The Machine Learning Department of Microsoft Research pushes the state of the art in machine learning. Our work spans the space from theoretical foundations of machine learning, to new machine learning systems and algorithms, to helping our partner product groups apply machine learning to large and complex tasks.
Machine learning, combinatorial statistics, multi-armed bandits, online learning, stochastic optimization, convex optimization
Chris Burges (Manager)
Machine learning algorithms, optimization, machine reading, ranking for web search, dimension reduction, audio fingerprinting
Practical applications of machine learning, learning graphical models, human computation and methods for coxswain displacement
Natural language processing, information extraction from large text collections, semantic modeling, information retrieval
Supervised learning algorithms, learning theory, online prediction, optimization, building large-scale machine learning systems, web search
Learning theory, hypothesis testing, continuous sensing, machine learning engineering principles
Dynamic language tools, machine learning, GPGPU, data visualization, adaptive document layout
Reinforcement learning, multi-armed bandit, online learning, recommendation, computational advertising
Chris Meek (Manager)
Graphical models (from various perspectives: inference, learning, relational, representation, algebraic, causal), temporal models (events, sequence data), and scalable algorithms
Machine learning algorithms, natural language modeling, HPC, GPGPU
Web search, online advertising, query log analysis, online privacy, community question answering, social networks, Markov logic, collective knowledge, crowdsourcing
Patrice Simard (Manager)
Interactive machine learning, active labeling, active featuring, large data sets, generalization, regularization, exploitation/exploration, neural networks
Convex optimization, first-order and online algorithms, interior point methods, optimization software, machine learning algorithms, sparsity recovery, parallel and distributed computing
Semantic similarity and relevance, spam filtering, structured-output learning, information extraction, natural language processing, information retrieval
Statistical machine learning, crowdsourcing (human computation), learning from clicks, learning representations, mathematical statistics
Applying for Positions
- Nihar Shah, Dengyong Zhou, and Yuval Peres, Approval Voting and Incentives in Crowdsourcing, in Proceedings of The 32nd International Conference on Machine Learning, July 2015.
- Lihong Li, Remi Munos, and Csaba Szepesvari, Toward Minimax Off-policy Value Estimation, in Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR: Workshop and Conference Proceedings, May 2015.
- Ryen W. White, Matthew Richardson, and Wen-tau Yih, Questions vs. Queries in Informational Search Tasks, in Proceedings of the companion publication of the 24th international conference on World Wide Web, ACM – Association for Computing Machinery, May 2015.
- Dragomir Yankov, Pavel Berkhin, and Lihong Li, Evaluation of Explore-Exploit Policies in Multi-result Ranking Systems, no. MSR-TR-2015-34, May 2015.
- Lihong Li, Shunbao Chen, Jim Kleban, and Ankur Gupta, Counterfactual Estimation and Optimization of Click Metrics in Search Engines: A Case Study, in Proceedings of the 24th International World Wide Web Conference (WWW'14), Companion Volume, ACM – Association for Computing Machinery, May 2015.
- Lihong Li, Offline Evaluation and Optimization for Interactive Systems, in Proceedings of the 8th ACM International Conference on Web Search and Data Mining, ACM – Association for Computing Machinery, February 2015.
- Lihong Li, Jin Young Kim, and Imed Zitouni, Toward Predicting the Outcome of an A/B Experiment for Search Relevance, in Proceedings of the 8th ACM International Conference on Web Search and Data Mining, ACM – Association for Computing Machinery, February 2015.
- Xi Chen, Qihang Lin, and Dengyong Zhou, Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling, in Journal of Machine Learning Research , vol. 16, pp. 1-46, January 2015.
- Yuchen Zhang and Lin Xiao, Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss, no. MSR-TR-2015-1, January 2015.
- Nihar B. Shah and Dengyong Zhou, On the Impossibility of Convex Inference in Human Computation, in Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI - Association for the Advancement of Artificial Intelligence, January 2015.
- Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng, Learning Multi-Relational Semantics Using Neural-Embedding Models, in NIPS 2014 workshop on Learning Semantics, 12 December 2014.
- Xiaodong He and Wen-tau Yih, Deep Learning and Continuous Representations for Language Processing (Tutorial for SLT-2014), 7 December 2014.
