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Edited Series

  • Chen, C., D. Cooley, J. Runge, and E. Szekely, (Eds.),听I. Ebert-Uphoff, D. Hammerling, C. Monteleoni, D. Nychka (Series Eds.),听. 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,听W. Ding, J. Dy, S. Lyubchich, A. Rhines (Eds.), I. Ebert-Uphoff, C. Monteleoni, D. Nychka (Series Eds.),听.听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, 鈥�,鈥� 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鈥�54, August 2017. Invited.
  • C. Tang and C. Monteleoni,听鈥�,鈥澨齣n 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鈥�175, 2014.听听Invited.听
  • C. Monteleoni,听,听F. Alexander, A. Niculescu-Mizil, K. Steinhaeuser,听,听, M.B. Blumenthal, A.R. Ganguly, J.E. Smerdon, and M. Tedesco,听鈥�Climate Informatics,鈥澨齣n听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鈥�126, 2013.听听Invited.

Journals & Periodicals

  • L. Alexander, S. Das, Z. Ives, H.V. Jagadish, and C. Monteleoni, 鈥淩esearch Challenges in Financial Data Modeling and Analysis.鈥� In Big Data, Sep 2017, 5(3): 177-188.
  • R. L. Glicksman, D. L. Markell, and C. Monteleoni,听鈥淭echnological Innovation, Data Analytics, and Environmental Enforcement,鈥澨齣n Ecology Law Quarterly, University of California, Berkeley, School of Law, Volume 44, Issue 1, 2017.听听Invited.听
  • ,听K. Choromanski,听,听and C. Monteleoni,听鈥淒ifferentially-Private Learning of Low Dimensional Manifolds,鈥澨齣n Theoretical Computer Science (TCS), Volume 620, pp. 91鈥�104, March 2016.听听Invited.听
  • C. Tang and C. Monteleoni,听鈥淐an Topic Modeling Shed Light on Climate Extremes?鈥澨齣n听IEEE Computing in Science and Engineering (CISE) Magazine, Special Issue on Computing & Climate. Vol. 17, no. 6, pp. 43鈥�52,听Nov./Dec.听2015.听
  • C. Monteleoni,听,听S. McQuade,听鈥淐limate Informatics: Accelerating Discovery in Climate Science with Machine Learning,鈥澨齣n听IEEE Computing in Science and Engineering (CISE) Magazine, Special Issue on Machine Learning. Vol. 15, no. 5, pp. 32鈥�40,听Sept.-Oct.听2013.听听Invited.
  • C. Monteleoni,听,听S. Saroha, and E. Asplund,听鈥淭racking Climate Models,鈥澨齣n听Journal of听Statistical Analysis and Data Mining:听 Special Issue: Best of CIDU 2010. Volume 4, Issue 4, pp. 72鈥�392, August 2011.听听Invited.
  • , C. Monteleoni, and听,听鈥淒ifferentially Private Empirical Risk Minimization,鈥澨齣n听Journal of Machine Learning Research (JMLR),听12(Mar):1069鈥�1109, 2011.听听
  • ,听, and C. Monteleoni, 鈥淎nalysis of Perceptron-Based Active Learning,鈥澨齣n听Journal of Machine Learning Research (JMLR), 10(Feb):281鈥�
    299, 2009.听

