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Edited Series

• Chen, C., D. Cooley, J. Runge, and E. Szekely, (Eds.), I. Ebert-Uphoff, D. Hammerling, C. Monteleoni, D. Nychka (Series Eds.), . NCAR Technical Note NCAR/TN-550+PROC, 2018, 151 pp, doi:10.5065/D6BZ64XQ.
• V. Lyubchich, N.C. Oza, A. Rhines, E. Szekely (Eds.), I. Ebert-Uphoff, C. Monteleoni, D. Nychka (Series Eds.), . NCAR Technical Note NCAR/TN-536+PROC, Sept 2017, doi: 10.5065/D6222SH7.
• A. Banerjee, W. Ding, J. Dy, S. Lyubchich, A. Rhines (Eds.), I. Ebert-Uphoff, C. Monteleoni, D. Nychka (Series Eds.), . NCAR Technical Note NCAR/TN-529+PROC, September 2016, 159 pages, doi: 10.5065/D6K072N6, ISBN: 978-0-9973548-1-2.

Book Chapters

• S. McQuade and C.Monteleoni, “,” Chapter 3, in Large-Scale Machine Learning in the Earth Sciences, Srivastava, Nemani, Steinhaeuser (Eds.), Data Mining and Knowledge Discovery Series, V. Kumar (Series Ed.), Chapman & Hall/CRC, pp. 33–54, August 2017. Invited.
• C. Tang and C. Monteleoni, “,” in Regularization, Optimization, Kernels, and Support Vector Machines. Johan A. K. Suykens, Marco Signoretto, and Andreas Argyriou. (Eds.), CRC Press, Taylor & Francis Group. Chapter 7, pp. 159–175, 2014.  Invited. 
• C. Monteleoni, , F. Alexander, A. Niculescu-Mizil, K. Steinhaeuser, , , M.B. Blumenthal, A.R. Ganguly, J.E. Smerdon, and M. Tedesco, “Climate Informatics,” in Computational Intelligent Data Analysis for Sustainable Development; Data Mining and Knowledge Discovery Series. Yu, T., Chawla, N., and Simoff, S. (Eds.), CRC Press, Taylor & Francis Group. Chapter 4, pp. 81–126, 2013.  Invited.

Journals & Periodicals

• L. Alexander, S. Das, Z. Ives, H.V. Jagadish, and C. Monteleoni, “Research Challenges in Financial Data Modeling and Analysis.” In Big Data, Sep 2017, 5(3): 177-188.
• R. L. Glicksman, D. L. Markell, and C. Monteleoni, “Technological Innovation, Data Analytics, and Environmental Enforcement,” in Ecology Law Quarterly, University of California, Berkeley, School of Law, Volume 44, Issue 1, 2017.  Invited. 
• , K. Choromanski, , and C. Monteleoni, “Differentially-Private Learning of Low Dimensional Manifolds,” in Theoretical Computer Science (TCS), Volume 620, pp. 91–104, March 2016.  Invited. 
• C. Tang and C. Monteleoni, “Can Topic Modeling Shed Light on Climate Extremes?” in IEEE Computing in Science and Engineering (CISE) Magazine, Special Issue on Computing & Climate. Vol. 17, no. 6, pp. 43–52, Nov./Dec. 2015. 
• C. Monteleoni, , S. McQuade, “Climate Informatics: Accelerating Discovery in Climate Science with Machine Learning,” in IEEE Computing in Science and Engineering (CISE) Magazine, Special Issue on Machine Learning. Vol. 15, no. 5, pp. 32–40, Sept.-Oct. 2013.  Invited.
• C. Monteleoni, , 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.
  • ​PDF of Tracking Climate Models
• , C. Monteleoni, and , “Differentially Private Empirical Risk Minimization,” in Journal of Machine Learning Research (JMLR), 12(Mar):1069–1109, 2011.  
  • ​PDF of Differentially Private Empirical Risk Minimization
• , , and C. Monteleoni, “Analysis of Perceptron-Based Active Learning,” in Journal of Machine Learning Research (JMLR), 10(Feb):281–
  299, 2009. 
  • ​PDF of Analysis of Perceptron-Based Active Learning

