Pengtao Xie

Assistant Professor

Electrical and Computer Engineering

University of California San Diego

I obtained my PhD from the Machine Learning Department, School of Computer Science, Carnegie Mellon University. My research interests mainly lie in machine learning inspired by humans’ learning skills (especially classroom learning skills), such as learning by passing tests, interleaving learning, learning by self-explanation, small-group learning, learning by teaching, learning by ignoring, etc., and their applications in healthcare. Please refer to this monograph and project page for details.

pengtaoxie2008@gmail.com Twitter Weibo

I am looking for highly-motivated PhD and master students to join my group. I am also looking for (remote) research interns for Summer 2021.

News

• 2020/8. My PhD thesis was selected as a finalist (top 5) for the AMIA Doctoral Dissertation Award.
• 2021/04/03. I am co-organizing ICML 2021 workshop “Self-supervised Learning for Reasoning and Perception”.
• 2021/04/03. I am co-organizing ICML 2021 workshop “Interpretable Machine Learning for Healthcare”.
• 2021/03/18. I will serve as an area chair for NeurIPS 2021.
• 2020/12/23. I will serve as an area chair for ICML 2021.
• 2020/12/11. I am co-organizing ICLR 2021 workshop “Machine Learning for Preventing and Combating Pandemics”.
• 2020/11/28. I am elected as a member of the Machine Learning for Signal Processing Technical Committee.
• 2020/11/17. I will serve as an area chair for ICCV 2021.
• 2020/10/29. I will serve as an area chair for NAACL 2021.
• 2020/9/14. I am recognized as a Top Reviewer for ICML 2020.
• 2020/8/24. I am co-organizing AAAI 2021 workshop “Trustworthy AI for Healthcare”.
• 2020/8/19. I will serve as an area chair of CVPR 2021.
• 2020/8/14. I am co-organizing NeurIPS 2020 workshop “Self-Supervised Learning – Theory and Practice”.
• 2020/8/6. I am awarded Google Cloud research credits.
• 2020/6/17. I will serve as an area chair of AAAI 2021.
• 2020/6/17. I will serve as an area chair of IJCAI 2021.
• 2020/6/15. I am awarded an Amazon AWS Research Award. Thank you AWS!
• 2020/6/1. I am awarded a Tencent AI-Lab Faculty Award. Thank you Tencent AI-Lab!
• 2020/5/1. I am awarded a Tencent Faculty Award. Thank you Tencent!

Current Students

• Arda Bati
• Bhanu Garg
• Ramtin Hosseini
• Sai Somayajula
• Tushar Sircar (CSE)
• Jeremy Wink (CSE)
• Xingyi Yang
• Ruisi Zhang
• Shengzhe Zhang

Past Students

Undergraduates

• Ruisi Zhang (2020 –> PhD student at UCSD ECE)
• Matt Hong (2020 –> PhD student at UCSD CSE)
• Jiayuan Huang (2020 –> Master student at CMU CS)
• Jiaqi Zeng (2020 –> Master student at CMU CS)
• Meng Zhou (2020 –> Master student at CMU CS)
• Yuhong Chen (2020 –> Master student at CMU INI)
• Yue Yang (2020 –> Master student at Georgia Tech CS)

Master Students

• Jiachen Li (2020 –> PhD student at UCSB)
• Xuehai He (2020 –> PhD student at UCSC)

Teaching

• ECE285 Deep Generative Models, Winter 2021.
• ECE269 Linear Algebra and Applications, Winter 2021.

Recent Works on Machine Learning Inspired by Humans’ Learning Skills

• Pengtao Xie, Xuefeng Du, Hao Ban. Skillearn: Machine Learning Inspired by Humans’ Learning Skills. arXiv:2012.04863 (2020).
• Xuefeng Du, Pengtao Xie. Learning by Passing Tests, with Application to Neural Architecture Search. arXiv:2011.15102 (2020).
• Ramtin Hosseini, Pengtao Xie. Learning by Self-Explanation, with Application to Neural Architecture Search. arXiv:2012.12899 (2020).
• Xuefeng Du, Pengtao Xie. Small-Group Learning, with Application to Neural Architecture Search. arXiv:2012.12502 (2020).
• Parth Sheth, Pengtao Xie. Learning by Teaching, with Application to Neural Architecture Search. TechRxiv (2020).
• Xingchen Zhao, Pengtao Xie. Learning by Ignoring. arXiv:2012.14288 (2020).
• Hao Ban, Pengtao Xie. Interleaving Learning. (2020).

