DENGYONG (DENNY) ZHOU 



I am a senior researcher in the Machine Learning Department 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 INTERESTSStatistical machine learning, crowdsourcing (human computation), learning from clicks, learning representations, learning at scale, structured learning, probabilistic modeling, mathematical statistics, game theory and mechanism design, randomized methods, recommender systems. 

PUBLICATIONSN. Shah and D. Zhou. Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing. Advances in Neural Information Processing Systems (NIPS) 28, 2015. Y. Zhang, X. Chen, D. Zhou and M. I. Jordan. Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing. Journal of Machine Learning Research. To appear. D. Zhou, Q. Liu, J. C. Platt, C. Meek and N. B. Shah. Regularized Minimax Conditional Entropy for Crowdsourcing. Technical Report arXiv:1503.07240 [cs.LG], 2015. N. B. Shah, D. Zhou and Y. Peres. Approval Voting and Incentives in Crowdsourcing. Proceedings of the 32nd International Conference on Machine Learning (ICML), 2015. (long version) N. B. Shah and D. Zhou. On the Impossibility of Convex Inference in Human Computation. Proceedings of the 29th AAAI Conference on Artificial Intelligence, 2015. N. B. Shah and D. Zhou. Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing. Technical Report arXiv:1408.1387 [cs.GT], 2014. (slides) Y. Zhang, X. Chen, D. Zhou and M. I. Jordan. Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing. Advances in Neural Information Processing Systems (NIPS) 27, 2014. 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) (data and code) X. Chen, Q. Lin, and D. Zhou. Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling. Journal of Machine Learning Research, 16 (Jan):146, 2015. C. Gao and D. Zhou. Minimax Optimal Convergence Rates for Estimating Ground Truth from Crowdsourced Labels. Technical Report arXiv:1310.5764, October, 2013. H. Li, B. Yu, and D. Zhou. Error rate analysis of labeling by crowdsourcing. ICML'13 Workshop: Machine Learning Meets Crowdsourcing, 2013. 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, 22042212, 2012. (slides) (data and code) Y. Song, D. Zhou, and L.W. He. Query Suggestion by Constructing TermTransition Graphs. Proceedings of the ACM 5th Conference on Web Search and Data Mining (WSDM), 353362, 2012. D. Zhou, L. Xiao and M. Wu. Hierarchical Classification via Orthogonal Transfer. Proceedings of the 28th International Conference on Machine Learning (ICML), 801808, 2011. (long version) Y. Song, D. Zhou, and L.W. He. PostRanking Query Suggestion by Diversifying Search Results. Proceedings of the 34th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 815824, 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), 287296, 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 (ACMTKDD), 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), 741750, 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), 469478, 2008. D. Zhou and C. Burges. HighOrder 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), 153162, 2007. D. Zhou and C. Burges. Spectral Clustering and Transductive Learning with Multiple Views. Proceedings of the 24th International Conference on Machine Learning (ICML), 11591166, 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, 2128, 2007. D. Zhou, J. Huang and B. Schölkopf. Learning with Hypergraphs: Clustering, Classification, and Embedding. Advances in Neural Information Processing Systems (NIPS) 19, 16011608. (Eds.) B. Schölkopf, J.C. Platt and T. Hofmann, MIT Press, Cambridge, MA, 2007. G. CampsValls, T. V. Bandos and D. Zhou. SemiSupervised GraphBased Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing (45), No. 10, 30443054, 2007. D. Zhou and B. Schölkopf. Discrete Regularization. Book chapter, SemiSupervised Learning, 221232. (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, 199210, Springer, New York, NY, USA. T. V. Bandos, D. Zhou and G. CampsValls. SemiSupervised Hyperspectral Image Classification with Graphs. IEEE International Geoscience and Remote Sensing Symposium (IGARSS06), 38833886. 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), 10411048. (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, 361368, Springer, Berlin, Germany, 2005. D. Zhou, B. Schölkopf and T. Hofmann. SemiSupervised Learning on Directed Graphs. Advances in Neural Information Processing Systems (NIPS) 17, 16331640. (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. SemiSupervised Protein Classification Using Cluster Kernels. Bioinformatics 21(15), 32413247, 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, 321328. (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, 169176. (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), 65596563, 2004. J. Weston, C. Leslie, D. Zhou, A. Elisseeff and W. S. Noble. SemiSupervised Protein Classification Using Cluster Kernels. Advances in Neural Information Processing Systems (NIPS) 16, 595602. (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, 237244. (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. SemiSupervised Induction. Max Planck Institute Technical Report 141, Max Planck Institute for Biological Cybernetics, T¨¹bingen, Germany, 2004. 

INVITED TALKSIncentives in Human Computation. Computer Science Department, Yale University. Hosted by Prof. Dan Spielman. May 7, 2015. Incentives in Human Computation. Microsoft TechFest, March 26, 2015. Double or Nothing: Unique Mechanism to Incentivize MTurkers to Skip. Computer Science and Engineering Department, University of Washington. Hosted by Prof. Anna Karlin. March 6, 2015. (slides) Double or Nothing: Unique Mechanism to Incentivize MTurkers to Skip. Facebook, March 6, 2015. Double or Nothing: Unique Mechanism to Incentivize MTurkers to Skip. Microsoft Bing, November 5, 2014. Algorithmic Crowdsourcing. NIPS Workshop on Crowdsourcing: Theory, Algorithms and Applications, December 9, 2013. (slides) Learning from the Wisdom of Crowds by Minimax Entropy. Amazon, July 25, 2013. Learning from the Wisdom of Crowds by Minimax Entropy. UC Berkeley, Neyman Seminar, March 15, 2013. Hosted by Prof. Bin Yu and Aditya Guntuboyina. (slides) Learning from the Wisdom of Crowds by Minimax Entropy. Facebook, March 14, 2013. Learning from the Wisdom of Crowds by Minimax Entropy. Joint UWMicrosoft Research Machine Learning Workshop. Oct 26, 2012. 

PROFESSIONAL ACTIVITIESCochair NIPS'14 workshop: Crowdsourcing and Machine Learning, Montreal, Quebec, Canada, 2014. Cochair ICML'14 workshop: Crowdsourcing and Human Computing, Beijing, China, 2014. Cochair NIPS'13 workshop: Crowdsourcing: Theory, Algorithms and Applications, Lake Tahoe, Nevada, United States, 2013. Cochair ICML'13 workshop: Machine Learning Meets Crowdsourcing, Atlanta, United States, 2013. PC member: ICML 15, NIPS 14, ICML14, ICML 13, NIPS 13, ICML 12, NIPS 12, ICML 11, NIPS 10, ICML10, KDD10, ICML09, ACLIJCNLP 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. 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. 

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

Last updated 8/11/2015 