Tianpei Xie's research focuses on robust machine learning from multiple data sources. He has developed algorithms for robust classification in the presence of noisy or corrupted training data, including GEM-MED which jointly performs classification and anomaly detection. He has also developed methods for multi-view learning on statistical manifolds, including CMV-MED which co-regularizes multiple models using a robust consensus measure based on information divergence between probability density functions. Current work involves predicting node attributes in networks by combining network topology and node distributions. He has published papers in major machine learning conferences and journals and maintains websites with details of his research activities.

1. Outline Introductions My research and contributions Additional information References
Summary of research activities
Tianpei Xie,
Advisor: Alfred O. Hero
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2. Outline Introductions My research and contributions Additional information References
1 Introductions
Backgrounds
Robust learning from multiple sources
2 My research and contributions
3 Additional information
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3. Outline Introductions My research and contributions Additional information References
Backgrounds
• With the advent of Big Data era, we experienced a great explosion in terms of
1 the amount of data that is public available;
2 the diversity of multiple data sources that are accessible simultaneously;
3 the power of computational resource.
Figure : One of the central neighborhood in Twitter
Network. https://dhs.standford.edu/
gephi-workshop/twitter-network-gallery/
Figure : Multi-modality data source
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4. Outline Introductions My research and contributions Additional information References
Beyond the conventional machine learning
New challenges in machine learning area:
• Robustness of model in terms of low quality training data;
• Learning from multiple information sources;
• Ability in handling data inconsistency and high dimensionality.
• Interest: single-source learning with clean training set ⇒ robust
learning from multiple sources using information theory.
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5. Outline Introductions My research and contributions Additional information References
Previous work
Robust learning
• Robust learning via surrogate loss e.g. [Bartlett and Mendelson,
2003], [Bousquet and Elisseeff, 2002], [Tyler, 2008], ROD [Xu et al.,
2006].
• Anomaly detection e.g. SVDD [Sch¨olkopf et al., 1999], GEM [Hero,
2006].
• Cons: sensitive to outlier in training sample, and solves a non-convex
optimization.
Learning from multiple source (Multi-view learning)
• Co-regularization on Euclidean feature space e.g. CCA [Hardoon
et al., 2004], SVM-2K [Farquhar et al., 2005], Neural Nets [Ngiam et al.,
2011]
• Cons: lack of ability to handle data with high uncertainty, high
dimensionality and between-view inconsistency.
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6. Outline Introductions My research and contributions Additional information References
Contributions
Robust learning:
• Proposed the GEM-MED algorithm [Xie et al., 2014] as a joint classification +
anomaly detection on noisy training set.
• Rely on the GEM estimator [Hero, 2006], a non-parametric entropy estimator.
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GEM−MED
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Figure : (a) The low-entropy region estimated by GEM [Hero, 2006] to separate outlier (red triangle) from the
nominal (circle and square) (b) The Precision-Recall curve for anomaly detection under given corruption rate
for GEM-MED and ROD.
• It only needs to solve a convex problem.
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7. Outline Introductions My research and contributions Additional information References
Our Contributions
Multi-view learning on statistical manifold:
• Assume data is given by parametric probability density function (p.d.f.) (data
with uncertainty.) and lies in a statistical manifold (space of all parametric p.d.f.).
• Proposed the CMV-MED algorithm [Xie et al., 2015] as the Co-regularization on
Statistical manifold, i.e. learning multiple models from the p.d.f. data.
• A robust consensus measure to quantify the between-view inconsistency using
the information divergence between p.d.fs .
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classifier 1
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classifier 2
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Figure : (a) The proposed stochastic consensus constraint on statistical manifold as a robust inconsistency
measure. (b) The interpretation of GEM-MED as averaging multiple statistical models on the manifold.
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8. Outline Introductions My research and contributions Additional information References
Current research: Node prediction in network
• Learning to predict node attributes by combining the network graph
topology and node distribution
... ... ...
? ?
personal info. friendship
node attribute
(meta data)
edge structure
ID
⇔
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9. Outline Introductions My research and contributions Additional information References
List of publications
List of relevant publications:
1 Xie, Tianpei, Nasser M. Nasrabadi, and Alfred O. Hero. ”Semi-supervised Multi-view learning
on statistical manifold via stochastic consensus constraints.” in preparation.
2 Xie, Tianpei, Nasser M. Nasrabadi, and Alfred O. Hero. ”Learning to classify with possible
sensor failures.” submitted to IEEE Transaction on Signal Processing, 2016
3 Xie, Tianpei, Nasser M. Nasrabadi, and Alfred O. Hero. ”Semi-supervised multi-sensor
classification via consensus-based Multi-View Maximum Entropy Discrimination.” In
Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on,
pp. 1936-1940. IEEE, 2015.
4 Xie, Tianpei, Nasser M. Nasrabadi, and Alfred O. Hero. ”Learning to classify with possible
sensor failures.” In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE
International Conference on, pp. 2395-2399. IEEE, 2014.
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10. Outline Introductions My research and contributions Additional information References
Websites and detailed information
• Contact:
Tianpei (Luke) Xie
Department of Electrical and Computing Engineering, University of
Michigan, Ann Arbor,
Office: 4313, EECS Building
TEL : 734-546-8048
Email: tianpei@umich.edu
• LinkedIn: personal webpage
https://www.linkedin.com/in/tianpeiluke
• Research: details for my research activities
http://tbayes.eecs.umich.edu/tianpei/research_main
• Github: my codes available
https://github.com/TianpeiLuke
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11. Outline Introductions My research and contributions Additional information References
Peter L Bartlett and Shahar Mendelson. Rademacher and Gaussian
complexities: Risk bounds and structural results. The Journal of Machine
Learning Research, 3:463–482, 2003.
Olivier Bousquet and Andr´e Elisseeff. Stability and generalization. The
Journal of Machine Learning Research, 2:499–526, 2002.
Jason Farquhar, David Hardoon, Hongying Meng, John S Shawe-taylor, and
Sandor Szedmak. Two view learning: SVM-2K, theory and practice.
Advances in neural information processing systems, pages 355–362, 2005.
David Hardoon, Sandor Szedmak, and John Shawe-Taylor. Canonical
correlation analysis: An overview with application to learning methods.
Neural computation, 16(12):2639–2664, 2004.
Alfred O Hero. Geometric entropy minimization (GEM) for anomaly detection
and localization. Advances in Neural Information Processing Systems,
pages 585–592, 2006.
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and
Andrew Y Ng. Multimodal deep learning. Proceedings of the 28th
International Conference on Machine Learning (ICML-11), pages 689–696,
2011.
Bernhard Sch¨olkopf, Robert C Williamson, Alex J Smola, John Shawe-Taylor,
and John C Platt. Support vector method for novelty detection. Advances
In Neural Information Processing Systems, 12:582–588, 1999.
David E Tyler. Robust statistics: Theory and methods. Journal of the
American Statistical Association, 103(482):888–889, 2008. 11 / 12

12. Outline Introductions My research and contributions Additional information References
Thank you!
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