Welcome

Yaliang Li

@ Alibaba Group

Email: yaliangl.ub at gmail

Work Experiences

  • November 2018 - Now, Alibaba DAMO Academy, Bellevue.

  • Senior Researcher, March 2018 - November 2018, Tencent America, Palo Alto.

  • Research Scientist, January 2017 - March 2018, Baidu Research, Sunnyvale.

  • Research Intern, June 2015 - May 2016, Baidu Research, Sunnyvale.

  • Research Engineer, July 2010 - May 2012, Singapore Management University, Singapore.

Education

  • Ph.D., 2012 - 2017, Computer Science, University at Buffalo. Advisor: Jing Gao

Professional Activities

  • Program Co-Chair:

  • Area Chair

    • NeurIPS'21, AAAI'22, NeurIPS'22

  • Senior Program Committee Member:

    • AAAI'20

  • Program Committee Member:

    • ICLR'22, WSDM'22, SIGIR'22, ARR, KDD'22, CIKM'22, ICML'22

    • ICML'21, ICLR'21, KDD'21, ACL/IJCNLP'21, SIGIR'21, WSDM'21, SDM'21, AAAI'21, EMNLP'21, CIKM'21

    • ICLR'20, KDD'20, ACL'20, IJCAI'20, SIGIR'20, CIKM'20, EMNLP'20, WSDM'20, NeurIPS'20

    • AAAI'19, ICLR'19, KDD'19, ACL'19, EMNLP'19

    • NIPS’18, KDD’18, CIKM’18, PAKDD’18

    • ICDM'17, CIKM'17, PAKDD'17, IJCNLP'17, NLPCC'17, IJCAI'15

  • Journal Reviewer: TKDE, TKDD, TMC, TBD, T-IFS, Neurocomputing, TWEB, KAIS

Awards

  • KDD 2022 Best Paper Award for ADS track

  • KDD Cup 2019 AutoML Track, Third Place

  • KDD Cup 2020 AutoGraph Competition, 4th Place


Publications [Google Scholar] [DBLP]

Preprint

  • Yuexiang Xie, Zhen Wang, Dawei Gao, Daoyuan Chen, Liuyi Yao, Weirui Kuang, Yaliang Li, Bolin Ding, Jingren Zhou. FederatedScope: A Flexible Federated Learning Platform for Heterogeneity. 2022.

https://arxiv.org/abs/2204.05011

Website: https://federatedscope.io/

GitHub: https://github.com/alibaba/FederatedScope

2022

  • A Practical Introduction to Federated Learning. In Proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), tutorial, 2022. https://joneswong.github.io/KDD22FLTutorial/

  • Zhen Wang, Weirui Kuang, Yuexiang Xie, Liuyi Yao, Yaliang Li, Bolin Ding, Jingren Zhou. FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning. In Proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2022. KDD Best Paper Award for ADS track. https://arxiv.org/abs/2204.05562

  • Zhen Wang, Zhewei Wei, Yaliang Li, Weirui Kuang, Bolin Ding. Graph Neural Networks with Node-wise Architecture. In Proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2022.

  • Yupeng Hou, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Bolin Ding, Ji-Rong Wen. Towards Universal Sequence Representation Learning for Recommender Systems. In Proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2022.

  • Dawei Gao, Yuexiang Xie, Zimu Zhou, Zhen Wang, Yaliang Li, Bolin Ding. Finding Meta Winning Ticket to Train Your MAML. In Proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2022.

  • Yuexiang Xie, Zhen Wang, Yaliang Li, Ce Zhang, Jingren Zhou, and Bolin Ding. iFlood: A Stable and Effective Regularizer. In Proceedings of the International Conference on Learning Representations (ICLR), 2022.

  • Yiqing Xie, Zhen Wang, Carl Yang, Yaliang Li, Hongbo Deng, Bolin Ding, and Jiawei Han. KoMen: Domain Knowledge-Guided Few-Shot Interaction Recommendation on Multiplex Networks. In Proceedings of the World Wide Web Conference (WWW), 2022.

  • Shaoyun Shi, Yuexiang Xie, Zhen Wang, Bolin Ding, Yaliang Li, and Min Zhang. Explainable Neural Rule Learning. In Proceedings of the World Wide Web Conference (WWW), 2022.

