Man must explore

Research Interests

• Machine Learning/ Deep Learning
  • Representation Learning
  • Multimodal Learning
  • Graph Neural Networks
  • Transformer Models
  • Memory Networks
• Natural Language Processing
  • Large Language Models
  • Question Answering
  • Natural Language Generation
  • Text Representation
  • Knowledge Graphs
  • Topic Modeling

Publications

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Preprints

1. Vibhor Agarwal, Yu Chen and Nishanth Sastry, Haterephrase: Zero- and Few-Shot Reduction of Hate Intensity in Online Posts using Large Language Models.
2. Vibhor Agarwal, Yu Chen and Nishanth Sastry, GASCOM: Graph-based Attentive Semantic Context Modeling for Online Conversation Understanding.
3. Arka Sadhu, Licheng Yu, Animesh Sinha, Yu Chen, Ram Nevatia and Ning Zhang, Unaligned Video-Text Pre-training using Iterative Alignment.

Conference Publications

1. [NAACL 2024] Chi Han, Qifan Wang, Hao Peng, Wenhan Xiong, Yu Chen, Heng Ji and Sinong Wang, LM-Infinite: Zero-Shot Extreme Length Generalization for Large Language Models. In Proceedings of the 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Mexico City, Mexico, Jun. 16–21, 2024. (Outstanding Paper Award) [PDF]
2. [ICLR 2024] Xiaotian Han, Jianfeng Chi, Yu Chen, Qifan Wang, Han Zhao, Na Zou and Xia Hu, FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods. In Proceedings of the 12th International Conference on Learning Representations, Vienna, Austria, May 7-11, 2024. 
3. [EALC 2024] Menglong Yao, Sijia Wang, Yu Chen, Qifan Wang, Minqian Liu, Zhiyang Xu, Licheng Yu and Lifu Huang, AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributes. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics, Malta, Mar. 17-22, 2024. 
4. [EMNLP 2023] Harman Singh, Pengchuan Zhang, Qifan Wang, Mengjiao MJ Wang, Wenhan Xiong, Jingfei Du and Yu Chen, Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Singapore, Dec. 6-10, 2023. [PDF]
5. [ACL 2023] Li Yang, Qifan Wang, Jingang Wang, Xiaojun Quan, Fuli Feng, Yu Chen, Madian Khabsa, Sinong Wang, Zenglin Xu and Dongfang Liu, MixPAVE: Mix-Prompt Tuning for Few-shot Product Attribute Value Extraction. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Toronto, Canada, July 9-14, 2023. [PDF]
6. [SIGKDD 2022] Licheng Yu, Jun Chen, Animesh Sinha, Mengjiao MJ Wang, Yu Chen, Tamara L. Berg and Ning Zhang, CommerceMM: Large-Scale Commerce MultiModal Representation Learning with Omni Retrieval. In Proceedings of the 28th International Conference on Knowledge Discovery and Data Mining, Washington DC, USA, Aug. 14-18, 2022. [PDF]
7. [TheWebConf 2022] Nian Liu, Xiao Wang, Lingfei Wu, Yu Chen, Xiaojie Guo and Chuan Shi, Compact Graph Structure Learning via Mutual Information Compression. In Proceedings of The Web Conference 2022, Virtual Event, Apr. 25-29, 2022. [PDF]
8. [ICLR 2021] Shangqing Liu, Yu Chen**, Xiaofei Xie**, Jing Kai Siow and Yang Liu (**Corresponding Author), Retrieval-Augmented Generation for Code Summarization via Hybrid GNN. In Proceedings of the 9th International Conference on Learning Representations, Virtual Event, May 4-8, 2021. Spotlight paper. Acceptance rate=3.8% (114/2997). [PDF][Data][Slides]
9. [WSDM 2021] Yu Chen, Ananya Subburathinam, Ching-Hua Chen and Mohammed J. Zaki, Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph. In Proceedings of the 14th International Conference on Web Search and Data Mining, Virtual Event, Mar. 8-12, 2021. Acceptance rate=18.6% (112/603). [PDF][Code][Slides]
10. [NeurIPS 2020] Yu Chen, Lingfei Wu and Mohammed J. Zaki, Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings. In Proceedings of the 34th Conference on Neural Information Processing Systems, Virtual Event, Dec. 6-12, 2020. Acceptance rate=20.1% (1900/9454). [PDF][Code][Slides][Video]