- Yuchen Zhang, Xi Chen, Dengyong Zhou, and Michael I. Jordan, Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing, in Advances in Neural Information Processing Systems 27, MIT Press, December 2014.
- Saleema Amershi, Maya Cakmak, W. Bradley Knox, and Todd Kulesza, Power to the People: The Role of Humans in Interactive Machine Learning, in AI Magazine, AAAI - Association for the Advancement of Artificial Intelligence, December 2014.
- Lihong Li, He He, and Jason Williams, Temporal Supervised Learning for Inferring a Dialog Policy from Example Conversations, in Proceedings IEEE Spoken Language Technology Workshop (SLT), IEEE – Institute of Electrical and Electronics Engineers, December 2014.
- Lin Xiao and Tong Zhang, A Proximal Stochastic Gradient Method with Progressive Variance Reduction, in SIAM Journal on Optimization, vol. 24, no. 4, pp. 2057-2075, SIAM – Society for Industrial and Applied Mathematics, December 2014.
- Miroslav Dudik, Dumitru Erhan, John Langford, and Lihong Li, Doubly Robust Policy Evaluation and Optimization, in Statistical Science, Institute of Mathematical Statistics, November 2014.
- Kai-Wei Chang, Wen-tau Yih, Bishan Yang, and Christopher Meek, Typed Tensor Decomposition of Knowledge Bases for Relation Extraction, in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, ACL – Association for Computational Linguistics, October 2014.
- Lihong Li, Rémi Munos, and Csaba Szepesvari, On Minimax Optimal Offline Policy Evaluation, no. MSR-TR-2014-124, 15 September 2014.
- Li Deng and John C. Platt, Ensemble Deep Learning for Speech Recognition, Proc. Interspeech, September 2014.
- Yuchen Zhang and Lin Xiao, Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization, no. MSR-TR-2014-123, September 2014.
- Nihar B. Shah and Dengyong Zhou, Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing, no. MSR-TR-2014-117, August 2014.
- David Heckerman, Christopher Meek, and Thomas S. Richardson, Variations on Undirected Graphical Models and their Relationships, in Kybernetika, Kybernetika, July 2014.
- Ryen W. White, Matthew Richardson, and Wen-tau Yih, Questions vs. Queries in Informational Search Tasks, no. MSR-TR-2014-96, July 2014.
- Tianbing Xu, Jianfeng Gao, Lin Xiao, and Amelia C. Regan, Online Classification using a Voted RDA Method, in Proceedings of the AAAI Conference on Artificial Intelligence , AAAI, July 2014.
- Christopher Meek, Toward Learning Graphical and Causal Process Models, July 2014.
- Qihang Lin, Zhaosong Lu, and Lin Xiao, An Accelerated Proximal Coordinate Gradient Method and its Application to Regularized Empirical Risk Minimization, no. MSR-TR-2014-94, July 2014.
- Qihang Lin and Lin Xiao, An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization, in Proceedings of the 31st International Conference on Machine Learning (ICML), June 2014.
- Dengyong Zhou, Qiang Liu, John C. Platt, and Christopher Meek, Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy, in Proceedings of the 31st International Conference on Machine Learning (ICML), June 2014.
- Wen-tau Yih, Xiaodong He, and Christopher Meek, Semantic Parsing for Single-Relation Question Answering, in Proceedings of ACL, Association for Computational Linguistics, June 2014.
- Crowd wisdom among many topics examined at top AI event
- CEO Nadella talks Microsoft's mobile ambitions, Windows 10 strategy, HoloLens and more
- The next evolution of machine learning: Machine teaching
- Young coders compete in 2015 Beauty of Programming
- Exploring the frontiers of computing
- The future of artificial intelligence: Myths, realities and aspirations
- Faculty Summit 2015: Explore advances in artificial intelligence—and much more
- Standing the test of time: Microsoft researcher honored for prescient machine learning work
- AAAI Spring Symp. on Observational Studies through Social Media and Other Human-Generated Content
Stanford, CA· 2123 March 2016
- MSR India Summer School 2015 on Machine Learning
Indian Institute of Science, Bangalore India· 1526 June 2015
- Cloud Computing for Research with Microsoft Azure
Lomonosov Moscow State University, Russia· 19 May 2015