Publications

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The slides and posters are licensed to me under Creative Commons.

Edited Series

• Chen, C., D. Cooley, J. Runge, and E. Szekely, (Eds.), I. Ebert-Uphoff, D. Hammerling, C. Monteleoni, D. Nychka (Series Eds.), Proceedings of the 8th International Workshop on Climate Informatics: CI 2018. 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.), Proceedings of the 7th International Workshop on Climate Informatics: CI 2017. 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.), Proceedings of the Sixth International Workshop on Climate Informatics: CI 2016. 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, “Spatiotemporal Global Climate Model Tracking,” 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, “On the Convergence Rate of Stochastic Gradient Descent for Strongly Convex Functions,” 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, G.A. Schmidt, F. Alexander, A. Niculescu-Mizil, K. Steinhaeuser, M. Tippett, A. Banerjee, 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. 
  • ​Download the PDF on the SSRN Site
• A. Choromanska, K. Choromanski, G. Jagannathan, and C. Monteleoni, “Differentially-Private Learning of Low Dimensional Manifolds,” in Theoretical Computer Science (TCS), Volume 620, pp. 91–104, March 2016.  Invited. 
  • ​Publisher Link
• 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, G. Schmidt, 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.
  • ​Publisher Link
• 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.
  • ​PDF of Tracking Climate Models
• K. Chaudhuri, C. Monteleoni, and A. Sarwate, “Differentially Private Empirical Risk Minimization,” in Journal of Machine Learning Research (JMLR), 12(Mar):1069–1109, 2011.  
  • ​PDF of Differentially Private Empirical Risk Minimization
• 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. 
  • ​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. 
  • ​Download on the PMLR Site
• 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. 
  • ​Download from the ACM DL Digital Library
• 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. 
  • ​Download on the PMLR Site
• 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 The Fifth International Workshop on Climate Informatics, 2015.  
  • ​PDF of Multi-Task Learning from a Single Task
• M. Mohan, C. Tang, C. Monteleoni, T. DelSole, and B. Cash,  “Seasonal Prediction Using Unsupervised Feature Learning and Regression,” in Proceedings of The Fifth International Workshop on Climate Informatics, 2015.  
  • ​PDF of Seasonal Prediction
• T. DelSole, C. Monteleoni, S. McQuade, M. K. Tippett, K. Pegion, and J. Shukla,  “Tracking Seasonal Prediction Models,” in Proceedings of The Fifth International Workshop on Climate Informatics, 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.  
  • ​Publisher Link
• 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. 
  • ​Publisher Link
• G. Jagannathan, 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
• A. Choromanska, T. Jebara, 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
• A. Choromanska, K. Choromanski, G. Jagannathan, 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
• A. Choromanska and C. Monteleoni,  “Online Clustering with Experts,” in the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS), 2012. ​
• A. Choromanska 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. 
  • ​PDF of Online Clustering with Experts
• C. Monteleoni, G. Schmidt, 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
• N. Ailon, R. Jaiswal, 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
• K. Chaudhuri and C. Monteleoni, “Privacy-preserving logistic regression,” in Advances in Neural Information Processing Systems (NIPS), 2008.
  • Updated journal version
• S. Dasgupta, D. Hsu, 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 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.
  • PDF of Practical Online Active Learning for Classification
  • Powerpoint Presentation
• 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
• 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.
  • PDF of Analysis of Perceptron-Based Active Learning 
  • Powerpoint Presentation
• C. Monteleoni and T. Jaakkola, “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 Modeling and decision-making in the spatiotemporal domain workshop, 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 Workshop on Advances in non-convex analysis and optimization, ICML 2016.
• C. Tang and C. Monteleoni,  “On Lloyd's algorithm: new theoretical insights for clustering in practice,” in NIPS 2015 Workshop on Non-convex Optimization for Machine Learning: Theory and Practice, NIPS 2015.
• C. Tang and C. Monteleoni,  “Scalable constant k-means approximation via heuristics on well-clusterable data,” in NIPS 2015 Workshop on Learning Faster from Easy Data, 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 (OPT2014), 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 The Third International Workshop on Climate Informatics, 2013.  
  • PDF of MRF-Based Spatial Expert Tracking of the Multi-Model Ensemble
• M. Ghafarianzadeh and C. Monteleoni,  “Climate Prediction via Matrix Completion,” in The Third International Workshop on Climate Informatics, 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 International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines (ROKS), 2013.  
• S. McQuade and C. Monteleoni,  “Global Climate Model Tracking using Geospatial Neighborhoods,” in The Second International Workshop on Climate Informatics, 2012.  
• S. McQuade and C. Monteleoni,  “Global Climate Model Tracking using Geospatial Neighborhoods,” in The Learning Workshop (Snowbird), 2012.  
• A. Choromanska and C. Monteleoni,  “Online Clustering with Experts,” in The Learning Workshop (Snowbird), 2012.  
• 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. 
• 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.
• 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.
  • PDF of Real-Time Prediction Using Online Learning
  • PDF Poster of Managing the Energy/Peformance Tradeoff with Machine Learning 
• 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. 

  • ​​PDF of  Learning with Online Constraints

  • Powerpoint Presentation​

• "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