Refereed Proceedings

  • S. Giffard-Roisin, M. Yang, G. Charpiat, B. K茅gl, and C. Monteleoni, 鈥淔used Deep Learning for Hurricane Track Forecast From Reanalysis Data.鈥� In Proceedings of the 8th International Workshop on Climate Informatics (CI), 2018.
  • M. Mohan and听C. Monteleoni,听听鈥淏eyond the听Nystr枚m听approximation: Speeding up spectral clustering using uniform sampling and weighted kernel听k-means,鈥� in Proceedings of the 26th International Joint Conference on Artificial Intelligence听(IJCAI), 2017.
  • M. Mohan and听C. Monteleoni,听听鈥淓xploiting Sparsity to Improve the Accuracy of Nystr枚m-based Large Scale Spectral Clustering,鈥� in Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), 2017.
  • C. Tang and听C. Monteleoni,听听鈥淐onvergence rate of stochastic听k-means,鈥澨齣n Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 2017.听
  • S. McQuade and听C. Monteleoni,听听鈥淥nline learning of volatility from multiple option term lengths,鈥澨齣n听Proceedings of听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.听
  • C. Tang and听C. Monteleoni,听听鈥淥n Lloyd's algorithm: new theoretical insights for clustering in practice,鈥澨齣n Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.听
  • S. McQuade and听C. Monteleoni,听听鈥淢ulti-Task Learning from a Single Task: Can Different Forecast Periods be Used to Improve Each Other?鈥澨齣n听Proceedings of听, 2015.听听
  • M. Mohan, C. Tang, C. Monteleoni,听, and听,听听鈥淪easonal Prediction Using Unsupervised Feature Learning and Regression,鈥澨齣n听Proceedings of听, 2015.听听
  • , C. Monteleoni, S. McQuade,听,听,听and听,听听鈥淭racking Seasonal Prediction Models,鈥澨齣n Proceedings of听, 2015.听听
  • C. Tang and C. Monteleoni,听听鈥淒etecting Extreme Events from Climate Time-Series via Topic Modeling,鈥澨齣n 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.听听
  • M. Mohan, D.听G谩lvez-L贸pez, C. Monteleoni, and G. Sibley,听鈥淓nvironment Selection And Hierarchical Place Recognition,鈥澨齣n Proceedings of the 2015 IEEE International Conference on Robotics听and Automation (ICRA), 2015.听
  • , C. Monteleoni, and K. Pillaipakkamnatt,听听鈥淎 Semi-Supervised Learning Approach to Differential Privacy,鈥澨齣n Proceedings of the 2013 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE Workshop on Privacy Aspects of Data Mining (PADM), 2013.听
  • ,听, H. Kim, M. Mohan, and C. Monteleoni,听听鈥淔ast spectral clustering via the Nystr枚m method,鈥澨齣n Algorithmic Learning Theory, 24th International Conference听(ALT), 2013.听
  • ,听K. Choromanski,听,听and C. Monteleoni,听鈥淒ifferentially-Private Learning of Low Dimensional Manifolds,鈥澨齣n Algorithmic Learning Theory, 24th International Conference听(ALT), 2013.听
  • M. Ghafarianzadeh and C. Monteleoni,听听鈥淐limate Prediction via Matrix Completion,鈥澨齣n Proceedings of the Twenty-Seventh Conference on Artificial Intelligence (AAAI),听Late-Breaking Papers Track,听2013.听
  • S. McQuade and C. Monteleoni,听听鈥淕lobal Climate Model Tracking using Geospatial Neighborhoods,鈥澨齣n Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (AAAI),听Computational Sustainability and AI Special Track,听2012.听
  • 听and C. Monteleoni,听听鈥淥nline Clustering with Experts,鈥澨齣n the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS), 2012.听
  • 听and C. Monteleoni,听听鈥淥nline Clustering with Experts,鈥澨齣n Proceedings of ICML 2011 Workshop on Online Trading of Exploration and Exploitation 2; Journal of Machine Learning Research (JMLR) Workshop and Conference Proceedings, 2012.听
  • C. Monteleoni,听, and听S. Saroha,听鈥淭racking Climate Models,鈥澨齣n NASA Conference on Intelligent Data Understanding (CIDU), 2010.听听Awarded Best Application Paper.听
  • ,听, and C. Monteleoni, 鈥淪treaming听k-means approximation,鈥澨齣n Advances in Neural Information Processing Systems (NIPS), 2009.
  • 听and C. Monteleoni, 鈥淧rivacy-preserving logistic regression,鈥澨齣n Advances in Neural Information Processing Systems (NIPS), 2008.
  • ,听, and C. Monteleoni, 鈥淎 general agnostic active learning algorithm,鈥澨齣n Advances in Neural Information Processing Systems (NIPS), 2007.
  • C. Monteleoni and听, 鈥淧ractical听Online Active Learning for Classification,鈥澨齣n听Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,听Online Learning for Classification Workshop,听(CVPR), 2007.
  • C. Monteleoni,听"Efficient Algorithms for General Active Learning,"听in Proceedings of the 19th Annual Conference on Learning Theory, Open Problems, (COLT), 2006.
  • ,听, and C. Monteleoni, 鈥淎nalysis of perceptron-based active learning,鈥�
    听in听Proceedings of the听18th Annual Conference on Learning Theory (COLT), 2005.
  • C. Monteleoni and听, 鈥淥nline Learning of Non-stationary Sequences,鈥澨齣n Advances in Neural Information Processing Systems (NIPS) 16, 2003.
  • C. Boutilier, M. Goldszmidt, C. Monteleoni, and B. Sabata, "Resource Allocation using Sequential Auctions,"听in Agent-Mediated Electronic Commerce II, Lecture Notes in Artificial Intelligence 1788. Springer-Verlag, 2000.听
  • A. Kehler, J.R. Hobbs, D. Appelt, J. Bear, M. Caywood, D. Israel, M. Kameyama, D. Martin, and C. Monteleoni,听"Information Extraction, Research and Applications: Current Progress and Future Directions,"听in TIPSTER Text Program Phase III Proceedings, 1999.听