  • Shanlei Mu, Yaliang Li, Wayne Xin Zhao, Jingyuan Wang, Bolin Ding and Ji-Rong Wen. Alleviating Spurious Correlations in Knowledge-aware Recommendations through Counterfactual Generator. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2022.

  • Changxin Tian, Yuexiang Xie, Yaliang Li, Nan Yang and Wayne Xin Zhao. Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2022.

  • Yupeng Hou, Wayne Xin Zhao, Yaliang Li, and Ji-rong Wen. Privacy-Preserved Neural Graph Similarity Learning. In Proceedings of the International Conference on Data Mining (ICDM), 2022.

  • Jinjia Feng, Zhen Wang, Yaliang Li, Bolin Ding, Zhewei Wei, and HongtengXu. MGMAE: Molecular Representation Learning by ReconstructingHeterogeneous Graphs with A High Mask Ratio. In Proceeding of the ACM International Conference on Information and Knowledge Management (CIKM), 2022.

  • Chenghao Lyu, Qi Fan, Fei Song, Arnab Sinha, Yanlei Diao, Wei Chen, Li Ma, Yihui Feng, Yaliang Li, Kai Zeng, Jingren Zhou. Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing. PVLDB, 2022.

  • Yang Deng, Yaliang Li, Wenxuan Zhang, Bolin Ding, Wai Lam. Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling. ACM Transactions on Information Systems (TOIS), 2022.

  • Hengtong Zhang, Yaliang Li, Bolin Ding, and Jing Gao. LOKI: A Practical Data Poisoning Attack Framework against Next Item Recommendations. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022.

  • Ziniu Wu, Pei Yu, Peilun Yang, Rong Zhu, Yuxing Han, Yaliang Li, Defu Lian, Kai Zeng, Jingren Zhou. A unified transferable model for ML-enhanced DBMS. In Annual Conference on Innovative Data Systems Research (CIDR), 2022.

2021

  • Yaliang Li, Zhen Wang, Bolin Ding, Ce Zhang. AutoML: A Perspective where Industry Meets Academy. In Proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2021. (Tutorial link)

  • Yuexiang Xie, Zhen Wang, Yaliang Li, Bolin Ding, Nezihe Merve Gürel, Ce Zhang, Minlie Huang, Wei Lin, Jingren Zhou. FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data. In Proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2021.

  • Hengtong Zhang, Changxin Tian, Yaliang Li, Lu Su, Nan Yang, Wayne Xin Zhao, Jing Gao. Data Poisoning Attack against Recommender System Using Incomplete and Perturbed Data. In Proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2021.

  • Siqing Li, Liuyi Yao, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Tonglei Guo, Bolin Ding, Ji-Rong Wen. Debiasing Learning based Cross-domain Recommendation. In Proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2021.

  • Yuexiang Xie, Fei Sun, Yang Deng, Yaliang Li, and Bolin Ding. Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP Finding), 2021.

  • Chenhe Dong, Yaliang Li, Ying Shen and Minghui Qiu. HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021.

  • Lingyun Feng, Minghui Qiu, Yaliang Li, Haitao Zheng and Ying Shen. Wasserstein Selective Transfer Learning for Cross-domain Text Mining. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021.

  • Hengtong Zhang, Tianhang Zheng, Yaliang Li, Jing Gao, Lu Su and Bo Li. Profanity-Avoiding Training Framework for Seq2seq Models with Certified Robustness. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021.

  • Xiaoxuan Hu, Hengtong Zhang, Wayne Xin Zhao, Yaliang Li, Jing Gao and Ji-Rong Wen. RAST: A Reward Augmented Model for Fine-Grained Sentiment Transfer. In Natural Language Processing and Chinese Computing (NLPCC), 2021. Best Student Paper Award.

  • Haojie Pan, Chengyu Wang, Minghui Qiu, Yichang Zhang, Yaliang Li, Jun Huang. Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2021.

  • Xiang Yue, Minxin Du, Tianhao Wang, Yaliang Li, Huan Sun, Sherman S. M. Chow. Differential Privacy for Text Analytics via Natural Text Sanitization. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL Finding), 2021.

  • Kun Zhou, Xiaolei Wang, Yuanhang Zhou, Chenzhan Shang, Yuan Cheng, Wayne Xin Zhao, Yaliang Li, Ji-Rong Wen. CRSLab: An Open-Source Toolkit for Building Conversational Recommender System. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL Demo), 2021. (GitHub link)

  • Yang Li, Yu Shen, Wentao Zhang, Jiawei Jiang, Yaliang Li, Bolin Ding, Jingren Zhou, Zhi Yang. Wentao Wu, Ce Zhang, Bin Cui. VolcanoML: Speeding up End-​to-End AutoML via Scalable Search Space Decomposition. PVLDB, 2021.