Refereed Proceedings

• S. Giffard-Roisin, M. Yang, G. Charpiat, B. Kégl, and C. Monteleoni, “Fused Deep Learning for Hurricane Track Forecast From Reanalysis Data.” In Proceedings of the 8th International Workshop on Climate Informatics (CI), 2018.
• M. Mohan and C. Monteleoni,  “Beyond the Nyström approximation: Speeding up spectral clustering using uniform sampling and weighted kernel k-means,” in Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017.
• M. Mohan and C. Monteleoni,  “Exploiting Sparsity to Improve the Accuracy of Nyström-based Large Scale Spectral Clustering,” in Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), 2017.
• C. Tang and C. Monteleoni,  “Convergence rate of stochastic k-means,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 2017. 
• S. McQuade and C. Monteleoni,  “Online learning of volatility from multiple option term lengths,” in Proceedings of the International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets (DSMM 2016), International Conference on Management of Data (SIGMOD/PODS), 2016. 
• C. Tang and C. Monteleoni,  “On Lloyd's algorithm: new theoretical insights for clustering in practice,” in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), 2016. 
• S. McQuade and C. Monteleoni,  “Multi-Task Learning from a Single Task: Can Different Forecast Periods be Used to Improve Each Other?” in Proceedings of , 2015.  
  • ​PDF of Multi-Task Learning from a Single Task
• M. Mohan, C. Tang, C. Monteleoni, , and ,  “Seasonal Prediction Using Unsupervised Feature Learning and Regression,” in Proceedings of , 2015.  
  • ​PDF of Seasonal Prediction
• , C. Monteleoni, S. McQuade, , , and ,  “Tracking Seasonal Prediction Models,” in Proceedings of , 2015.  
  • ​PDF of Tracking Seasonal Prediction Models
• C. Tang and C. Monteleoni,  “Detecting Extreme Events from Climate Time-Series via Topic Modeling,” in Machine Learning and Data Mining Approaches to Climate Science: Proceedings of the 4th International Workshop on Climate Informatics. Lakshmanan, V., Gilleland, E., McGovern, A., Tingley, M. (Eds.), Springer, 2015.  
• M. Mohan, D. Gálvez-López, C. Monteleoni, and G. Sibley, “Environment Selection And Hierarchical Place Recognition,” in Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), 2015. 
• , C. Monteleoni, and K. Pillaipakkamnatt,  “A Semi-Supervised Learning Approach to Differential Privacy,” in Proceedings of the 2013 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE Workshop on Privacy Aspects of Data Mining (PADM), 2013. 
  • ​PDF of A Semi-Supervised Learning Approach to Differential Privacy
• , , H. Kim, M. Mohan, and C. Monteleoni,  “Fast spectral clustering via the Nyström method,” in Algorithmic Learning Theory, 24th International Conference (ALT), 2013. 
  • ​PDF of Fast Spectral Clustering via the Nystrom Method
• , K. Choromanski, , and C. Monteleoni, “Differentially-Private Learning of Low Dimensional Manifolds,” in Algorithmic Learning Theory, 24th International Conference (ALT), 2013. 
  • ​PDF of Differentially-Private Learning of Low Dimensional Manifolds
• M. Ghafarianzadeh and C. Monteleoni,  “Climate Prediction via Matrix Completion,” in Proceedings of the Twenty-Seventh Conference on Artificial Intelligence (AAAI), Late-Breaking Papers Track, 2013. 
  • ​PDF of Climate Prediction via Matrix Completion
• S. McQuade and C. Monteleoni,  “Global Climate Model Tracking using Geospatial Neighborhoods,” in Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (AAAI), Computational Sustainability and AI Special Track, 2012. 
  • ​PDF of Global Climate Model Tracking using Geospatial Neighborhoods
•  and C. Monteleoni,  “Online Clustering with Experts,” in the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS), 2012. 
•  and C. Monteleoni,  “Online Clustering with Experts,” in Proceedings of ICML 2011 Workshop on Online Trading of Exploration and Exploitation 2; Journal of Machine Learning Research (JMLR) Workshop and Conference Proceedings, 2012. 
• C. Monteleoni, , and S. Saroha, “Tracking Climate Models,” in NASA Conference on Intelligent Data Understanding (CIDU), 2010.  Awarded Best Application Paper. 
  • PDF of Tracking Climate Models Here. 
  • Tracking Climate Model Presentation Slides
• , , and C. Monteleoni, “Streaming k-means approximation,” in Advances in Neural Information Processing Systems (NIPS), 2009.
  • PDF of Streaming k-means Approximation 
  • Appendix of Streaming k-means Approximation
  • Presentation Slides
•  and C. Monteleoni, “Privacy-preserving logistic regression,” in Advances in Neural Information Processing Systems (NIPS), 2008.
• , , and C. Monteleoni, “A general agnostic active learning algorithm,” in Advances in Neural Information Processing Systems (NIPS), 2007.
  • PDF of A General Agnostic Active Learning Algorithm. (long version) 
• C. Monteleoni and , “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.
  • PDF of Practical Online Active Learning for Classification
• C. Monteleoni, "Efficient Algorithms for General Active Learning," in Proceedings of the 19th Annual Conference on Learning Theory, Open Problems, (COLT), 2006.
  • PDF of Efficient Algorithms for General Active Learning
  • PDF Presentation Slides
• , , and C. Monteleoni, “Analysis of perceptron-based active learning,”
   in Proceedings of the 18th Annual Conference on Learning Theory (COLT), 2005.
  • PDF of Analysis of Perceptron-Based Active Learning 
• C. Monteleoni and , “Online Learning of Non-stationary Sequences,” in Advances in Neural Information Processing Systems (NIPS) 16, 2003.
  • PDF of Online Learning of Non-stationary Sequences  
  • PDF Presentation Slides
• 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 