11. [ISWC 2020] Nidhi Rastogi, Oshani Seneviratne, Yu Chen, Jon Harris, Diya Li, Ananya Subburathinam, Ruisi Jian, Megan Goulet, Yuheng Zhou, Osama Minhas, Jared Okun, Aaron Hill, Ching-Hua Chen and Dan Gruen, Applying Learning and Semantics for Personalized Food Recommendations. In Proceedings of the 19th International Semantic Web Conference, Virtual Event, Nov. 2-6, 2020. Demo Track paper. [PDF]
12. [AMIA 2020] Yu Chen, Ching-Hua Chen and Mohammed J. Zaki, Combining User Preferences and Health Needs in Personalized Food Recommendation. In Proceedings of the 2020 American Medical Informatics Association Virtual Annual Symposium, Virtual Event, Nov. 14-18, 2020. [PDF]
13. [IJCAI 2020] Yu Chen, Lingfei Wu and Mohammed J. Zaki, GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension. In Proceedings of the 29th International Joint Conference on Artificial Intelligence, Virtual Event, Jan. 7-15, 2021. Acceptance rate=12.6% (592/4717). [PDF][Code][Slides]
14. [ICLR 2020] Yu Chen, Lingfei Wu and Mohammed J. Zaki, Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation. In Proceedings of the 8th International Conference on Learning Representations, Virtual Event, Apr. 26-30, 2020. Acceptance rate=26.5% (687/2594). [PDF][Code][Slides][Video]
15. [AAAI DLGMA 2020] Yu Chen, Lingfei Wu and Mohammed J. Zaki, Deep Iterative and Adaptive Learning for Graph Neural Networks. In AAAI 2020 Workshop on Deep Learning on Graphs: Methodologies and Applications, New York, NY, USA, Feb. 7-12, 2020. (Best Student Paper Award) [PDF][Code][Slides]
16. [NeurIPS GRL 2019] Yu Chen, Lingfei Wu and Mohammed J. Zaki, Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model. In NeurIPS 2019 Workshop on Graph Representation Learning, Vancouver, BC, Canada, Dec. 8-14, 2019. [PDF][Code]
17. [ISWC 2019] Steven Haussmann, Yu Chen, Oshani Seneviratne, Nidhi Rastogi, James Codella, Ching-Hua Chen, Deborah McGuinness, Mohammed J. Zaki, FoodKG Enabled Q&A Application. In Proceedings of the 18th International Semantic Web Conference, Auckland, New Zealand, Oct. 26-30, 2019. Demo Track paper. [PDF][Code]
18. [ISWC 2019] Steven Haussmann, Oshani Seneviratne, Yu Chen, Yarden Ne’eman, James Codella, Ching-Hua Chen, Deborah L. McGuinness and Mohammed J. Zaki, FoodKG: A Semantics-Driven Knowledge Graph for Food Recommendation. In Proceedings of the 18th International Semantic Web Conference, Auckland, New Zealand, Oct. 26-30, 2019. Resources Track Full Paper. [PDF][Code][Website]
19. [ICML LRG 2019] Yu Chen, Lingfei Wu and Mohammed J. Zaki, GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension. In ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Representations, Long Beach, CA, USA, Jun. 9-15, 2019. [PDF][Code]
20. [NAACL 2019] Yu Chen, Lingfei Wu and Mohammed J. Zaki, Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases. In Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Minneapolis, MN, USA, Jun. 2-7, 2019. Long Oral Paper. Acceptance rate=26.3% (281/1067). [PDF][Code][Slides][Video]
21. [IEEE SSCI 2017] Yu Chen, Rhaad M. Rabbani, Aparna Gupta and Mohammed J. Zaki, Comparative Text Analytics via Topic Modeling in Banking. In Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, Hawaii, USA, Nov. 27-Dec. 1, 2017. [PDF][Code]
22.  [SIGKDD 2017] Yu Chen and Mohammed J. Zaki, KATE: K-competitive Autoencoder for Text. In Proceedings of the 23rd International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, Aug. 13-17, 2017. Full Oral Paper. Acceptance rate=8.6% (64/748). [PDF][Code][Slides][Video]
23. [CAD 2016] Yu Chen, Hao Chen and Jie Shen, Fast Voxel-based Surface Propagation Method for Outlier Removal. In Proceedings of the 13th International CAD Conference, Vancouver, BC, Canada, Jun. 27-29, 2016. [PDF][Code]

