Huaxiu Yao (姚骅修)

[1] Huaxiu Yao*, Xinyu Yang*, Allan Zhou, Chelsea Finn, Multi-Domain Long-Tailed Learning by Augmenting Disentangled Representations, arXiv 2210.14358 (the short version is presented in NeurIPS 2022 Workshop on Distribution Shifts). [arXiv]

[2] Zhenbang Wu, Huaxiu Yao, Zhe Su, David M Liebovitz, Lucas M Glass, James Zou, Chelsea Finn, Jimeng Sun, Knowledge-Driven New Drug Recommendation, arXiv 2210.05572 (the short version is presented in NeurIPS 2022 Workshop on Meta-Learn). [arXiv]

[3] Yoonho Lee*, Annie S. Chen*, Fahim Tajwar, Ananya Kumar, Huaxiu Yao, Percy Liang, Chelsea Finn, Surgical Fine-Tuning Improves Adaptation to Distribution Shifts, arXiv 2210.11466 (the short version is presented in NeurIPS 2022 I Can't Believe It's Not Better Workshop and Workshop on Distribution Shifts). [arXiv]

[4] Yoonho Lee, Huaxiu Yao, Chelsea Finn, Diversify and Disambiguate: Learning From Underspecified Data, arXiv 2202.03418 (the short version is presented in ICML 2022 Workshop on Spurious correlations, Invariance, and Stability and Workshop on Principles of Distribution Shift). [Project Page] [Code] [arXiv]

[5] Percy Liang, Rishi Bommasani, Tony Lee, [and 47 others, including Huaxiu Yao], Holistic Evaluation of Language Models, arXiv 2211.09110. [arXiv]

[1] Huaxiu Yao*, Yiping Wang*, Linjun Zhang, James Zou, Chelsea Finn, C-Mixup: Improving Generalization in Regression, in Proceeding of the Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS 2022a), New Orleans, LA, 2022. [PDF] [Code] [Video] [Bilibili] [Slides] [Poster] [arXiv]

[2] Huaxiu Yao*, Caroline Choi*, Bochuan Cao, Yoonho Lee, Pang Wei Koh, Chelsea Finn, Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time, in Proceeding of the Thirty-Sixth Conference on Neural Information Processing Systems Track on  Datasets & Benchmarks (NeurIPS 2022b), New Orleans, LA, 2022. [PDF] [Code] [Website] [Video] [Bilibili] [Slides] [Poster] [arXiv]

[3] Yemin Yu, Ying Wei, Kun Kuang, Zhengxing Huang, Huaxiu Yao, Fei Wu, GRASP: Navigating Retrosynthetic Planning with Goal-driven Policy, in Proceeding of the Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS 2022c), New Orleans, LA, 2022. [PDF]

[4] Huaxiu Yao*, Yu Wang*, Sai Li, Linjun Zhang, Weixin Liang, James Zou, Chelsea Finn, Improving Out-of-Distribution Robustness via Selective Augmentation, in Proceeding of the Thirty-ninth International Conference on Machine Learning (ICML 2022), Baltimore, MD, July 2022. [PDF] [Code] [Video] [Bilibili] [Slides] [Poster] [arXiv]

[5] Huaxiu Yao, Linjun Zhang, Chelsea Finn, Meta-Learning with Fewer Tasks through Task Interpolation, in Proceeding of the 10th International Conference on Learning Representations (ICLR 2022), Virtual, Apr. 2022 (Oral, 54/3391). [Openreview] [PDF] [Code] [Slides] [Poster] [arXiv]

[6] Yingxiu Zhao, Zhiliang Tian, Huaxiu Yao, Yinhe Zheng, Dongkyu Lee, Yiping Song, Jian Sun, Nevin Zhang, Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation, in Proceeding of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022), Dublin, Ireland, May 2022 (long paper). [Openreview] [PDF] [Code] [arXiv]

[7] Bin Lu, Xiaoying Gan, Weinan Zhang, Huaxiu Yao, Luoyi Fu, Xinbing Wang, Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer, in Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2022), Washington DC, Aug. 2022 (Research Track). [PDF] [Code] [arXiv]

