C. Monteleoni: Research

C. Monteleoni: Research

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For the Powerpoint talks, please install Texpoint to properly view the math symbols.

Journal Articles

C. Monteleoni, G. Schmidt, S. Saroha, and E. Asplund, “Tracking Climate Models,” in Journal of Statistical Analysis and Data Mining: Special Issue: Best of CIDU 2010. Volume 4, Issue 4, pp. 72–392, August 2011. Invited.
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K. Chaudhuri, C. Monteleoni, and A. Sarwate, “Differentially Private Empirical Risk Minimization,” in Journal of Machine Learning Research (JMLR), 12(Mar):1069-1109, 2011.

S. Dasgupta, A.T. Kalai, and C. Monteleoni, “Analysis of Perceptron-Based Active Learning,” in Journal of Machine Learning Research (JMLR), 10(Feb):281--299, 2009.

Refereed Proceedings

A. Choromanska and C. Monteleoni, “Online Clustering with Experts,” to appear in the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS), 2012.

C. Monteleoni, G. Schmidt, and S. Saroha, “Tracking Climate Models,” in NASA Conference on Intelligent Data Understanding (CIDU), 2010. Awarded Best Application Paper.
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N. Ailon, R. Jaiswal, and C. Monteleoni, “Streaming k-means approximation,” in Advances in Neural Information Processing Systems (NIPS), 2009.
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H. Dutta, D. Waltz, A. Moschitti, D. Pighin, P. Gross, C. Monteleoni, A. Salleb-Aouissi, A. Boulanger, M. Pooleery, and R. Anderson, “Estimating the Time Between Failures of Electrical Feeders in the New York Power Grid,” in Next Generation Data Mining Summit, 2009.

K. Chaudhuri and C. Monteleoni, “Privacy-preserving logistic regression,” in Advances in Neural Information Processing Systems (NIPS), 2008.
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S. Dasgupta, D. Hsu, and C. Monteleoni, “A general agnostic active learning algorithm,” in Advances in Neural Information Processing Systems (NIPS), 2007.
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C. Monteleoni and M. Kääriäinen, “Practical Online Active Learning for Classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Online Learning for Classification Workshop, (CVPR), 2007.

C. Monteleoni, "Efficient Algorithms for General Active Learning," in Proceedings of the 19th Annual Conference on Learning Theory, Open Problems, (COLT), 2006.
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S. Dasgupta, A.T. Kalai, and C. Monteleoni, “Analysis of perceptron-based active learning,” in Proceedings of the 18th Annual Conference on Learning Theory (COLT), 2005.

C. Monteleoni and T. Jaakkola, “Online Learning of Non-stationary Sequences,” in Advances in Neural Information Processing Systems (NIPS) 16, 2003.
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C. Boutilier, M. Goldszmidt, C. Monteleoni, and B. Sabata, "Resource Allocation using Sequential Auctions," in Agent-Mediated Electronic Commerce II, Lecture Notes in Artificial Intelligence 1788. Springer-Verlag, 2000.

A. Kehler, J.R. Hobbs, D. Appelt, J. Bear, M. Caywood, D. Israel, M. Kameyama, D. Martin, and C. Monteleoni, "Information Extraction, Research and Applications: Current Progress and Future Directions," in TIPSTER Text Program Phase III Proceedings, 1999.

Workshop Papers

A. Choromanska and C. Monteleoni, “Online Clustering with Experts,” in Workshop for Women in Machine Learning (WiML), collocated with NIPS 2011.

G. Jagannathan, C. Monteleoni, and Krishnan Pillaipakkamnatt , “A Semi-Supervised Learning Approach to Differential Privacy,” in Workshop for Women in Machine Learning (WiML), collocated with NIPS 2011.

A. Choromanska and C. Monteleoni, “Online Clustering with Experts,” in the Sixth Annual Machine Learning Symposium, New York Academy of Sciences, 2011. Student Paper Award, Third Place.

A. Choromanska and C. Monteleoni, “Online Clustering with Experts,” in Workshop on Online Trading of Exploration and Exploitation 2, ICML 2011.

C. Monteleoni, S. Saroha, and G. Schmidt, “Tracking Climate Models,” in The Learning Workshop (Snowbird), 2010.

C. Monteleoni, S. Saroha, and G. Schmidt, “Can machine learning techniques improve forecasts?” in Intergovernmental Panel on Climate Change (IPCC) Expert Meeting on Assessing and Combining Multi Model Climate Projections, Boulder, 2010.

C. Monteleoni, S. Saroha, and G. Schmidt, “Tracking Climate Models,” in Workshop on Temporal Segmentation: Perspectives from Statistics, Machine Learning, and Signal Processing, NIPS 2009.

N. Ailon, R. Jaiswal, and C. Monteleoni, “One-pass approximate k-means optimization,” in Workshop on On-line Learning with Limited Feedback, ICML/UAI/COLT 2009.

C. Monteleoni, H. Balakrishnan, N. Feamster, and T. Jaakkola, “Real-Time Prediction Using Online Learning: Application to Energy Management in Wireless Networks.” in Forum on Analytics, San Diego, 2007. Long version: “Managing the 802.11 Energy/Performance Tradeoff with Machine Learning,” in MIT-LCS-TR-971 Technical Report, MIT Computer Science and Artificial Intelligence Lab, 2004.
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S. Dasgupta, D. Hsu, and C. Monteleoni, “A general agnostic active learning algorithm,” in Workshop for Women in Machine Learning (WiML), Orlando, 2007.

C. Monteleoni and M. Kääriäinen, "Active Learning under Arbitrary Distributions" in Workshop on Value of Information in Inference, Learning and Decision-Making, NIPS 2005.

Theses

"Learning with Online Constraints: Shifting Concepts and Active Learning," PhD Thesis, CSAIL Technical Report 2006-057, MIT, August 2006.

"Online Learning of Non-stationary Sequences," SM Thesis, MIT Artificial Intelligence Technical Report 2003-011, May 2003.

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