Journal Publications

1. [TMLR 2024] Xiaotian Han, Hanqing Zeng, Yu Chen, Shaoliang Nie, Jingzhou Liu, Kanika Narang, Zahra Shakeri, Karthik Abinav Sankararaman, Song Jiang, Madian Khabsa, Qifan Wang and Xia Hu, On the Equivalence of Graph Convolution and Mixup. Transactions on Machine Learning Research, Sep. 2024.
2. [MIR 2023] Jing Hu, Lingfei Wu, Yu Chen, Po Hu and Mohammed J. Zaki, GraphFlow+: Exploiting Conversation Flow in Conversational Machine Comprehension with Graph Neural Networks. Machine Intelligence Research, May 2023.
3. [TNNLS 2023] Yu Chen, Lingfei Wu and Mohammed J. Zaki, Toward Subgraph-Guided Knowledge Graph Question Generation With Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, Apr. 2023. [PDF]
4. [FnT Machine Learning 2022] Lingfei Wu*, Yu Chen*, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei and Bo Long (*Equal contributions), Graph Neural Networks for Natural Language Processing: A Survey. Foundations and Trends in Machine Learning, Jun. 2022. [PDF]
5. [Phys. Rev. Materials 2019] Yuwei Guo, Berit Goodge, Lifu Zhang, Jie Jiang, Yu Chen, Lena F. Kourkoutis and Jian Shi, Unit-cell-thick Domain in Free-standing Quasi-two-dimensional Ferroelectric Material. Phys. Rev. Materials 5 (4), Apr. 2021. [Paper]
6. [IJPEM 2019] Hao Chen, Yu Chen, Xu Zhang, Baiyuan Li, Xiaoqiang Liu, Xuefei Shi and Jie Shen, A Fast Voxel-based Method for Outlier Removal in Laser Measurement. International Journal of Precision Engineering and Manufacturing, 2019. [PDF]

Book Chapters

1. Yu Chen and Lingfei Wu, Graph Neural Networks: Graph Structure Learning. Graph Neural Networks: Foundations, Frontiers, and Applications, pp. 297-321. Springer, Singapore, 2022. [SpringerLink][Chinese version by Post & Telecom Press]

Dissertation

1. Yu Chen, Question Answering and Generation from Structured and Unstructured Data. Rensselaer Polytechnic Institute, 2020. [ProQuest][PDF]

Tutorials

1. [IJCAI 2024] Bang Liu, Yu Chen, Manling Li, Heng Ji and Lingfei Wu. Beyond Human Creativity: A Tutorial on Advancements in AI Generated Content. In Proceedings of the 33rd International Joint Conference on Artificial Intelligence, Aug. 3-9, 2024.
2. [AAAI 2024] Bang Liu, Yu Chen, Xiaojie Guo and Lingfei Wu. Beyond Human Creativity: A Tutorial on Advancements in AI Generated Content. In Proceedings of the 38th AAAI Conference on Artificial Intelligence, Feb. 20-27, 2024.
3. [TheWebConf 2022] Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li and Bang Liu. Deep Learning on Graphs for Natural Language Processing. In Proceedings of the ACM Web Conference 2022, Apr. 25-29, 2022. [Website] [Demo]
4. [AAAI 2022] Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li and Bang Liu. Deep Learning on Graphs for Natural Language Processing. In Proceedings of the 36th AAAI Conference on Artificial Intelligence, Feb. 22-Mar. 1, 2022. [Website] [Slides] [Video] [Demo]
5. [IJCAI 2021] Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li and Bang Liu. Deep Learning on Graphs for Natural Language Processing. In Proceedings of the 30th International Joint Conference on Artificial Intelligence, Aug. 21-26, 2021. [Website] [Slides] [Video] [Demo]
6. [SIGKDD 2021] Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li and Bang Liu. Deep Learning on Graphs for Natural Language Processing. In Proceedings of the 27th International Conference on Knowledge Discovery and Data Mining, Aug. 14-18, 2021. [Website] [Slides] [Demo]
7. [SIGIR 2021] Lingfei Wu, Yu Chen, Heng Ji and Bang Liu. Deep Learning on Graphs for Natural Language Processing. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 11-15, 2021. [Website] [Slides] [Video] [Demo]
8. [NAACL 2021] Lingfei Wu, Yu Chen, Heng Ji and Yunyao Li. Deep Learning on Graphs for Natural Language Processing. In Proceedings of the 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Jun. 6-11, 2021. [Website] [Slides] [Video] [Demo]

