The Machine Learning Groups of Microsoft Research include a set of researchers and developers who push the state of the art in machine learning. We span the space from proving theorems about the math underlying ML, to creating new ML systems and algorithms, to helping our partner product groups apply ML to large and complex data sets.
People
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Interactive machine learning, interactive tutoring systems, educational data mining, dynamic languages for scientific computation, machine learning for creativity applications, machine learning for systems applications, auditory analysis and synthesis |
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Learning from user behavior, predictive personalization, scaling up machine learning, learning applications in online advertising, adaptive similarity functions, building tools for improving predictive accuracy |
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Large-scale learning, statistical machine learning, structured learning systems, stochastic gradient learning, transduction, causality and machine learning, kernel methods, neural networks, interactive machine learning, reasoning and machine learning, machine learning and semantics, compound image compression |
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Chris Burges (Manager) Machine learning algorithms, optimization, machine reading, ranking for web search, dimension reduction, audio fingerprinting |
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Denis Charles Algorithms, Complexity Theory, Graph Theory, Game Theory and Mechanism Design, Web Scale Computing, Computational Number Theory, Algebraic Number Theory and Algebraic Geometry |
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Practical applications of machine learning, learning graphical models, human computation and methods for coxswain displacement |
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Natural language processing, information extraction from large text collections, semantic modeling, information retrieval |
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Supervised learning algorithms, learning theory, online prediction, optimization, building large-scale machine learning systems, web search |
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Learning theory, hypothesis testing, continuous sensing, machine learning engineering principles |
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Temporal modeling and forecasting of events, modeling user intent, recommender systems, online advertising |
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Machine learning for security applications, data analysis problems involving adversaries, fraud and abuse, risk analysis, economics and incentive problems |
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Dynamic language tools, machine learning, GPGPU, data visualization, adaptive document layout |
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reinforcement learning, multi-armed bandit, online learning, recommendation, computational advertising |
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Chris Meek (Manager) Graphical models (from various perspectives: inference, learning, relational, representation, algebraic, causal), temporal models (events, sequence data), and scalable algorithms |
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Machine learning algorithms, natural language modeling, HPC, GPGPU |
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John Platt (Manager) Improving the data/human interface, fast machine learning, automatically discovering representations |
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Software development, large-scale data analysis and algorithms, numerics |
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Web search, online advertising, query log analysis, online privacy, community question answering, social networks, Markov logic, collective knowledge, crowdsourcing |
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Patrice Simard (Manager) Interactive machine learning, active labeling, active featuring, large data sets, generalization, regularization, exploitation/exploration, neural networks |
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Machine learning for security, malware classification, malicious webpage detection, active learning |
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Convex optimization, first-order and online algorithms, interior point methods, optimization software, machine learning algorithms, sparsity recovery, parallel and distributed computing |
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Semantic similarity and relevance, spam filtering, structured-output learning, information extraction, natural language processing, information retrieval |
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Big data analysis, large-scale and parallel learning, software and systems diagnosis |
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Supervised/unsupervised learning, theory and algorithms of crowdsourcing, human/social computing, representation or feature learning, distributed computing, convex/nonconvex optimization, web search and online advertising
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Applying for Positions
- Apply here for a Postdoctoral Researcher position for 2013 and have your application material (including references) sent to mlgapp@microsoft.com.
- Apply here for a summer internship for 2013 and inform us that you have applied by emailing mlgapp@microsoft.com.
2013
- Qihang Lin and Lin Xiao, An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization, no. MSR-TR-2013-41, 5 April 2013
- Xi Chen, Qihang Lin, and Dengyong Zhou, Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing, in Proceedings of the 30th International Conference on Machine Learning (ICML), 2013
2012