  • Yang Deng, Yaliang Li, Fei Sun, Bolin Ding, Wai Lam. Unified Conversational Recommendation Policy Learning via Graph-enhanced Reinforcement Learning. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2021.

  • Ronghang Zhu, Zhiqiang Tao, Yaliang Li, and Sheng Li. Automated Graph Learning via Population Based Self-Tuning GCN. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR short), 2021.

  • Yaliang Li, Zhen Wang, Yuexiang Xie, Bolin Ding, Kai Zeng and Ce Zhang. AutoML: From Methodology to Application. In Proceeding of the ACM International Conference on Information and Knowledge Management (CIKM), 2021. (Tutorial link)

  • Liuyi Yao, Yaliang Li, Sheng Li, Mengdi Huai, Jing Gao and Aidong Zhang. SCI: Subspace Learning Based Counterfactual Inference for Individual Treatment Effect Estimation. In Proceeding of the ACM International Conference on Information and Knowledge Management (CIKM), 2021.

  • Minghui Qiu, Peng Li, Chengyu Wang, Haojie Pan, An Wang, Cen Chen, Xianyan Jia, Yaliang Li, Jun Huang, Deng Cai and Wei Lin. EasyTransfer: A Simple and Scalable Deep Transfer Learning Platform for NLP Applications. In Proceeding of the ACM International Conference on Information and Knowledge Management (CIKM), 2021.

  • Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Kaiyuan Li, Yushuo Chen, Yujie Lu, Hui Wang, Changxin Tian, Xingyu Pan, Yingqian Min, Zhichao Feng, Xinyan Fan, Xu Chen, Pengfei Wang, Wendi Ji, Yaliang Li, Xiaoling Wang and Ji-Rong Wen. RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms. In Proceeding of the ACM International Conference on Information and Knowledge Management (CIKM), 2021.

  • Liuyi Yao, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, and Aidong Zhang. A Survey on Causal Inference. ACM Transactions on Knowledge Discovery from Data (TKDD), 2021.

  • Yang Deng, Yuexiang Xie, Yaliang Li, Min Yang, Wai Lam, Ying Shen. Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge. ACM Transactions on Information Systems (TOIS), 2021.

  • Siqing Li, Yaliang Li, Wayne Xin Zhao, Bolin Ding, and Ji-Rong Wen. Interpretable Aspect-aware Capsule Network for Peer Review based Citation Count Prediction. ACM Transactions on Information Systems (TOIS), 2021.

  • Shanlei Mu, Yaliang Li, Wayne Xin Zhao, Siqing Li, and Ji-Rong Wen. Knowledge-Guided Disentangled Representation Learning for Recommender Systems. ACM Transactions on Information Systems (TOIS), 2021.

  • Lingyun Feng, Minghui Qiu, Yaliang Li, Hai-Tao Zheng, Ying Shen. Learning to Augment for Data-Scarce Domain BERT Knowledge Distillation. The AAAI Conference on Artificial Intelligence (AAAI), 2021.

2020

  • Zhiqiang Tao, Yaliang Li, Bolin Ding, Ce Zhang, Jingren Zhou, Yun Fu. Learning to Mutate with Hypergradient Guided Population. Neural Information Processing Systems (NeurIPS), 2020.

  • Ming Chen, Zhewei Wei, Bolin Ding, Yaliang Li, Ye Yuan, Xiaoyong Du, Ji-Rong Wen. Scalable Graph Neural Networks via Bidirectional Propagation. Neural Information Processing Systems (NeurIPS), 2020.

  • Peng Cui, Zheyan Shen, Sheng Li, Liuyi Yao, Yaliang Li, Zhixuan Chu and Jing Gao. Causal Inference Meets Machine Learning. In Proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020. (Tutorial link)

  • Daoyuan Chen, Yaliang Li, Bolin Ding, and Ying Shen. An Adaptive Embedding Framework for Heterogeneous Information Networks. In Proceeding of the ACM International Conference on Information and Knowledge Management (CIKM'20), 2020.

  • Daoyuan Chen, Yaliang Li (co-first author), Minghui Qiu, Zhen Wang, Bofang Li, Bolin Ding, Hongbo Deng, Jun Huang, Wei Lin, Jingren Zhou. AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2020.

  • Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, and Philip S. Yu. Entity Synonyms Discovery via Multipiece Bilateral Context Matching. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2020.

  • Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. Simple and Deep Graph Convolutional Networks. In Proceedings of the International Conference on Machine Learning (ICML), 2020.

  • Daoyuan Chen, Yaliang Li, Kai Lei, and Ying Shen. Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2020.

  • Ruiyang Ren, Zhaoyang Liu, Yaliang Li, Wayne Xin Zhao, Hui Wang, Bolin Ding, and Ji-Rong Wen. Sequential Recommendation with Self-attentive Multi-adversarial Network. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020.

  • Yang Deng, Wenxuan Zhang, Yaliang Li, Min Yang, Wai Lam and Ying Shen. Bridging Hierarchical and Sequential Context Modeling for Question-driven Extractive Answer Summarization. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR short), 2020.

  • Sheng Li, Liuyi Yao, Yaliang Li, Jing Gao and Aidong Zhang. Representation Learning for Causal Inference. The AAAI Conference on Artificial Intelligence (AAAI), Tutorial, 2020. (Tutorial link)

  • Yaliang Li, Houping Xiao, Zhan Qin, Chenglin Miao, Lu Su, Jing Gao, Kui Ren, and Bolin Ding. Towards Differentially Private Truth Discovery for Crowd Sensing Systems. In Proceedings of the International Conference on Distributed Computing Systems (ICDCS), 2020.

  • Ying Shen, Ning Ding, Hai-Tao Zheng, Yaliang Li, and Min Yang. Modeling Relation Paths for Knowledge Graph Completion. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020.

  • 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.

  • Sheng Shen, Yaliang Li, Nan Du, Xian Wu, Yusheng Xie, Shen Ge, Tao Yang, Kai Wang, Xingzheng Liang, and Wei Fan. On the Generation of Medical Question-Answer Pairs. The AAAI Conference on Artificial Intelligence (AAAI), 2020.

  • Yuexiang Xie, Ying Shen, Yaliang Li, Min Yang, and Kai Lei. Attentive User-Engaged Adversarial Neural Network for Community Question Answering. The AAAI Conference on Artificial Intelligence (AAAI), 2020.

  • Yang Deng, Wai Lam, Yuexiang Xie, Daoyuan Chen, Yaliang Li, Min Yang, and Ying Shen. Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering. The AAAI Conference on Artificial Intelligence (AAAI), 2020.

  • Hengtong Zhang, Yaliang Li, Bolin Ding, and Jing Gao. Practical Data Poisoning Attack Against Next-Item Recommendation. In Proceedings of the World Wide Web Conference (WWW), 2020.

  • Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li, and Zhenhui Li. Automated Relational Meta-learning. In Proceedings of the International Conference on Learning Representations (ICLR), 2020.

2019

  • Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, and Aidong Zhang. ACE: Adaptively Similarity-preserved Representation Learning for Individual Treatment Effect Estimation. In Proceedings of the International Conference on Data Mining (ICDM’19), Short paper, 2019.

  • Yang Deng, Yaliang Li, Nan Du, Wei Fan, Ying Shen, Min Yang, and Kai Lei. MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data. In Proceeding of the ACM International Conference on Information and Knowledge Management (CIKM'19), Long paper, 2019.

  • Daoyuan Chen, Yaliang Li, Min Yang, Hai-Tao Zheng, and Ying Shen. Knowledge-aware Textual Entailment with Graph Attention Network. In Proceeding of the ACM International Conference on Information and Knowledge Management (CIKM'19), Short paper, 2019.

  • Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, and Philip S. Yu. Joint Slot Filling and Intent Detection via Capsule Neural Networks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), 2019.

  • Congying Xia, Chenwei Zhang, Tao Yang, Yaliang Li, Nan Du, Xian Wu, Wei Fan, Fenglong Ma, and Philip S. Yu. Multi-grained Named Entity Recognition. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), 2019.

  • Bo Wang, Minghui Qiu, Xisen Wang, Yaliang Li, Yu Gong, Xiaoyi Zeng, Jun Huang, Bo Zheng, Deng Cai, and Jingren Zhou. A Minimax Game for Instance based Selective Transfer Learning. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019.