[8] Yinjie Jiang, Yemin Yu, Ming Kong, Yu Mei, Luotian Yuan, Zhengxing Huang, Kun Kuang, Zhihua Wang, Huaxiu Yao, James Zou, Connor W. Coley, Ying Wei, Artificial Intelligence for Retrosynthesis Prediction, Engineering (Engineering), 2022 (survey). [PDF]

[9] Huaxiu Yao*, Yu Wang*, Ying Wei, Peilin Zhao, Mehrdad Mahdavi, Defu Lian, Chelsea Finn, Meta-learning with an Adaptive Task Scheduler, in Proceeding of the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021a), Virtual, Dec. 2021. [PDF] [Supplementary] [Code] [Slides] [Poster] [arXiv]

[10] Huaxiu Yao, Ying Wei, Longkai Huang, Ding Xue, Junzhou Huang, Zhenhui Li, Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery, in Proceeding of the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021b), Virtual, Dec. 2021. [PDF] [Supplementary] [Code] [Slides]

[11] Huaxiu Yao, Yingxin Wu, Maruan Al-Shedivat, Eric P. Xing, Knowledge-Aware Meta-learning for Low-Resource Text Classification, in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021), Punta Cana, Dominican Republic, Nov. 2021 (Short Paper, Oral). [PDF] [Code] [Slides] [Poster] [arXiv]

[12] Huaxiu Yao*, Longkai Huang*, Linjun Zhang, Ying Wei, Li Tian, James Zou, Junzhou Huang, Zhenhui Li, Improving Generalization in Meta-learning via Task Augmentation, in Proceeding of the Thirty-eighth International Conference on Machine Learning (ICML 2021), Virtual, Jul. 2021. [PDF] [Supplementary] [Code] [Slides] [Poster] [arXiv]

[13] Jiatu Shi*, Huaxiu Yao*, Xian Wu, Tong Li, Zedong Lin, Tengfei Wang, Binqiang Zhao, Relation-aware Meta-learning for E-commerce Market Segment Demand Prediction with Limited Records, in Proceeding of the 14th ACM International Conference on Web Search and Data Mining (WSDM 2021), Virtual, Mar. 2021. [PDF] [arXiv]

[14] Chuxu Zhang, Huaxiu Yao, Lu Yu, Chao Huang, Dongjing Song, Haifeng Chen, Meng Jiang, Nitesh V. Chawla, Inductive Contextual Relation Learning for Personalization, ACM Transactions on Information System (TOIS) 39, no. 3 (2021): 1-22. [PDF]

[15] Huaxiu Yao, Yingbo Zhou, Mehrdad Mahdavi, Zhenhui Li, Richard Socher, Caiming Xiong, Online Structured Meta-learning, in Proceeding of the Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020), Virtual, Dec. 2020. [PDF] [Supplementary] [Slides] [Poster] [arXiv]

[16] Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li, Zhenhui Li, Automated Relational Meta-learning, in Proceeding of the Eighth International Conference on Learning Representations (ICLR 2020), Virtual, Apr. 2020. [Openreview] [PDF] [Code] [Slides] [arXiv]

[17] Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh V. Chawla, Zhenhui Li, Graph Few-shot Learning via Knowledge Transfer, in Proceeding of the Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI 2020a), New York, NY, Feb. 2020 (the short version is presented in NeurIPS 2019 Graph Representation Learning Workshop). [PDF] [Full Paper] [Code] [arXiv]

[18] Chuxu Zhang, Huaxiu Yao, Chao Huang, Meng Jiang, Zhenhui Li, Nitesh V. Chawla, Few-Shot Knowledge Graph Completion, in Proceeding of the Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI 2020b), New York, NY, Feb. 2020. [PDF] [Code] [Poster] [arXiv]

[19] Xinshi Zang, Huaxiu Yao, Guanjie Zheng, Nan Xu, Kai Xu, Zhenhui Li, MetaLight: Value-based Meta-reinforcement Learning for Online Universal Traffic Signal Control, in Proceeding of the Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI 2020c), New York, NY, Feb. 2020. [PDF] [Code] [Poster]

[20] Xianfeng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, Suhang Wang, Transferring Robustness for Graph Neural Network Against Poisoning Attacks, in Proceeding of the 13th ACM International Conference on Web Search and Data Mining (WSDM 2020), Houston, TX, Feb. 2020. [PDF] [Code] [arXiv]

