Misha

Mikhail Bilenko


mbilenko@microsoft.com

Research   |   Personal   |   Contact


I am a researcher in the Machine Learning Department at Microsoft Research. I am interested in learning problems that arise in the context of large behavioral, transactional and textual datasets. Specific applications on which I worked recently include click probability prediction, semantic ad matching, constructing user profiles for ad targeting, and improving search relevance by mining logs of browsing behavior. In the past, I worked on semi-supervised clustering and record linkage (entity resolution, de-duplication, etc.). I am generally interested in adaptive similarity/distance functions, implementing learning algorithms on parallel/distributed platforms, and creating tools for machine learning practitioners.

I completed my Ph.D. in the Department of Computer Science at the University of Texas at Austin in 2006, where I was a member of the Machine Learning Group. Along the way, I spent the summer of 2002 at IBM T.J. Watson Research Center, and the summer/fall of 2004 at Google.

Research

  • Learning from large datasets

    • Lazy Paired Hyper-Parameter Tuning
      Alice X. Zheng and Mikhail Bilenko. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI-2013), Beijing, China, August 2013.
      [PDF]     [Slides (PPT)]
    • Fast Prediction of New Feature Utility
      Hoyt Koepke and Mikhail Bilenko. In Proceedings of the 29th International Conference on Machine Learning (ICML-2012), Edinburgh, Scotland, June 2012.
      [PDF]     [Slides (PPT)]
    • Scaling Up Machine Learning. Edited by Ron Bekkerman, Mikhail Bilenko, and John Langford. Cambridge University Press, 2012.
    • NIPS 2011 Workshop on Big Learning
    • Predictive Client-side Profiles for Personalized Advertising
      Mikhail Bilenko and Matthew Richardson. In Proceedings of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2011), San Diego, August 2011.
      [PDF]     [Slides (PPT)]
    • SIGIR-2009 Workshop on Information Retrieval and Advertising
    • Catching the Drift: Learning Broad Matches from Clickthrough Data
      Sonal Gupta, Mikhail Bilenko, and Matthew Richardson. Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2009), Paris, June 2009.
      [PDF]     [Slides (PPT)]
    • Enhancing Web Search by Promoting Multiple Search Engine Usage
      Ryen W. White, Matthew Richardson, Mikhail Bilenko, and Allison Heath. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-2008), Singapore, July 2008.
      [PDF]
    • Talking the Talk vs. Walking the Walk: Salience of Information Needs in Querying vs. Browsing
      Mikhail Bilenko, Ryen W. White, Matthew Richardson, and G. Craig Murray. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-2008), Singapore, July 2008.
      [PDF]
    • Mining the Search Trails of Surfing Crowds: Identifying Relevant Websites From User Activity
      Mikhail Bilenko and Ryen W. White. In Proceedings of the 17th International World Wide Web Conference (WWW-2008), Beijing, April 2008.
      [PDF]
    • Leveraging Popular Destinations to Enhance Web Search Interaction
      Ryen W. White, Mikhail Bilenko, and Silviu Cucerzan. ACM Transactions on the Web (TWEB), 2(3), pp.1-30, 2008.
      [PDF]
      • Earlier version: Studying the Use of Popular Destinations to Enhance Web Search Interaction
        Ryen W. White, Mikhail Bilenko, and Silviu Cucerzan. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-2007), Amsterdam, July 2007.
        (Winner of Best Paper Award)
        [PDF]

  • Learnable similarity functions and their applications in information integration (e.g., record linkage/identity uncertainty) and text mining


  • Semi-supervised clustering

    • Probabilistic Semi-Supervised Clustering with Constraints
      Sugato Basu, Mikhail Bilenko, Arindam Banerjee, and Raymond J. Mooney. In Semi-Supervised Learning, O. Chapelle, B. Schölkopf, and A. Zien (eds.), MIT Press, 2006.
      Note: this chapter summarizes the KDD and ICML papers below
      [PDF]

    • A Probabilistic Framework for Semi-Supervised Clustering
      Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004), pp.59-68, Seattle, WA, August 2004.
      (Winner of Best Research Paper Award)
      [PDF]

    • Integrating Constraints and Metric Learning in Semi-Supervised Clustering
      Mikhail Bilenko, Sugato Basu, and Raymond J. Mooney. In Proceedings of the 21st International Conference on Machine Learning (ICML-2004), pp.81-88, Banff, Canada, July 2004.
      [PDF]

    • A Comparison of Inference Techniques for Semi-supervised Clustering with Hidden Markov Random Fields
      Mikhail Bilenko and Sugato Basu. In Proceedings of the ICML-2004 Workshop on Statistical Relational Learning and its Connections to Other Fields (SRL-2004), pp.17-22, Banff, Canada, July 2004.
      [PDF]

  • Indirect learning in information integration (record linkage, information extraction), text classification, and clustering

    • Two Approaches to Handling Noisy Variation in Text Mining
      Un Yong Nahm, Mikhail Bilenko, and Raymond J. Mooney. In Proceedings of the ICML-2002 Workshop on Text Learning (TextML'2002), pp.18-27, Sydney, Australia, July 2002.
      [PDF]

Personal
In my leisure time I enjoy applying hill-climbing search and gradient descent algorithms to real-world domains.
Contact Info

Email mbilenko@microsoft.com
   
Postal Microsoft Research
One Microsoft Way
Redmond, WA 98052
USA