  • Hengtong Zhang, Tianhang Zheng, Jing Gao, Chenglin Miao, Lu Su, Yaliang Li, and Kui Ren. Towards Data Poisoning Attack against Knowledge Graph Embedding. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2019.

  • Liuyi Yao, Sheng Li, Yaliang Li, Hongfei Xue, Jing Gao, and Aidong Zhang. On the Estimation of Treatment Effect with Text Covariates. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2019.

  • Chenglin Miao, Wenjun Jiang, Lu Su, Yaliang Li, Suxin Guo, Zhan Qin, Houping Xiao, Jing Gao, and Kui Ren. Privacy-Preserving Truth Discovery in Crowd Sensing Systems. ACM Transactions on Sensor Networks (TOSN), Vol.15, Issue 1, No.9, 2019.

  • Daoyuan Chen, Hai-Tao Zheng, Min Yang, Yaliang Li , and Ying Shen. Answer-enhanced Path-aware Relation Detection over Knowledge Base. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR short), 2019.

  • Fenglong Ma, Yaliang Li, Chenwei Zhang, Jing Gao, Nan Du, and Wei Fan. MCVAE: Margin-based Conditional Variational Autoencoder for Relation Classification and Pattern Generation. In Proceedings of the World Wide Web Conference (WWW short), 2019.

  • Chaochun Liu, Yaliang Li, Hongliang Fei, and Ping Li. Deep Skip-Gram Networks for Text Classification. In Proceedings of the SIAM International Conference on Data Mining (SDM), 2019.

  • Liuyi Yao, Yaliang Li, Yezheng Li, Hengtong Zhang, Mengdi Huai, Jing Gao, and Aidong Zhang. DTEC: Distance Transformation Based Early Time Series Classification. In Proceedings of the SIAM International Conference on Data Mining (SDM), 2019.

  • Ying Shen, Desi Wen, Yaliang Li, Nan Du, Hai-tao Zheng, and Min Yang. Path-based Attribute-aware Representation Learning for Relation Prediction. In Proceedings of the SIAM International Conference on Data Mining (SDM), 2019.

  • Yang Deng, Yuexiang Xie, Yaliang Li, Min Yang, Nan Du, Wei Fan, Kai Lei, and Ying Shen. Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering, The AAAI Conference on Artificial Intelligence (AAAI), 2019.

2018

  • Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, and Aidong Zhang. Representation Learning for Treatment Effect Estimation from Observational Data. The Thirty-second Annual Conference on Neural Information Processing Systems (NIPS) , 2018.

  • Yaliang Li, Chenglin Miao, Lu Su, Jing Gao, Qi Li, Bolin Ding, Zhan Qin, and Kui Ren. An Efficient Two-Layer Mechanism for Privacy-Preserving Truth Discovery. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’18), 2018.

  • Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, and Philip S. Yu. On the Generative Discovery of Structured Medical Knowledge. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’18), 2018.

  • Hengtong Zhang, Yaliang Li, Fenglong Ma, Jing Gao, and Lu Su. TextTruth: An Unsupervised Approach to Discover Trustworthy Information from Multi-Sourced Text Data. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’18), 2018.

  • Mengdi Huai, Chenglin Miao, Yaliang Li, Qiuling Suo, Lu Su, and Aidong Zhang. Metric Learning from Probabilistic Labels. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’18), 2018.

  • Kai Lei, Daoyuan Chen, Yaliang Li, Nan Du, Min Yang, Wei Fan, and Ying Shen. Cooperative Denoising for Distantly Supervised Relation Extraction. In Proceedings of the International Conference on Computational Linguistics (COLING’18), 2018.

  • Yang Deng, Ying Shen, Min Yang, Yaliang Li, Nan Du, Wei Fan, and Kai Lei. Knowledge as A Bridge: Improving Cross-domain Answer Selection with External Knowledge. In Proceedings of the International Conference on Computational Linguistics (COLING’18), 2018.

  • Hengtong Zhang, Fenglong Ma, Yaliang Li, Chao Zhang, Tianqi Wang, Yaqing Wang, Jing Gao, Lu Su. Leveraging the Power of Informative Users for Local Event Detection. In Proceedings of the International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2018), 2018.

  • Ying Shen, Daoyuan Chen, Min Yang, Yaliang Li, Nan Du, and Kai Lei. Ontology Evaluation with Path-based Text-aware Entropy Computation. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’18 short), 2018.