[21] Huaxiu Yao, Ying Wei, Junzhou Huang, Zhenhui Li, Hierarchically Structured Meta-learning, in Proceeding of the 36th International Conference on Machine Learning (ICML 2019), Long Beach, CA, June 2019. [PDF] [Supplementary] [Slides] [Poster] [Code] [arXiv]

[22] Huaxiu Yao, Yiding Liu, Ying Wei, Xianfeng Tang, Zhenhui Li, Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction, in Proceeding of The Web Conference 2019 (WWW 2019a), San Francisco, CA, May 2019 (Research Track, Long Paper). [PDF] [Slides] [Poster] [Code] [arXiv with Erratum]

[23] Xianfeng Tang, Boqing Gong, Yanwei Yu, Huaxiu Yao, Yandong Li, Haiyong Xie, Xiaoyu Wang, Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference, in Proceeding of The Web Conference 2019 (WWW 2019b), San Francisco, CA, May 2019 (Research Track, Long Paper). [PDF] [arXiv]

[24] Huaxiu Yao*, Xianfeng Tang*, Hua Wei, Guanjie Zheng, Zhenhui Li, Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction, in Proceeding of the Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2019), Honolulu, Hawaii, Jan. 2019. [PDF] [Poster] [Code] [Spotlight] [arXiv]

[25] Huaxiu Yao, Defu Lian, Yi Cao, Yifan Wu, Tao Zhou, Predicting Academic Performance for College Students: A Campus Behavior Perspective, ACM Transactions on Intelligent Systems and Technology (TIST) 10.3 (2019): 24. [PDF] [arXiv]

[26] Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, Zhenhui Li, Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction, in Proceeding of the Thirty-second AAAI Conference on Artificial Intelligence (AAAI 2018), New Orleans, LA, Feb. 2018. [PDF] [Poster] [Code] [Spotlight] [Penn State News] [arXiv]

[27] Hua Wei*, Guanjie Zheng*, Huaxiu Yao, and Zhenhui Li, IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control, in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), London, UK, Aug. 2018 (Research Track). [PDF] [Code] [Demo]

[28] Yi Cao*, Jian Gao*, Defu Lian, Zhihai Rong, Jiatu Shi, Qing Wang, Yifan Wu*, Huaxiu Yao*, Tao Zhou*. Orderliness predicts academic performance: behavioural analysis on campus lifestyle, Journal of the Royal Society Interface (J. R. Soc. Interface), 15 (146), (2018) 20180210 (alphabetical order). [PDF] [Supplementary]

Tutorial

• Learning with Small Data. (KDD 2020 [Website] [Slides] [YouTube] [Bilibili]) (WSDM 2020 [Website]) (AAAI 2021)

• Meta-learning and Automated Machine Learning: Approaches and Applications​. (IJCAI 2020)

Guest Lecture

• CS 330: Deep Multi-Task and Meta Learning, Stanford University, Fall 2022
• CS 5824: Advanced Machine Learning, Virginia Tech, Spring 2022

​Teaching Assistant

• IST 597: Reinforcement Learning, Instructor: Dr. Zihan Zhou, Fall 2019

[2022 - now] Fahim Tajwar, undergrad@Stanford

[2022 - now] Zhenbang Wu, Ph.D. student@UIUC, co-mentored with Prof. Jimeng Sun, Achievement: preprint'22b

[2022 - now] Caroline Choi, undergrad@Stanford, Achievement: NeurIPS'22b

[2022 - now] Nathan Hu, undergrad@Stanford

[2022 - now] Yiping Wang, undergrad@ZJU, Achievement: NeurIPS'22a

[2022 - now] Xinyu Yang, undergrad@SJTU, Achievement: preprint'22a

[2022 - now] Zhiyu Xie, undergrad@THU

[2022] Xinyi Pan, undergrad@SJTU

[2021 - 2022] Bochuan Cao, undergrad@UESTC -> Ph.D. student@PSU, Achievement: NeurIPS'22b

[2021 - 2022] Zhe Su, undergrad@ZJU, Achievement: preprint'22b

[2021 - 2022] Takao Yatagai, undergrad@Stanford

[2021 - 2022] Yu Wang, undergrad@USTC -> Ph.D. student@UCSD, Achievements: NeurIPS'21a, ICML'22

[2021] Yingxin Wu, undergrad@USTC -> Ph.D. student@Stanford, Achievement: EMNLP'21

