Mengdi Huai

• Wei Qian*, Chenxu Zhao*, Wei Le, Meiyi Ma, and Mengdi Huai, "Towards Understanding and Enhancing Robustness of Deep Learning Models against Malicious Unlearning Attacks", the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2023), Long Beach, USA, August 2023. (* indicates equal contribution) [PDF]
  
• Cheng-Long Wang, Mengdi Huai, and Di Wang, "Inductive Graph Unlearning", the 32nd USENIX Security Symposium (USENIX Security 2023), Anaheim, USA, August 2023. [PDF]
  
• Liuyi Yao, Yaliang Li, Bolin Ding, Jingren Zhou, Jinduo Liu, Mengdi Huai, and Jing Gao, "Path-specific Causal Fair Prediction via Auxiliary Graph Structure Learning", the Web Conference (WWW 2023), Austin, USA, April 2023. [PDF]
  
• Guangtao Zheng, Qiuling Suo, Mengdi Huai, and Aidong Zhang, "Learning to Learn Task Transformations for Improved Few-Shot Classification", the 2023 SIAM International Conference on Data Mining (SDM 2023), Minneapolis, USA, April 2023. [PDF]
  
• Sanchit Sinha, Mengdi Huai, Jianhui Sun, and Aidong Zhang, "Understanding and Enhancing Robustness of Concept-based Models", the 37th AAAI Conference on Artificial Intelligence (AAAI 2023), Washington DC, USA, February, 2023. [PDF]
  
• Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, and Di Wang, "SEAT: Stable and Explainable Attention", the 37th AAAI Conference on Artificial Intelligence (AAAI 2023), Washington DC, USA, February, 2023. [PDF]
  
• Wei Qian, Chenxu Zhao, Huajie Shao, Minghan Chen, Fei Wang, and Mengdi Huai, "Patient Similarity Learning with Selective Forgetting", the 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2022), Las Vegas, USA, December 2022. [PDF]
  
• Yilin Lu, Jinduo Liu, Junzhong Ji, Han Lv, and Mengdi Huai, "Brain Effective Connectivity Learning with Deep Reinforcement Learning", the 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2022), Las Vegas, USA, December 2022. [PDF]
  
• Liuyi Yao, Yaliang Li, Sheng Li, Jinduo Liu, Mengdi Huai, Aidong Zhang, and Jing Gao, "Concept-Level Model Interpretation from the Causal Aspect", accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE). [PDF]
  
• Jianhui Sun, Mengdi Huai, Kishlay Jha, and Aidong Zhang, "Demystify Hyperparameters for Stochastic Optimization with Transferable Representations", the 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2022), Washington DC, USA, August 2022. [PDF]
  
• Mengdi Huai, Jinduo Liu, Chenglin Miao, Liuyi Yao, and Aidong Zhang, "Towards Automating Model Explanations with Certified Robustness Guarantees", the 36th AAAI Conference on Artificial Intelligence (AAAI 2022), Vancouver, Canada, February 2022. [PDF]
  
• Mengdi Huai, Tianhang Zheng, Chenglin Miao, Liuyi Yao, and Aidong Zhang, "On the Robustness of Metric Learning: An Adversarial Perspective", ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 16, No. 5, 2022. [PDF]
  
• Yi Zhu, Chenglin Miao, Foad Hajiaghajani, Mengdi Huai, Lu Su, and Chunming Qiao, "Adversarial Attacks against LiDAR Semantic Segmentation in Autonomous Driving", the 19th ACM Conference on Embedded Networked Sensor Systems (SenSys 2021), November 2021. [PDF]
  
• Liuyi Yao, Yaliang Li, Sheng Li, Mengdi Huai, Aidong Zhang, and Jing Gao, "SCI: Subspace Learning Based Counterfactual Inference for Individual Treatment Effect Estimation", the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021), November 2021. [PDF]
  
• Zhiyu Xue, Shaoyang Yang, Mengdi Huai, and Di Wang, "Differentially Private Pairwise Learning Revisited", the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021), August 2021. [PDF]
  
• Mengdi Huai, Chenglin Miao, Jinduo Liu, Di Wang, Jingyuan Chou, and Aidong Zhang, "Global Interpretation for Patient Similarity Learning", the 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2020), Online, December, 2020. [PDF]
  
• Jingyuan Chou, Stefan Bekiranov, Chongzhi Zang, Mengdi Huai, and Aidong Zhang, "Analysis of Meta-Learning Approaches for TCGA Pan-cancer Datasets", the 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2020), Online, December, 2020. [PDF]
  
• Mengdi Huai, Jianhui Sun, Renqin Cai, Liuyi Yao, and Aidong Zhang, "Malicious Attacks against Deep Reinforcement Learning Interpretations", the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2020), San Diego, USA, August, 2020. (Best Paper Runner-up). [PDF]
  
• Mengdi Huai, Di Wang, Chenglin Miao, and Aidong Zhang, "Towards Interpretation of Pairwise Learning", the 34th AAAI Conference on Artificial Intelligence (AAAI 2020), New York, USA, February 2020. [PDF]
  