  • Ying Shen, Yang Deng, Min Yang, Yaliang Li, Nan Du, Wei Fan, and Kai Lei. Knowledge-aware Attentive Neural Network for Ranking Question Answer Pairs. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’18 short), 2018.

  • Liuyi Yao, Lu Su, Qi Li, Yaliang Li, Fenglong Ma, Jing Gao, and Aidong Zhang. Online Truth Discovery on Time Series Data. In Proceedings of the SIAM International Conference on Data Mining (SDM’2018), 2018.

  • Mengdi Huai, Chenglin Miao, Qiuling Suo, Yaliang Li, Jing Gao, and Aidong Zhang. Uncorrelated Patient Similarity Learning. In Proceedings of the SIAM International Conference on Data Mining (SDM’2018), 2018.

2017 and before

  • Guangxu Xun, Kishlay Jha, Vishrawas Gopalakrishnan, Yaliang Li, and Aidong Zhang. Generating Medical Hypotheses Based on Evolutionary Medical Concepts. In Proceedings of the International Conference on Data Mining (ICDM’17), pp. 535-544, 2017.

  • Chenwei Zhang, Wei Fan, Nan Du, Yaliang Li, Chun-Ta Lu, and Philip S. Yu. Bringing Semantic Structures to User Intent Detection in Online Medical Queries. In Proceedings of the International Conference on Big Data (BigData’17), 2017

  • Guangxu Xun, Yaliang Li, Jing Gao, and Aidong Zhang. Collaboratively Improving Topic Discovery and Word Embeddings by Coordinating Global and Local Contexts. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'17), 2017.

  • Chenglin Miao, Lu Su, Wenjun Jiang, Yaliang Li, Miaomiao Tian. A Lightweight Privacy-Preserving Truth Discovery Framework for Mobile Crowd Sensing Systems. In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM 2017), 2017.

  • Guangxu Xun, Yaliang Li, Wayne Xin Zhao, Jing Gao, and Aidong Zhang. A correlated topic model using word embeddings. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'17), 2017.

  • Yaliang Li, Nan Du, Chaochun Liu, Yusheng Xie, Wei Fan, Qi Li, Jing Gao, and Huan Sun. Reliable Medical Diagnosis from Crowdsourcing: Discover Trustworthy Answers from Non-Experts. In Proceeding of the International Conference on Web Search and Data Mining (WSDM'17), 2017.

  • Yaliang Li, Chaochun Liu, Nan Du, Wei Fan, Qi Li, Jing Gao, Chenwei Zhang, and Hao Wu. Extracting Medical Knowledge from Crowdsourced Question Answering Website. IEEE Transactions on Big Data, accepted, Sep 2016.

  • Guangxu Xun, Vishrawas Gopalakrishnan, Fenglong Ma, Yaliang Li, Jing Gao, and Aidong Zhang. Topic Discovery for Short Texts Using Word Embeddings. In Proceeding of the IEEE International Conference on Data Mining series (ICDM'16), 2016.

  • Chaochun Liu, Huan Sun, Nan Du, Sulong Tan, Hongliang Fei, Wei Fan, Tao Yang, Hao Wu, Yaliang Li, and Chenwei Zhang. An Augmented LSTM Framework to Construct Medical Self-diagnosis Android. In Proceeding of the IEEE International Conference on Data Mining series (ICDM'16), 2016.

  • Yaliang Li, Jing Gao, Patrick P. C. Lee, Lu Su, Caifeng He, Cheng He, Fan Yang, and Wei Fan. A Weighted Crowdsourcing Approach for Network Quality Measurement in Cellular Data Networks. IEEE Transactions on Mobile Computing, accepted, March 2016.

  • Yaliang Li, Qi Li (co-first author), Jing Gao, Lu Su, Bo Zhao, Wei Fan, and Jiawei Han. Conflicts to Harmony: A Framework for Resolving Conflicts in Heterogeneous Data by Truth Discovery. IEEE Transactions on Knowledge and Data Engineering, Vol.28, No.8, pp.1986-1999, 2016.

  • Sheng Li, Yaliang Li, and Yun Fu. Multi-View Time Series Classification: A Discriminative Bilinear Projection Approach. In Proceeding of the 25th ACM International Conference on Information and Knowledge Management (CIKM'16), 2016.

  • Chenwei Zhang, Sihong Xie, Yaliang Li, Jing Gao, Wei Fan, and Philip S. Yu. Multi-source Hierarchical Prediction Consolidation. In Proceeding of the 25th ACM International Conference on Information and Knowledge Management (CIKM'16), 2016.