[2021] Yingxiu Zhao, Ph.D. student@HKUST, co-mentored with Prof. Nevin Zhang, Achievement: ACL'22

[2020] Jiatu Shi (industry practitioner), senior engineer@Alibaba -> senior engineer@ByteDance, Achievement: WSDM'21

[2019] Xinshi Zang, undergrad@SJTU -> Ph.D. student@CUHK, Achievement: AAAI'20

• Lead Organizer, Sixth Workshop on Meta-Learning, NeurIPS 2022 (Co-organizers: Fábio Ferreira, Frank Hutter, Joaquin Vanschoren, Eleni Triantafillou, Qi Lei) [Website]
• Lead Organizer, the First Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward, ICML 2022 (Co-organizers: Hugo Larochelle, Colin Rafel, Percy Liang, Jian Tang, Ying Wei, Saining Xie, Eric P. Xing, Chelsea Finn) [Website]
• Organizer, Fifth Workshop on Meta-Learning, NeurIPS 2021 (Co-organizers: Fábio Ferreira, Erin Grant, Frank Hutter, Jonathan Schwarz, Joaquin Vanschoren) [Website]

Conference Area Chair/Senior Program Committee:

• AAAI Conference on Artificial Intelligence (AAAI), 2023
• Conference on Neural Information Processing Systems, Dataset and Benchmark Track (NeurIPS, D&B Track), 2022
• International Conference on Automated Machine Learning (AutoML-Conf), 2022 (Senior AC)
• Learning on Graphs Conference (LoG), 2022

Conference Program Committee/Reviewer:

• International Conference on Machine Learning (ICML), 2020 - 2022
• Annual Conference on Neural Information Processing Systems (NeurIPS), 2020 - 2022
• International Conference on Learning Representations (ICLR), 2020 - 2023
• International Conference on Artificial Intelligence and Statistics (AISTATS), 2022 - 2023
• ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020 - 2022
• AAAI Conference on Artificial Intelligence (AAAI), 2020 - 2022
• Annual Meeting of the Association for Computational Linguistics (ACL), 2021 - 2022 (ARR)

Journal Invited Reviewer:

• Journal of Machine Learning Research (JMLR)
• Transactions on Machine Learning Research (TMLR)
• IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI)
• Journal of Artificial Intelligence (AIJ)
• ACM Transactions on Knowledge Discovery from Data (TKDD)
• IEEE Transactions on Knowledge and Data Engineering (TKDE)
• ACM Transaction on Intelligent System and Technology (TIST)
• Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)

Actionable Machine Learning for Tackling Distribution Shift

• University of California, Santa Cruz, 2022
• University of Maryland, 2022
• MedAI Seminar,  Stanford, 2022
• Center for Multi-Agent Research, Peking University, 2022

Improving Generalization in Meta-learning through Organization and Augmentation

• StatsML Seminar, Imperial & Oxford, 2022
• University of Fribourg, 2021
• M₂D₂ Seminar, Valence Discovery & Mila, 2022

Learning to Learn with Structured Knowledge

• Juniper Network, 2022
• Carnegie Mellon University, 2020
• Stanford University, 2020
• Microsoft Dynamic 365, 2020
• Amazon A9, 2020

• AI Rising Stars in Chinese Students, Baidu Research, 2021
• College of IST Ph.D. Award for Research Excellence, Penn State University, 2020
• Excellent Bachelor Thesis​ in UESTC, 2016
• Excellent Graduates in UESTC, 2016

• Applied Scientist Intern, Amazon A9, Mentor: Dr. Nikhil Rao, Dr. Sumeet Katariya, Prof. Sujay Sanghavi, Summer 2020
• Research Intern, Salesforce Research, Mentor: Dr. Caiming Xiong, Dr. Yingbo Zhou, Manager: Dr. Richard Socher, Fall 2019
• Research Intern, Alibaba DAMO Academy, USA, Mentor & Manager: Dr. Bolin Ding, Summer 2019
• Research Intern, Tencent AI Lab, Mentor: Dr. Ying Wei, Manager: Prof. Junzhou Huang, Summer 2018
• Research Intern, Didi AI Labs, Mentor: Dr. Pinghua Gong, Manager: Prof. Jieping Ye, Spring 2017