- Dengyong Zhou, John Platt, Sumit Basu, and Yi Mao, Learning from the Wisdom of Crowds by Minimax Entropy, in Advances in Neural Information Processing Systems (NIPS), December 2012
- Léon Bottou, Jonas Peters, Joaquin Quiñonero-Candela, Denis X. Charles, D. Max Chickering, Elon Portugualy, Dipankar Ray, Patrice Simard, and Ed Snelson, Counterfactual Reasoning and Learning Systems, no. MSR-TR-2012-130, September 2012
- Yucheng Low and Alice X. Zheng, Fast Top-K Similarity Queries Via Matrix Compression, no. MSR-TR-2012-81, 7 August 2012
- Hoyt Koepke and Mikhail Bilenko, Fast Prediction of New Feature Utility, in Proceedings of the 29th International Conference on Machine Learning (ICML-2012), June 2012
- Lin Xiao and Tong Zhang, A Proximal-Gradient Homotopy Method for the Sparse Least-Squares Problem, no. MSR-TR-2012-36, March 2012
- Yang Song, Dengyong Zhou, and Li-wei He, Query Suggestion by Constructing Term-Transition Graphs, in WSDM '12, ACM, 8 February 2012
- Yucheng Low and Alice X. Zheng, Fast Top-K Similarity Queries Via Matrix Compression, in Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM 2012), ACM International Conference on Information and Knowledge Management (CIKM), 2012
- Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, and Lin Xiao, Optimal Distributed Online Prediction using Mini-Batches, in Journal of Machine Learning Research, vol. 13, pp. 165-202, Microtome Publishing, January 2012
2011
- Puyang Xu, Sanjeev Khudanpur, and Asela Gunawardana, Randomized Maximum Entropy Language Models, in Automatic Speech Recognition and Understanding, IEEE SPS, December 2011
- Asela Gunawardana, Christopher Meek, and Puyang Xu, A Model for Temporal Dependencies in Event Streams, in Neural Information Processing Systems, Neural Information Processing Systems Foundation, December 2011
- Steven M. Drucker, Danyel Fisher, and Sumit Basu, Helping Users Sort Faster with Adaptive Machine Learning Recommendations, in Proceedings of Interact 2011, Springer, September 2011
- Mikhail Bilenko and Matthew Richardson, Predictive Client-side Profiles for Personalized Advertising, in Proceedings of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2011), San Diego, CA, USA, August 2011
- Jianfeng Gao, Kristina Toutanova, and Wen-tau Yih, Clickthrough-Based Latent Semantic Models for Web Search, in Proceedings of the Thirty-Fourth Annual International ACM SIGIR Conference, ACM, 24 July 2011
- Rajhans Samdani and Wen-tau Yih, Domain Adaptation with Ensemble of Feature Groups, in Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, 21 July 2011
- Jim C. Huang, Christopher Meek, Carl Kadie, and David Heckerman, Conditional Random Fields for Fast, Large-Scale Genome-Wide Association Studies, in PLoS ONE, PLoS, 12 July 2011
- Puyang Xu, Asela Gunawardana, and Sanjeev Khudanpur, Efficient Subsampling for Training Complex Language Models, in Empirical Methods in Natural Language Processing, Association for Computational Linguistics, July 2011
- Yang Song, Dengyong Zhou, and Li-wei He, Post-Ranking Query Suggestion by Diversifying Search Results, in SIGIR '11 Proceedings of the 34st annual international ACM SIGIR conference on Research and development in information retrieval , Association for Computing Machinery, Inc., July 2011
- Guy Shani, Asela Gunawardana, and Christopher Meek, Unsupervised hierarchical probabilistic segmentation of discrete events, in Intelligent Data Analysis, IOS Press, 27 June 2011
- Wen-tau Yih, Kristina Toutanova, John Platt, and Chris Meek, Learning Discriminative Projections for Text Similarity Measures, in Proceedings of the Fifteenth Conference on Computational Natural Language Learning , Association for Computational Linguistics, 13 June 2011
- Benjamin Birnbaum, Nikhil R. Devanur, and Lin Xiao, Distributed Algorithms via Gradient Descent for Fisher Markets, in Proceedings of the 12th ACM Conference on Electronic Commerce, ACM, June 2011
- Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, and Lin Xiao, Optimal Distributed Online Prediction, in Proceedings of the 28th International Conference on Machine Learning (ICML), June 2011
- Dengyong Zhou, Lin Xiao, and Mingrui Wu, Hierarchical Classification via Orthogonal Transfer, in Proceedings of the 28th International Conference on Machine Learning (ICML), Bellevue, WA, USA, June 2011
- Ryen W. White, Matthew Richardson, and Yandong Liu, Effects of Community Size and Contact Rate in Synchronous Social Q&A, in Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2011), Vancouver, Canada, May 2011
- Krysta M. Svore and Christopher J.C. Burges, Large-scale Learning to Rank using Boosted Decision Trees, in Scaling Up Machine Learning: Parallel and Distributed Approaches, Cambridge University Press, May 2011
- Junjie Zhang, Christian Seifert, Jack W. Stokes, and Wenke Lee, ARROW: Generating Signatures to Detect Drive-By Downloads, in WWW 2011, WWW 2011, 28 March 2011
- Krysta M. Svore, Maksims N. Volkovs, and Christopher J.C. Burges, Learning to Rank with Multiple Objective Functions, in Proceedings of WWW 2011, International World Wide Web Conference, March 2011
- Matthew Richardson and Ryen W. White, Supporting Synchronous Social Q&A Throughout the Question Lifecycle, in Proceedings of the 20th International World Wide Web Conference (WWW 2011), International World Wide Web Conference, Hyderabad, India, March 2011
- Anagha Kulkarni, Jaime Teevan, Krysta M. Svore, and Susan T. Dumais, Understanding Temporal Query Dynamics, in Web Search and Data Mining (WSDM) 2011, Association for Computing Machinery, Inc., February 2011
Applications
Contents
- Machine Learning Seminar
- People
- Publications
- Past Members, Postdocs and Interns
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- ICML 2013 Workshop: Machine Learning Meets Crowdsourcing
Atlanta, USA ·21 June 2013 - Games, Networks and Markets 2013
Cambridge, UK ·6–7 June 2013 - Machine Learning Summit 2013
Paris, France ·22–24 April 2013
