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Mikhail Bilenkombilenko@microsoft.com |
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I am a Researcher in the Text Mining,
Search, and Navigation (TMSN) group at
Microsoft Research. I am broadly interested in
machine learning, data mining, and information retrieval tasks that arise in the context of large textual and
behavioral datasets. Specific problems on which I focus include
record linkage, semi-supervised clustering, and improving information retrieval
and targeted advertising via user behavior modeling.
I am also interested in information extraction, recommender systems, and
methods related to learning similarity (distance, kernel) functions.
I completed my Ph.D. in the Department of Computer Sciences at the University of Texas at Austin in the summer of 2006. I was a member of the Machine Learning group led by Prof. Raymond Mooney. Along the way, I spent the summer of 2002 at IBM T.J. Watson Research Center, and the summer/fall of 2004 at Google. |
- Learning from large datasets of user behavior
- Enhancing Web Search by Promoting Multiple Search Engine Usage
Ryen W. White, Matthew Richardson, Mikhail Bilenko, and Allison Heath. To appear in Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-2008), Singapore, July 2008.
- 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. To appear in Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-2008), Singapore, July 2008.
[PDF] [bib] - Mining the Search Trails of Surfing Crowds: Identifying Relevant Websites From User Activity
Mikhail Bilenko and Ryen W. White. To appear in Proceedings of the 17th International World Wide Web Conference (WWW-2008), pp.51-60, Beijing, April 2008.
[PDF] [bib] - 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), pp.159-166, Amsterdam, July 2007.
(Winner of Best Paper Award)
[PDF] [PS.gz] [bib]
- Enhancing Web Search by Promoting Multiple Search Engine Usage
- Learnable similarity functions and their applications in
information integration
(e.g., record linkage/identity uncertainty) and text mining
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RIDDLE: Repository of Information on Duplicate Detection, Record Linkage, and Identity Uncertainty
- Adaptive Blocking: Learning to Scale Up Record Linkage and Clustering
Mikhail Bilenko, Beena Kamath, and Raymond J. Mooney. In Proceedings of the 6th IEEE International Conference on Data Mining (ICDM-2006), pp.87-96, Hong Kong, December 2006.
[PDF] [PS.gz] [bib] - Adaptive Product Normalization: Using Online Learning
for Record Linkage in Comparison Shopping
Mikhail Bilenko, Sugato Basu, and Mehran Sahami. In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM-2005), pp.58-65, Houston, TX, November 2005.
[PDF] [PS.gz] [bib] - Adaptive Duplicate Detection Using
Learnable String Similarity Measures
Mikhail Bilenko and Raymond J. Mooney. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2003), pp.39-48, Washington, DC, August 2003.
[PDF] [PS.gz] [bib] - On Evaluation and Training-Set Construction
for Duplicate Detection
Mikhail Bilenko and Raymond J. Mooney. In Proceedings of the KDD-2003 Workshop on Data Cleaning, Record Linkage, and Object Consolidation, pp.7-12, Washington, DC, August 2003.
[PDF] [PS.gz] [bib]
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RIDDLE: Repository of Information on Duplicate Detection, Record Linkage, and Identity Uncertainty
- 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] [PS.gz] [bib] - 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] [PS.gz] [bib] - 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] [PS.gz] [bib] - 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] [PS.gz] [bib]
- Probabilistic Semi-Supervised Clustering with Constraints
- 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] [PS.gz] [bib]
- Two Approaches to Handling Noisy Variation in Text Mining
| In my leisure time I enjoy applying hill-climbing search and gradient descent algorithms to real-world domains. |
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