The Machine Learning department of Microsoft Research is 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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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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Systems, anti-counterfeiting, security, parallel computation, mobile payments, Kinect and gesture recognition, regression/classification, embedded systems |
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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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Machine learning for security, malware classification, malicious webpage detection, active learning |
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Large-scale machine learning systems, spatial statistics, abnormality detection, time-series analysis, event detection and forecasting in time series, Bayesian networks and graphical models in general |
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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 learning, kernel machines, semi-supervised learning and clustering, optimization algorithms, learning theory, crowd sourcing, social networks, web search |
Applying for Positions
- Apply here for a Postdoctoral Researcher position for 2012 and have your application material (including references) sent to mlgapp@microsoft.com.
- Apply here for a summer internship for 2012 and inform us that you have applied by emailing mlgapp@microsoft.com.
2012
- Yang Song, Dengyong Zhou, and Li-wei He, Query Suggestion by Constructing Term-Transition Graphs, in WSDM '12, ACM, 8 February 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
- 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
- Puyang Xu, Sanjeev Khudanpur, and Asela Gunawardana, Randomized Maximum Entropy Language Models, in Automatic Speech Recognition and Understanding, IEEE SPS, 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Christopher J.C. Burges, Krysta M. Svore, Paul N. Bennett, Andrzej Pastusiak, and Qiang Wu, Learning to Rank using an Ensemble of Lambda-Gradient Models, in Journal of Machine Learning Research: Workshop and Conference Proceedings, vol. 14, pp. 25-35, Journal of Machine Learning Research, February 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
- Alice X. Zheng, John Dunagan, and Ashish Kapoor, Active Graph Reachability Reduction for Network Security and Software Engineering, in Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI), 2011
- Hao Ma, Dengyong Zhou, Chao Liu, Michael R. Lyu, and Irwin King, Recommender systems with social regularization, in Proceedings of the fourth ACM international conference on Web search and data mining, Association for Computing Machinery, Inc., New York, NY, USA, January 2011
- Christopher Meek and Ydo Wexler, Improved Approximate Sum-Product Inference Using Multiplicative Error Bounds, in Bayesian Statistics 9, Oxford University Press, 2011
- Raja R. Sambasivan, Alice X. Zheng, Michael De Rosa, Elie Krevat, Spencer Whitman, Michael Stroucken, William Wang, Lianghong Xu, and Gregory R. Ganger, Diagnosing performance changes by comparing request flows, in Proceedings of the 8th USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2011
- Guy Shani and Asela Gunawardana, Evaluating Recommendation Systems, in Recommender Systems Handbook, Springer, 2011
- Yang Song, Nam Nguyen, Li-wei He, Scott Imig, and Robert Rounthwaite, Searchable Web Sites Recommendation, in WSDM'11: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, Association for Computing Machinery, Inc., 2011
2010
- Wen-tau Yih and Ning Jiang, Similarity Models for Ad Relevance Measures, in NIPS Workshop: Machine Learning in Online Advertising (MLOAD 2010), 10 December 2010
Applications
Contents
- Machine Learning Seminar
- People
- Publications
- Past Members, Postdocs and Interns
- Faculty Fellows: the Magnificent Seven
- Data in the Fast Lane
- Inside Microsoft Research: More Research Contributions to Windows Phone Translation
- Microsoft Research Debuts N.Y.C. Lab
- Inside Microsoft Research: Start Spreading the News … Announcing Microsoft Research New York City!
- Olympic Torch Wins UK Design of the Year 2012 Award
- Microsoft Research: Bringing Sexy Back
- 2012 Women to Watch: Jennifer Chayes
- AISEC 2012
Sheraton Raleigh Hotel, Raleigh, NC USA ·16–18 October 2012 - Computer Vision School 2011
Moscow, Russia ·28 July–3 August 2011 - The 2011 School on Approximability
Bangalore, India ·5–9 January 2011
