Machine Learning and Intelligence
The Machine Learning and Intelligence group performs research in machine learning, information retrieval, data mining, machine understanding, and human-computer interaction. We work on new algorithms, and with large datasets, with the overall goal of advancing the science and enhancing the user's experience.
Publications
- Geoffrey Zweig, John C. Platt, Christopher Meek, Christopher J.C. Burges, Ainur Yessenalina, and Qiang Liu, Computational Approaches to Sentence Completion, in ACL 2012, ACL/SIGPARSE, July 2012
- Scott Yih, Geoffrey Zweig, and John Platt, Polarity Inducing Latent Semantic Analysis, in Experimental Methods in Natural Language Processing 2012, ACL/SIGPARSE, July 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
- Geoffrey Zweig and Chris J.C. Burges, A Challenge Set for Advancing Language Modeling, in Workshop on the Future of Language Modeling for HLT, NAACL-HLT 2012, ACL/SIGPARSE, June 2012
- Geoffrey Zweig and Christopher J.C. Burges, The Microsoft Research Sentence Completion Challenge, no. MSR-TR-2011-129, December 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
- 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
- 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