Publications Since 2020

Google Scholar

Publications Before 2020

• Congzheng Song, Shanghang Zhang, Najmeh Sadoughi, Pengtao Xie, Eric Xing. Generalized Zero-shot ICD Coding. International Joint Conference on Artificial Intelligence (IJCAI 2020).
• Zeya Wang, Baoyu Jing, Yang Ni, Nanqing Dong, Pengtao Xie, Eric P Xing. Adversarial Domain Adaptation Being Aware of Class Relationships. European Conference on Artificial Intelligence (ECAI 2020).
• B. Huang, K. Zhang, P. Xie, M. Gong, E. P. Xing. Specific and Shared Causal Relation Modeling and Mechanism-based Clustering. Advances in Neural Information Processing Systems (NeurIPS 2019).
• K. Xu, M. Lam, J. Pang, X. Gao, C. Band, P. Mathur, F. Papay, A. K. Khanna, J. B. Cywinski, K. Maheshwari, P. Xie, E. P. Xing. Multimodal Machine Learning for Automated ICD Coding. Conference on Machine Learning for Healthcare (MLHC 2019).
• Z.Wang, N.Dong, S.Rosario, M.Xu, P.Xie, and E.P.Xing. Ellipse Detection of Optic Disc-and-Cup Boundary in Fundus Image with Unsupervised Domain Adaption. The IEEE International Symposium on Biomedical Imaging (ISBI 2019).
• P.Xie, W.Wu, Y.Zhu and E.P.Xing. Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis. The 35th International Conference on Machine Learning (ICML 2018) (Long Oral Presentation).
• P.Xie, H.Zhang, Y.Zhu and E.P.Xing. Nonoverlap-Promoting Variable Selection. The 35th International Conference on Machine Learning (ICML 2018) (Short Oral Presentation).
• P.Xie, H.Shi, M.Zhang and E.P.Xing. A Neural Architecture for Automated ICD Coding. The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018) (Oral Presentation)
• B.Jing, P.Xie and E.P.Xing. On the Automatic Generation of Medical Imaging Reports. The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018).
• P.Xie, J.Zhu and E.P.Xing. Diversity-Promoting Bayesian Learning of Latent Variable Models. To appear in the Journal of Machine Learning Research (JMLR), 2018.
• P.Xie, J.Kim, Q.Ho, Y.Yu and E.P.Xing. Orpheus: Efficient Distributed Machine Learning via System and Algorithm Co-design. Symposium of Cloud Computing (SoCC 2018).
• D.Sachan, P.Xie and E.P.Xing. Effective Use of Bidirectional Language Modeling for Medical Named Entity Recognition. Conference on Machine Learning for Healthcare (MLHC 2018).
• X.Liu, K.Xu, P.Xie and E.P.Xing. Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records. NIPS ML for Healthcare Workshop, 2018 (Spotlight Presentation).
• P.Xie, R.Salakhutdinov, L.Mou and E.P.Xing. Deep Conditional Determinantal Point Process for Large-Scale Multi-Label Classification. International Conference on Computer Vision (ICCV 2017).
• P.Xie, B.Poczos and E.P.Xing. Near-Orthogonality Regularization in Kernel Methods. Conference on Uncertainty in Artificial Intelligence (UAI 2017) (Plenary Presentation).
• H.Zhang, Z.Zheng, S.Xu, X.Liang, W.Dai, Q.Ho, Z.Hu, J.Wei, P.Xie, and E.P.Xing. Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters. 2017 USENIX Annual Technical Conference (ATC 2017) (Oral Presentation).
• P.Xie, A.Singh and E.P.Xing. Uncorrelation and Evenness: A New Diversity-Promoting Regularizer. The 34th International Conference on Machine Learning (ICML 2017) (Oral Presentation).
• P.Xie, Y.Deng, Y.Zhou, A.Kumar, Y.Yu, J.Zou and E.P.Xing. Learning Latent Space Models with Angular Constraints. The 34th International Conference on Machine Learning (ICML 2017) (Oral Presentation).
• H.Zhou, J.Li, P.Xie and Y.Zhang. Improving the Generalization Performance of Multi-class SVM via Angular Regularization. The 26th International Joint Conference on Artificial Intelligence (IJCAI 2017).
• P.Xie and E.P.Xing. A Constituent-Centric Neural Architecture for Reading Comprehension. The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017).
• Y.Zhou, K.Yuan, Y.Yu, X.Ni, P.Xie, E.P.Xing and S.Xu. Inference of multiple-wave population admixture by modeling decay of linkage disequilibrium with polynomial functions. Heredity, 2017.
• E.P.Xing, Q.Ho, P.Xie and W.Dai. Strategies and Principles of Distributed Machine Learning on Big Data. Engineering, Transactions of Chinese Academy of Engineering (Engineering 2016).
• P.Xie, J.Kim, Y.Zhou, Q.Ho, A.Kumar, Y.Yu and E.P.Xing. Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting. The 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016).
• P.Xie, J.Zhu and E.P.Xing. Diversity-Promoting Bayesian Learning of Latent Variable Models. The 33rd International Conference on Machine Learning (ICML 2016) (Oral Presentation).
• E.P.Xing, Q.Ho, W.Dai, J.Kim, J.Wei, S.Lee, X.Zheng, P.Xie, A.Kumar and Y.Yu. Petuum: A New Platform for Distributed Machine Learning on Big Data. IEEE Transactions on Big Data (IEEE BigData 2015).
• P.Xie. Learning Compact and Effective Distance Metrics with Diversity Regularization. European Conference on Machine Learning (ECML 2015) (Oral Presentation).
• P.Xie, Y.Deng and E.P.Xing. Diversifying Restricted Boltzmann Machine for Document Modeling. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2015) (Oral Presentation).
• E.P.Xing, Q.Ho, W.Dai, J.Kim, J.Wei, S.Lee, X.Zheng, P.Xie, A.Kumar and Y.Yu. Petuum: A New Platform for Distributed Machine Learning on Big Data. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2015) (Oral Presentation).
• P.Xie, D.Yang and E.P.Xing. Incorporating Word Correlation Knowledge into Topic Modeling. The 2015 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2015).
• P.Xie, Y.Pei, Y.Xie and E.P.Xing. Mining User Interests from Personal Photos. The 29th AAAI Conference on Artificial Intelligence (AAAI 2015).
• P.Xie and E.P.Xing. Integrating Image Clustering and Codebook Learning. The 29th AAAI Conference on Artificial Intelligence (AAAI 2015) (Oral Presentation).
• P.Xie and E.P.Xing. Multi-Modal Distance Metric Learning. The 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013) (Oral Presentation).
• P.Xie and E.P.Xing. Integrating Document Clustering and Topic Modeling. Proceedings of the 29th International Conference on Uncertainty in Artificial Intelligence (UAI 2013).