  • Hengtong Zhang, Qi Li, Fenglong Ma, Houping Xiao, Yaliang Li, Jing Gao, and Lu Su. Influence-Aware Truth Discovery. In Proceeding of the 25th ACM International Conference on Information and Knowledge Management (CIKM'16), 2016.

  • Yaliang Li, Jing Gao, Chuishi Meng, Qi Li, Lu Su, Bo Zhao, Wei Fan, and Jiawei Han. A Survey on Truth Discovery. ACM SIGKDD Explorations Newsletter, 17(2):1-16, 2015.

  • Chuishi Meng, Wenjun Jiang, Yaliang Li, Jing Gao, Lu Su, Hu Ding, and Yun Cheng. Truth Discovery on Crowd Sensing of Correlated Entities. In Proceeding of the ACM Conference on Embedded Networked Sensor Systems (SenSys'15), 2015.

  • Chenglin Miao, Wenjun Jiang, Lu Su, Yaliang Li, Suxin Guo, Zhan Qin, Houping Xiao, Jing Gao, and Kui Ren. Cloud-Enabled Privacy-Preserving Truth Discovery in Crowd Sensing Systems. In Proceeding of the ACM Conference on Embedded Networked Sensor Systems (SenSys'15), 2015.

  • Yaliang Li, Qi Li, Jing Gao, Lu Su, Bo Zhao, Wei Fan, and Jiawei Han. On the Discovery of Evolving Truth. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'15), 2015.

  • Fenglong Ma, Yaliang Li, Qi Li, Minghui Qui, Jing Gao, Shi Zhi, Lu Su, Bo Zhao, Heng Ji, and Jiawei Han. FaitCrowd: Fine Grained Truth Discovery for Crowdsourced Data Aggregation. In Proceeding of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'15), 2015.

  • Qi Li, Yaliang Li, Jing Gao, Lu Su, Bo Zhao, Murat Demirbas, Wei Fan, and Jiawei Han. A Confidence-Aware Approach for Truth Discovery on Long-Tail Data. In Proceedings of the International Conference on Very Large Data Bases (VLDB'15), 8(4): 425-436, 2015.

  • Houping Xiao, Yaliang Li, Jing Gao, Fei Wang, Liang Ge, Wei Fan, Long Vu, Deepak Turaga. Believe It Today or Tomorrow? Detecting Untrustworthy Information from Dynamic Multi-Source Data. In Proceedings of the SIAM International Conference on Data Mining (SDM'15), 2015.

  • Yaliang Li, Jing Gao, Qi Li, and Wei Fan. Ensemble Learning. In Charu C. Aggarwal (Eds.), Data Classification: Algorithms and Applications, CRC Press, 2014.

  • Bahadir Aydin, Yavuz Yilmaz, Yaliang Li, Qi Li, Jing Gao, and Murat Demirbas. Crowdsourcing for multiple-choice question answering. In Proceedings of the Conference on Innovative Applications of Artificial Intelligence (IAAI’14), 2014.

  • Qi Li, Yaliang Li (co-first author), Jing Gao, Bo Zhao, Wei Fan, and Jiawei Han. Resolving Conflicts in Heterogeneous Data by Truth Discovery and Source Reliability Estimation. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’14), pp. 1187-1198, 2014.

  • Minghui Qiu, Yaliang Li, and Jing Jiang. Query-oriented Keyphrase Extraction. In Proceedings of the 8th Asia Information Retrieval Societies Conference (AIRS’2012), pp. 64-75, 2012. (regular paper)

  • Yaliang Li, Jing Jiang, Hai Leong Chieu, and Kian Ming A. Chai. Extracting Relation Descriptors with Conditional Random Fields. In Proceedings of the 5th International Joint Conference on Natural Language Processing (IJCNLP'2011), pp. 392-400, 2011. (oral presentation)

  • Forrest Sheng Bao, Yaliang Li, Jue-Ming Gao, and Jin Hu. Performance of Dynamic Features in Classifying Scalp Epileptic Interictal and Normal EEG. In Proceedings of 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'2010), pp. 6308-6311, 2010.

  • Yaliang Li, Fei Ding, and Yu-Xuan Wang. Iterated Function System Based Adaptive Differential Evolution Algorithm. In Proceedings of IEEE Congress on Evolutionary Computation (CEC'2008), pp. 1290-1294, 2008.