• Mengdi Huai, Di Wang (co-first author), Chenglin Miao, Jinhui Xu, and Aidong Zhang, "Pairwise Learning with Differential Privacy Guarantees", the 34th AAAI Conference on Artificial Intelligence (AAAI 2020), New York, USA, February 2020. [PDF]
  
• Jinduo Liu, Junzhong Ji, Guangxu Xun, Liuyi Yao, Mengdi Huai, and Aidong Zhang, "EC-GAN: Inferring Brain Effective Connectivity via Generative Adversarial Networks", the 34th AAAI Conference on Artificial Intelligence (AAAI 2020), New York, USA, February 2020. [PDF]
  
• Mengdi Huai, Chenglin Miao, Yaliang Li, Qiuling Suo, Lu Su, and Aidong Zhang, "Learning Distance Metrics from Probabilistic Information", ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 14, No. 5, 2020. [PDF]
  
• Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, and Aidong Zhang, "ACE: Adaptively Similarity-preserved Representation Learning for Individual Treatment Effect Estimation", the 19th IEEE International Conference on Data Mining (ICDM 2019), Beijing, China, November 2019. [PDF]
  
• Mengdi Huai, Di Wang, Chenglin Miao, Jinhui Xu, and Aidong Zhang, "Privacy-aware Synthesizing for Crowdsourced Data", the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), Macao, China, August 2019. [PDF]
  
• Mengdi Huai, Hongfei Xue, Chenglin Miao, Liuyi Yao, Lu Su, Changyou Chen, and Aidong Zhang, "Deep Metric Learning: The Generalization Analysis and an Adaptive Algorithm", the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), Macao, China, August 2019. [PDF]
  
• Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, and Aidong Zhang, "DTEC: Distance Transformation Based Early Time Series Classification", the 2019 SIAM International Conference on Data Mining (SDM 2019), Alberta, Canada, May 2019. [PDF]
  
• Mengdi Huai, Chenglin Miao, Yaliang Li, Qiuling Suo, Lu Su, and Aidong Zhang, "Metric Learning from Probabilistic Labels", the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), London, United Kingdom, August 2018. [PDF]
  
• Chenglin Miao, Qi Li, Lu Su, Mengdi Huai, Wenjun Jiang, and Jing Gao, "Attack under Disguise: An Intelligent Data Poisoning Attack Mechanism in Crowdsourcing", the World Wide Web Conference (WWW 2018), Lyon, France, April 2018. [PDF]
  
• Mengdi Huai, Chenglin Miao, Qiuling Suo, Yaliang Li, Jing Gao, and Aidong Zhang, "Uncorrelated Patient Similarity Learning", the 18th SIAM International Conference on Data Mining (SDM 2018), San Diego, USA, May 2018. [PDF]
  
• Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, and Aidong Zhang, "Representation Learning for Treatment Effect Estimation from Observational Data", the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montreal, Canada, December 2018. [PDF]
  
• Qiuling Suo, Weida Zhong, Fenglong Ma, Ye Yuan, Mengdi Huai, and Aidong Zhang, "Multi-Task Sparse Metric Learning for Monitoring Patient Similarity Progression", the 18th IEEE International Conference on Data Mining (ICDM 2018), Singapore, November, 2018. [PDF]
  
• Chenglin Miao, Qi Li, Houping Xiao, Wenjun Jiang, Mengdi Huai, and Lu Su, "Towards Data Poisoning Attacks in Crowd Sensing Systems", the 19th ACM Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 2018), Los Angeles, USA, June 2018. (Best Paper Award Nominee). [PDF]
  
• Di Wang, Mengdi Huai, and Jinhui Xu, "Differentially Private Sparse Inverse Covariance Estimation", the 6th IEEE Global Conference on Signal and Information Processing (GlobalSip 2018), California, USA, November, 2018. [PDF]
  
• Qiuling Suo, Fenglong Ma, Ye Yuan, Mengdi Huai, Weida Zhong, Jing Gao, and Aidong Zhang, "Deep Patient Similarity Learning for Personalized Healthcare", IEEE Transactions on NanoBioscience (TNB), Vol. 17, No. 3, 2018. [PDF]
  
• Qiuling Suo, Fenglong Ma, Ye Yuan, Mengdi Huai, Weida Zhong, and Aidong Zhang, "Personalized Disease Prediction Using A CNN-Based Similarity Learning Method", the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017), Kansas City, MO, USA, November, 2017. [PDF]
  
• Mengdi Huai, Liusheng Huang, Wei Yang, Lu Li, and Mingyu Qi, "Privacy-preserving Naive Bayes Classification", the 8th International Conference on Knowledge Science, Engineering and Management (KSEM 2015), Chongqing, China, October 2015. [PDF]
  
• Mengdi Huai, Liusheng Huang, Yu-e Sun, and Wei Yang, "Efficient Privacy-Preserving Aggregation for Mobile Crowdsensing", the IEEE 5th International Conference on Big Data and Cloud Computing (BDCloud 2015), Dalian, China, August 2015. [PDF]
  
• Jun Wang, Shangfei Wang, Mengdi Huai, Chongliang Wu, Zhen Gao, Yue Liu and Qiang Ji "Capture Expression-dependent AU Relations for Expression Recognition", the IEEE International Conference on Multimedia and Expo Workshops (ICMEW 2014), Chengdu, China, July 2014. [PDF]