Professional Activities

Area Chairs for:

• ICML 2021, NeurIPS 2021, CVPR 2021, NAACL 2021, ICCV 2021, AAAI 2021, IJCAI 2021,

Co-organizer for:

• NeurIPS 2020 workshop “Self-Supervised Learning – Theory and Practice”
• AAAI 2021 workshop “Trustworthy AI for Healthcare”
• ICLR 2021 workshop “Machine Learning for Preventing and Combating Pandemics”

Member for:

• Machine Learning for Signal Processing Technical Committee
• ACM, IEEE, AMIA

Program Committee Members or Reviewers for:

• Conferences: ICML (2014, 2018-2019), NIPS (2016, 2018), AISTATS (2017-2019), UAI (2018), ICLR (2019), AAAI (2019), CVPR (2016-2019), ICCV (2015, 2017), ECCV (2016, 2018), ACL (2015-2018), EMNLP (2015), KDD (2015), ECML (2016-2017), ACCV (2016), BMVC (2017)
• Journals: TPAMI (2018), TKDE (2015-2018), TMM (2016-2017), PLOS ONE (2017-2018), TNNLS (2015-2016, 2018), JASA (2015)

Selected Awards and Honors

• Finalist (top 5) for AMIA Doctoral Dissertation Award.
• Amazon AWS Research Award.
• Tencent AI-Lab Faculty Award.
• Tencent Faculty Award.
• Google Cloud Research Credits.
• Top Reviewer for ICML 2020.
• Innovator Award, 2018 (presented by the Pittsburgh Business Times).
• 1st Place (out of 400+ participating teams) in both Defenses and Targeted Attacks, 3rd Place in Untargeted Attacks, in NIPS Adversarial Vision Challenge, 2018.
• Siebel Scholarship, 2014 (85 graduate students from around the world).

Recent Invited Talks

• Machine Learning for Medical Decision Support
  • Nov 2019, Department of Biomedical Informatics, University of Pittsburgh
  • Oct 2019, AI NEXTCon Developer Conference
  • Apr 2019, New York University
  • Apr 2019, University of Massachusetts Amherst
  • Mar 2019, University of California San Diego
  • Feb 2019, Columbia University
  • Feb 2019, Johns Hopkins University
  • Feb 2019, University of California Los Angeles
  • Jan 2019, University of Wisconsin-Madison