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DENGYONG (DENNY) ZHOU

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Machine Learning Group
Microsoft Research
One Microsoft Way
Redmond, WA 98052

E-mail: dengyong.zhou@microsoft.com
Phone: (425)421-6338
Fax: (425)936-7329
Office: Building 99/Room 39
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I am a researcher in the Machine Learning Group at  Microsoft Research.  Before I worked at the Machine Learning Department of NEC Laboratories America (Princeton campus) with Vladimir Vapnik, and at the Max Planck Institute for Intelligent Systems (formerly Max Planck Institute for Biological Cybernetics), the Empirical Inference Department headed by Bernhard Schölkopf.

RESEARCH INTERESTS

Supervised learning, algorithmic crowdsourcing (human computing), learning representations, large-scale and stochastic learning, probabilistic modeling, nonparametric statistics, randomized methods, and their applications to web search, social media and human-computer interaction.

New  Co-organizing ICML¡¯14 workshopCrowdsourcing and Human Computing, Beijing, China.

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PUBLICATIONS

N. B. Shah and  D. Zhou. Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing.  Technical Report  arXiv:1408.1387 [cs.GT], 2014.

Y. Zhang, X. Chen, D. Zhou and M. I. Jordan. Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing.  Technical Report arXiv:1406.3824, 2014. Submitted.

D. Zhou, Q. Liu, J. C. Platt, and C. Meek. Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy. Proceedings of the 31st International Conference on Machine Learning (ICML), 2014. (slides)

X. Chen, Q. Lin, and D. Zhou. Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling. Technical Report arXiv:1403.3080, March, 2014. To appear at Journal of Machine Learning Research. 

C. Gao and D. Zhou. Minimax Optimal Convergence Rates for Estimating Ground Truth from Crowdsourced Labels. Technical Report arXiv:1310.5764, October, 2013. Submitted.

X. Chen, Q. Lin, and D. Zhou.  Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing. Proceedings of the 30th International Conference on Machine Learning (ICML), 2013. (appendix)

D. Zhou, J. C. Platt, S. Basu, and Y. Mao. Learning from the Wisdom of Crowds by Minimax Entropy. Advances in Neural Information Processing Systems (NIPS) 25, 2204-2212, 2012. (slides)

Y. Song, D. Zhou, and L.-W. He. Query Suggestion by Constructing Term-Transition Graphs. Proceedings of the ACM 5th Conference on Web Search and Data Mining (WSDM), 353-362, 2012.

D. Zhou, L. Xiao and M. Wu. Hierarchical Classification via Orthogonal Transfer. Proceedings of the 28th International Conference on Machine Learning (ICML), 801-808, 2011. (long version)

Y. Song, D. Zhou, and L.-W. He.  Post-Ranking Query Suggestion by Diversifying Search Results. Proceedings of the  34th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 815-824, 2011.

H. Ma, D. Zhou, C. Liu, M. Lyu, and I. King. Recommender Systems with Social Regularization. Proceedings of the ACM 4th Conference on Web Search and Data Mining (WSDM), 287-296, 2011.

D. Zhou, L. Xiao and M. Wu. Hierarchical Classification via Orthogonal Transfer. NIPS Workshop on Optimization for Machine Learning, 2010.  (long version)

Y. Chi, X. Song, D. Zhou, K. Hino, and B. Tseng.  On Evolutionary Spectral Clustering. ACM Transactions on Knowledge Discovery from Data (ACM-TKDD), Volume 3,  Issue 4, Article 17, 2009. 

D. Shen, Y. Li, X. Li, and D. Zhou. Product Query Classification. Proceedings of the ACM 18th Conference on Information and Knowledge Management (CIKM), 741-750, 2009.

Q. Mei, D. Zhou, and K. Church. Query Suggestion Using Hitting Time. Proceedings of the ACM 17th Conference on Information and Knowledge Management (CIKM), 469-478, 2008.

D. Zhou and C. Burges. High-Order Regularization on Graphs.  International Workshop on Mining and Learning with Graphs, Helsinki, Finland, 2008.

Y. Chi, X. Song, D. Zhou, K. Hino, and B. Tseng.  Evolutionary Spectral Clustering by Incorporating Temporal Smoothness. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), 153-162, 2007. 

D. Zhou and C. Burges. Spectral Clustering and Transductive Learning with Multiple Views. Proceedings of the 24th International Conference on Machine Learning (ICML), 1159-1166, 2007.

D. Zhou, C. Burges and T. Tao.  Transductive Link Spam Detection Proceedings of the 3rd International Workshop on Adversarial Information Retrieval on the Web, 21-28, 2007.

D. Zhou, J. Huang and B. Schölkopf. Learning with Hypergraphs: Clustering, Classification, and Embedding. Advances in Neural Information Processing Systems (NIPS) 19, 1601-1608. (Eds.) B. Schölkopf, J.C. Platt and T. Hofmann, MIT Press, Cambridge, MA, 2007.

G. Camps-Valls, T. V. Bandos and D. Zhou. Semi-Supervised Graph-Based Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing (45), No. 10, 3044-3054, 2007.

D. Zhou and B. Schölkopf.  Discrete Regularization. Book chapter, Semi-Supervised Learning, 221-232. (Eds.) O. Chapelle,  B. Schölkopf and A. Zien, MIT Press, Cambridge, MA, 2006.

J. Huang, T. Zhu, R. Rereiner, D. Zhou and D. Schuurmans. Information Marginalization on Subgraphs. Proceedings of 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, 199-210, Springer, New York, NY, USA.