• S. Giffard-Roisin, M. Yang, G. Charpiat, B. Kégl, and C. Monteleoni, "Deep Learning for Hurricane Track Forecasting from Aligned Spatio-temporal Climate Datasets," in , NIPS 2018.
  • Preprint
• C. Tang and C. Monteleoni, “Demystifying wide nonlinear auto-encoders: fast SGD convergence towards sparse representation from random initialization.” In Workshop for Women in Machine Learning, collocated with NIPS 2017.
• C. Tang and C. Monteleoni,  “The convergence rate of stochastic k-means,” in , ICML 2016.
• C. Tang and C. Monteleoni,  “On Lloyd's algorithm: new theoretical insights for clustering in practice,” in , NIPS 2015.
• C. Tang and C. Monteleoni,  “Scalable constant k-means approximation via heuristics on well-clusterable data,” in , NIPS 2015.
• C. Tang and C. Monteleoni,  “Scaling up Lloyd’s algorithm: stochastic and parallel block-wise optimization perspectives,” in the 7th NIPS Workshop on Optimization for Machine Learning (), NIPS 2014.
• S. McQuade and C. Monteleoni,  “MRF-Based Spatial Expert Tracking of the Multi-Model Ensemble,” in New Approaches for Pattern Recognition and Change Detection, session at American Geophysical Union (AGU) Fall Meeting, 2013.  
• M. Ghafarianzadeh and C. Monteleoni,  “Climate Prediction via Matrix Completion,” in Workshop on Machine Learning for Sustainability, NIPS 2013.  
• M. Ghafarianzadeh and C. Monteleoni,  “Climate Prediction via Matrix Completion,” in Workshop for Women in Machine Learning (WiML), collocated with NIPS 2013.  
• C. Tang and C. Monteleoni,  “Convergence analysis of stochastic gradient descent on strongly convex objective functions,” in Workshop for Women in Machine Learning (WiML), collocated with NIPS 2013.  
• S. McQuade and C. Monteleoni,  “MRF-Based Spatial Expert Tracking of the Multi-Model Ensemble,” in , 2013.  
  • PDF of MRF-Based Spatial Expert Tracking of the Multi-Model Ensemble
• M. Ghafarianzadeh and C. Monteleoni,  “Climate Prediction via Matrix Completion,” in , 2013.  
  • PDF of Climate Prediction via Matrix Completion
• C. Tang and C. Monteleoni,  “Convergence analysis of stochastic gradient descent on strongly convex objective functions,” in  (ROKS), 2013.  
• S. McQuade and C. Monteleoni,  “Global Climate Model Tracking using Geospatial Neighborhoods,” in , 2012.  
• S. McQuade and C. Monteleoni,  “Global Climate Model Tracking using Geospatial Neighborhoods,” in , 2012.  
•  and C. Monteleoni,  “Online Clustering with Experts,” in , 2012.  
•  and C. Monteleoni,  “Online Clustering with Experts,” in Workshop for Women in Machine Learning (WiML), collocated with NIPS 2011. 
• , 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. 
•  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.
•  and C. Monteleoni,  “Online Clustering with Experts,” in , ICML 2011.  
• C. Monteleoni, S. Saroha, and ,  “Tracking Climate Models,” in , 2010.  
• C. Monteleoni, S. Saroha, and ,  “Can machine learning techniques improve forecasts?” in Intergovernmental Panel on Climate Change (IPCC) Expert Meeting on Assessing and Combining Multi Model Climate Projections, �鶹ӰԺ, 2010.
• C. Monteleoni, S. Saroha, and ,  “Tracking Climate Models,” in Workshop on Temporal Segmentation: Perspectives from Statistics, Machine Learning, and Signal Processing, NIPS 2009. 
• 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.
• , , and C. Monteleoni, “One-pass approximate k-means optimization,” in Workshop on On-line Learning with Limited Feedback, ICML/UAI/COLT 2009.
• C. Monteleoni, , , and , “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.
  • PDF of Real-Time Prediction Using Online Learning
  • PDF Poster of Managing the Energy/Peformance Tradeoff with Machine Learning 
• , , and C. Monteleoni, “A general agnostic active learning algorithm,” in Workshop for Women in Machine Learning (WiML), Orlando, 2007. 
• C. Monteleoni and , "Active Learning under Arbitrary Distributions" in , NIPS 2005.

Theses

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

  • ​�ċPDF of  Learning with Online Constraints

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