Patents

1. Lingfei Wu, Yu Chen and Mohammed J. Zaki. Subgraph Guided Knowledge Graph Question Generation. Publication, Jan. 2022. US20220027707A1
2. Lingfei Wu, Yu Chen and Mohammed J. Zaki. Iterative Deep Graph Learning for Graph Neural Networks. Publication, Dec. 2021. US20210374499A1
3. Lingfei Wu, Yu Chen and Mohammed J. Zaki. Natural Question Generation via Reinforcement Learning Based Graph-to-sequence Model. Publication, Jul. 2021. US20210209139A1
4. Lingfei Wu, Mohammed J. Zaki and Yu Chen. Conversation History Within Conversational Machine Reading Comprehension. Publication, Feb. 2021. US20210056445A1

Invited Talks

• Invited talk entitled "Graph4NLP: A Library for Deep Learning on Graphs for NLP" at Graph Machine Learning Summit'22, Jun 2022. [Slides]

• Keynote talk entitled "Graph Structure Learning for Graph Neural Networks" at IEEE AIIOT'22, Jun 2022. [Slides]

• Invited position talk entitled "Graph4NLP: A Library for Deep Learning on Graphs for NLP" at DLG4NLP@ICLR'22, Apr 2022. [Slides]

• Invited talk entitled "Graph4NLP: Library and Demo" at CLIQ-ai, Nov 2021. [Video][Slides]

• Guest lecture entitled "Deep Learning on Graphs for Natural Language Processing" in UIUC NLP course, Nov 2021.

• Invited talk entitled "Deep Learning on Graphs for Natural Language Processing" at 机器之心, July 2021. [Slides (password: flwv)][Video (password: wppp)]

• Dissertation talk entitled "Question Answering and Generation from Structured and Unstructured Data" at RPI, Jun. 24, 2020. [Slides]

• Invited talk entitled "Natural Question Generation and Graph Structure Learning with Graph Neural Networks" at Tencent AI Lab America, Mar. 2020.

• Invited talk entitled "Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation" at Dataminr, Inc. and Amazon, Inc., Mar.-Apr. 2020.

• Invited talk entitled "Automatic Graph Structure Learning for Graph Neural Networks" at IBM Research, Yorktown Heights, NY, USA, Nov. 18, 2019.

• Invited talk entitled "Knowledge Base Question Answering and Its Potential Applications in Adaptive Education" at the 3rd International Conference on Artificial Intelligence + Adaptive Education (AIAED 2019), Beijing, China, May 24-25, 2019. [Slides]

• Invited talk entitled "Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases" at IBM AI Horizons Seminar Series, May 2, 2019. [Talk]

Selected Awards

• Outstanding Paper Award at NAACL 2024.

• 2024 AACYF Top U30.

• 2023 Data Intelligence Leader of the Year, DataFun, 2024.

• Epubit Bestseller Award 2022 (Chapter contributor of the Chinese GNN book).

• Karen and Lester Gerhardt Prize (Outstanding Doctoral Dissertation Award), RPI, 2021.

• Robert McNaughton Prize (Outstanding Graduate Student Award), RPI, 2021.

• Best Student Paper Award at AAAI DLGMA 2020.

• Student Travel Award at SIGKDD 2017.

• 2nd Place at the 2016 Rensselaer Datathon, RPI.

• The First-Class People’s Scholarship, UESTC, 2013, 2014.

• National Scholarship, UESTC, 2012.

Media Coverage

2024 AACYF Top U30 (LAPost (2024-03), AACYF (2024-03), China News Service (2024-03), People's Daily Online (2024-03), China Daily (2024-03))

LM-Infinite (knowledge_base_6655 (2023-08), SparksofAGI (2023-08), zenRRan (2023-09))

CommerceMM (Meta AI Blog (2022-05))

DLG4NLP tutorials (Quan_Zhuanzhi (2021-07), zhuanzhi (2021-06), GraphRec (2021-06))

GNN4NLP survey (almosthuman2014 (2021-07), AI_era (2021-06), GraphRec (2021-06), AIGraph (2021-06), MLNLPer-World (2021-06), aitechtalk (2021-06))

Graph4NLP library (aitechtalk (2021-06))

GSL4GNNs book chapter (almosthuman2014 (2022-01), AIGraph (2022-01), SpringerCS (2022-01), AIGraph (2022-04), epubit (2022-11), DataFunTalk (2022-12))

Product recommendations (Meta Blog (2021-11), Ad Age (2021-11), Adweek (2021-11), TechCrunch (2021-11))

Personalized food recommendation system (World Economic Forum (2021-02), TechXplore (2021-01))

GSL4GNNs papers (thejiangmen (2020-12), AIGraph (2020-12), BUPT_GAMMA (2022-01))

Graph4NLP literature review (NewBeeNLP (2021-06))