWW), 2019 Code Slides
  Best Paper Candidate.

• XFake: Explainable Fake News Detector with Visualizations
  Fan Yang*, Shiva Pentyala*, Sina Mohseni*, Mengnan Du*, Hao Yuan*, Rhema Linder, Eric Ragan, Shuiwang Ji and Xia Hu
  The Web Conference (WWW), 2019, demo track

• Representation Interpretation with Spatial Encoding and Multimodal Analytics
  Ninghao Liu, Mengnan Du, Xia Hu
  ACM International Conference on Web Search and Data Mining (WSDM), 2019

• Multivariate Multi-step Deep Learning Time Series Approach in Forecasting Parkinson’s Disease Future Severity Progression
  Nur Hafieza Ismail, Mengnan Du, Diego Martinez and Zhe He
  In The 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (BCB), 2019

• Identification of Cancer Survivors Living with Post-Traumatic Stress Disorder (PTSD) on Social Media
  Nur Hafieza Ismail, Ninghao Liu, Mengnan Du, Zhe He, and Xia Hu
  In The World Congress on Medical and Health Informatics (MEDINFO), poster, 2019

• SpecAE: Spectral Autoencoder for Anomaly Detection in Attributed Networks
  Yuening Li, Xiao Huang, Jundong Li, Mengnan Du, and Na Zou
  ACM International Conference on Information and Knowledge Management (CIKM), 2019
  INFORMS 2019 Best Refereed Paper Finalists.

• Deep Structured Cross-Modal Anomaly Detection
  Yuening Li, Ninghao Liu, Jundong Li, Mengnan Du, Xia Hu
  International Joint Conference on Neural Networks (IJCNN), 2019

• Social Media and Psychological Disorder
  Nur Hafieza Ismail, Mengnan Du, Xia Hu
  Social Web and Health Research, Springer, 2019

• Using Deep Neural Network to Identify Cancer Survivors Living with Post-Traumatic Stress Disorder on Social Media
  Nur Hafieza Ismail, Ninghao Liu, Mengnan Du, Zhe He, and Xia Hu
  In The 4th Interpretable Workshop on Semantics-Powered Data Mining and Analytics (SEPDA), 2019

2018

• Towards Explanation of DNN-based Prediction with Guided Feature Inversion
  Mengnan Du, Ninghao Liu, Qingquan Song, and Xia Hu
  The 24rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2018 Code

2017 and Before

• Supervised Training and Contextually Guided Salient Object Detection
  Mengnan Du, Xingming Wu, Weihai Chen, Zhengguo Li
  Digital signal processing (DSP), 2017

• Exploiting multiple contexts for saliency detection
  Mengnan Du, Xingming Wu, Weihai Chen, Jianhua Wang
  Journal of Electronic Imaging (JEI), 2017

• Salient object detection via region contrast and graph regularization
  Xingming Wu, Mengnan Du, Weihai Chen, Jianhua Wang
  SCIENCE CHINA Information Sciences, 2016

• Exploiting Deep Convolutional Neural Network and Patch-level CRFs for Indoor Semantic Segmentation
  Xingming Wu, Mengnan Du, Weihai Chen, Zhengguo Li
  in IEEE Conference on Industrial Electronics and Applications (ICIEA), 2016

• Fusing region contrast and graph regularization for saliency detection
  Mengnan Du, Xingming Wu, Weihai Chen, Jianhua Wang
  In Chinese Control and Decision Conference (CCDC), 2015

• Attitude control based on complementary filter for two-wheel self-balance car
  Guangsheng Liang, Mengnan Du, Zihao Zhou, et al.
  Measurement Control Technology, 2015 (in chinese)