T. V. Bandos,  D. Zhou and G. Camps-Valls. Semi-Supervised Hyperspectral Image Classification with Graphs. IEEE International Geoscience and Remote Sensing Symposium (IGARSS06), 3883-3886.

D. Zhou, J. Huang and B. Schölkopf. Learning from Labeled and Unlabeled Data on a Directed Graph. Proceedings of the 22nd International Conference on Machine Learning (ICML), 1041-1048. (Eds.) L. De Raedt and S. Wrobel, ACM press, 2005.

D. Zhou and B. Schölkopf. Regularization on Discrete Spaces. Pattern Recognition, Proceedings of the 27th DAGM Symposium, 361-368, Springer, Berlin, Germany, 2005.

D. Zhou, B. Schölkopf and T. Hofmann. Semi-Supervised Learning on Directed Graphs. Advances in Neural Information Processing Systems (NIPS) 17, 1633-1640. (Eds.) L.K. Saul,  Y. Weiss and L. Bottou, MIT Press, Cambridge, MA, 2005.

J. Weston,  C. S. Leslie, E. Ie, D. Zhou, A. Elisseeff and W. S. Noble. Semi-Supervised Protein Classification Using Cluster Kernels. Bioinformatics 21(15), 3241-3247, 2005.

D. Zhou,  J. Huang and B. Schölkopf.  Beyond Pairwise Classification and Clustering Using Hypergraphs. Max Planck Institute Technical Report 143, Max Planck Institute for Biological Cybernetics, T¨¹bingen, Germany, 2005.  

D. Zhou, O. Bousquet, T.N. Lal, J. Weston and B. Schölkopf. Learning with Local and Global Consistency. Advances in Neural Information Processing Systems (NIPS) 16, 321-328. (Eds.) S. Thrun,  L. Saul and B. Schölkopf, MIT Press, Cambridge, MA, 2004.

D. Zhou, J. Weston, A. Gretton, O. Bousquet and B. Schölkopf. Ranking on Data Manifolds. Advances in Neural Information Processing Systems (NIPS) 16, 169-176. (Eds.) S. Thrun,  L. Saul and B. Schölkopf, MIT Press, Cambridge, MA, 2004.

J. Weston, A. Elisseeff, D. Zhou, C. Leslie and W. S. Noble. Protein Ranking: From Local to Global Structure in the Protein Similarity Network. Proceedings of the National Academy of Science (PNAS) 101(17), 6559-6563, 2004.

J. Weston, C. Leslie, D. Zhou, A. Elisseeff and W. S. Noble. Semi-Supervised Protein Classification Using Cluster Kernels. Advances in Neural Information Processing Systems (NIPS) 16, 595-602. (Eds.) S. Thrun,  L. Saul and B. Schölkopf, MIT Press, Cambridge, MA, 2004.

D. Zhou  and B. Schölkopf. A Regularization Framework for Learning from Graph Data. ICML Workshop on Statistical Relational Learning and Its Connections to Other Fields, 2004.

D. Zhou  and B. Schölkopf. Learning from Labeled and Unlabeled Data Using Random Walks. Pattern Recognition, Proceedings of the 26th DAGM Symposium, 237-244. (Eds.) C.E. Rasmussen, H.H. B¨¹lthoff, M.A. Giese and B. Schölkopf, Springer, Berlin, Germany, 2004.

K. Yu, V. Tresp and D. Zhou. Semi-Supervised Induction. Max Planck Institute Technical Report 141,  Max Planck Institute for Biological Cybernetics, T¨¹bingen, Germany, 2004.

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INVITED TALKS

Algorithmic crowdsourcing. NIPS Workshop on Crowdsourcing: Theory, Algorithms and Applications, December 9, 2013. (slides)

Learning from the Wisdom of Crowds by Minimax Entropy. UC Berkeley, Neyman Seminar, March 15, 2013. (slides)

Learning from the Wisdom of Crowds by Minimax Entropy. Facebook, March 14, 2013. (slides)

A Minimax Entropy Method for Learning the Wisdom of Crowds. Joint UW-Microsoft Research Machine Learning Workshop. Oct 26, 2012. (slides)

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PROFESSIONAL ACTIVITIES

Co-chair ICML'14 workshop: Crowdsourcing and Human Computing, Beijing, China, 2014.

Co-chair NIPS'13 workshop: Crowdsourcing: Theory, Algorithms and Applications, Lake Tahoe, Nevada, United States, 2013. 

Co-chair ICML'13 workshop: Machine Learning Meets Crowdsourcing, Atlanta, USA, 2013. 

PC member: NIPS 14, ICML14,  ICML 13, NIPS 13, ICML 12 NIPS 12ICML 11 NIPS 10, ICML10, KDD10 ICML09, ACL-IJCNLP 09 AAAI 07,  ICML 07, MLG 07, ICML 06, ECML 06, AISTATS 09, NIPS 07, IJCAI 07, NIPS 06, NIPS 05, IJCAI 05, NIPS 04.

Area Chair: NIPS 10

Reviewer:  Journal of Machine Learning Research, Machine Learning Journal, IEEE Transactions on Information Theory, IEEE Transactions on Pattern Analysis and Machine Intelligence, and IEEE Transactions on Neural Networks.

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HOBBIES

I like reading, writing, painting, skiing, swimming, hiking, travelling, and jogging.

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Last updated 6/6/2014