About

I am a Principal Researcher in the Deep Learning Group of  Microsoft Research.  Before joining Microsoft Research,  I was at the Max Planck Institute for Intelligent Systems, the Empirical Inference Department headed by Bernhard Schölkopf, and followed by a short stay with Vladimir Vapnik at the Machine Learning Department of NEC Laboratories America (Princeton campus).

Research interests

Machine learning, artificial intelligence, crowdsourcing and human computation, deep learning, machine reasoning, unsupervised and weakly supervised learning, machine reading and comprehension, automatic programming, machine translation, question answering, dialogue systems.

Recent publications

C. Wang, Y. Wang, P.-S. Huang, A. Mohamed, D. Zhou and L. Deng. Sequence Modeling via Segmentations. Proceedings of the 34th International Conference on Machine Learning (ICML), 2017.

L. Li, Y. Lu and D. Zhou. Provably Optimal Algorithms for Generalized Linear Contextual Bandits. Proceedings of the 34th International Conference on Machine Learning (ICML), 2017.

S. Du, J. Chen, L. Li, L. Xiao and D. Zhou. Stochastic Variance Reduction Methods for Policy Evaluation. Proceedings of the 34th International Conference on Machine Learning (ICML), 2017.

E. Parisotto, A. Mohamed, R. Singh, L. Li, D. Zhou and P. Kohli. Neuro-symbolic program synthesis. International Conference on Learning Representations (ICLR), 2017.

N. Shah and  D. Zhou. Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing.  Journal of Machine Learning Research, 17(165):1-52, 2016.

N. Shah and D. Zhou. No Oops, You Won’t Do It Again: Mechanisms for Self-correction in Crowdsourcing.   Proceedings of the 33rd International Conference on Machine Learning (ICML), 2016.

C. Gao, Y. Lu and D. Zhou. Exact Exponent in Optimal Rates for Crowdsourcing.  Proceedings of the 33rd International Conference on Machine Learning (ICML), 2016.

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, 17(102):1-44, 2016. [code/data]

N. Shah and D. Zhou. Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing.  Advances in Neural Information Processing Systems (NIPS) 28, 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.

X. Chen, Q. Lin, and D. Zhou. Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling. Journal of Machine Learning Research, 16 (Jan):1-46, 2015.

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. [code/data]

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.

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. [code/data]

Invited talks

Incentive mechanisms for crowdsourcing data labeling. Distinguished Lecture Series at Jump Trading LLC, Chicago. December 14, 2016.

Incentives 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 UW-Microsoft Research Machine Learning Workshop. Oct 26, 2012.

Professional Activities

Co-chair NIPS’14 workshop: Crowdsourcing and Machine Learning, Montreal, Quebec, Canada, 2014.

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, United States, 2013.

PC member: ICML 15, NIPS 14, ICML14,  ICML 13, NIPS 13, ICML 12NIPS 12ICML 11NIPS 10, ICML10, KDD10ICML09, ACL-IJCNLP 09AAAI 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 15, 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.

Projects

Neural-Symbolic Learning and Reasoning

Established: February 1, 2016

We develop algorithms to integrate differentiable neural models with non-differentiable symbolic knowledge. We are interested at both learning and reasoning. Contact person: Denny Zhou Publications C. Wang, Y. Wang, P.-S. Huang, A. Mohamed, D. Zhou and L. Deng. Sequence Modeling via Segmentations. Proceedings of the 34th International Conference on Machine Learning (ICML), 2017. E. Parisotto, A. Mohamed, R. Singh, L. Li, D. Zhou and P. Kohli. Neuro-symbolic program synthesis. International Conference on Learning Representations…

Virtual Algorithms Center (VIRAL)

Established: December 27, 2013

MSR has a strong group of scientists working on algorithm design, analysis, and experimental evaluation, as well as researchers in related areas (e.g., coding theory), but no formal algorithms group. The Virtual Algorithms Center (VIRAL) brings these individuals together. The goals of the center is to enhance collaboration between algorithms researchers and the rest of MSR, provide internal consulting, and give an external view of the algorithms research at MSR.

Algorithmic Crowdsourcing

Established: February 1, 2012

We study algorithmic issues for combining the intelligence of human and the computing power of machine to solve the problems that are difficult to solve by either human or machine alone. Contact person: Denny Zhou People Denny Zhou (Microsoft Research) John Platt (Google) Xi Chen (Carnegie Mellon University) Nihar Shah (University of California at Berkeley) Chao Gao (Yale University) Qiang Liu (University of California at Irvine) Yuchen Zhang (University of California at Berkeley) Yuval Peres (Microsoft Research) Chris…

Publications

2016

2015

2014

2013

2012

2011

2009

2008

2007

2006

Information Marginalization on Subgraphs
Jiayuan Huang, Tingshao Zhu, Russell Greiner, Denny Zhou, Dale Schuurmans, in 10th European Conference on Principles and Practice of Knowledge Discovery in Databases Berlin, Germany, September 18-22, 2006 Proceedings, Springer Berlin Heidelberg, September 18, 2006, View abstract, Download PDF

2005

2004

2003