Workshop Papers听

  • S. Giffard-Roisin, M. Yang, G. Charpiat, B. K茅gl, and C. Monteleoni, "Deep Learning for Hurricane Track Forecasting from Aligned Spatio-temporal Climate Datasets," in ,听NIPS 2018.
  • C. Tang and C. Monteleoni, 鈥淒emystifying wide nonlinear auto-encoders: fast SGD convergence towards sparse representation from random initialization.鈥� In Workshop for Women in Machine Learning, collocated with NIPS 2017.
  • C. Tang and C. Monteleoni,听听鈥淭he convergence rate of stochastic听k-means,鈥澨齣n听, ICML 2016.
  • C. Tang and C. Monteleoni,听听鈥淥n Lloyd's algorithm: new theoretical insights for clustering in practice,鈥澨齣n听, NIPS 2015.
  • C. Tang and C. Monteleoni,听听鈥淪calable constant听k-means approximation via heuristics on well-clusterable data,鈥澨齣n听, NIPS 2015.
  • C. Tang and C. Monteleoni,听听鈥淪caling up Lloyd鈥檚 algorithm: stochastic and parallel block-wise optimization perspectives,鈥澨齣n the 7th NIPS Workshop on Optimization for Machine Learning (), NIPS 2014.
  • S. McQuade and C. Monteleoni,听听鈥淢RF-Based Spatial Expert Tracking of the Multi-Model Ensemble,鈥澨齣n New Approaches for Pattern Recognition and Change Detection, session at American Geophysical Union (AGU) Fall Meeting, 2013.听听
  • M. Ghafarianzadeh and C. Monteleoni,听听鈥淐limate Prediction via Matrix Completion,鈥澨齣n Workshop on Machine Learning for Sustainability, NIPS 2013.听听
  • M. Ghafarianzadeh and C. Monteleoni,听听鈥淐limate Prediction via Matrix Completion,鈥澨齣n听Workshop for Women in Machine Learning (WiML), collocated with听NIPS听2013.听听
  • C. Tang and C. Monteleoni,听听鈥淐onvergence analysis of stochastic gradient descent on strongly convex objective functions,鈥澨齣n听Workshop for Women in Machine Learning (WiML), collocated with听NIPS听2013.听听
  • S. McQuade and C. Monteleoni,听听鈥淢RF-Based Spatial Expert Tracking of the Multi-Model Ensemble,鈥澨齣n听, 2013.听听
  • M. Ghafarianzadeh and C. Monteleoni,听听鈥淐limate Prediction via Matrix Completion,鈥澨齣n听, 2013.听听
  • C. Tang and C. Monteleoni,听听鈥淐onvergence analysis of stochastic gradient descent on strongly convex objective functions,鈥澨齣n听听(ROKS), 2013.听听
  • S. McQuade and C. Monteleoni,听听鈥淕lobal Climate Model Tracking using Geospatial Neighborhoods,鈥澨齣n听, 2012.听听
  • S. McQuade and C. Monteleoni,听听鈥淕lobal Climate Model Tracking using Geospatial Neighborhoods,鈥澨齣n听, 2012.听听
  • 听and C. Monteleoni,听听鈥淥nline Clustering with Experts,鈥澨齣n听, 2012.听听
  • 听and C. Monteleoni,听听鈥淥nline Clustering with Experts,鈥澨齣n听Workshop for Women in Machine Learning (WiML), collocated with听NIPS 2011.听
  • , C. Monteleoni, and Krishnan Pillaipakkamnatt听,听听鈥淎 Semi-Supervised Learning Approach to Differential Privacy,鈥澨齣n听Workshop for Women in Machine Learning (WiML),听collocated with听NIPS 2011.听
  • 听and C. Monteleoni,听听鈥淥nline Clustering with Experts,鈥澨齣n the Sixth Annual Machine Learning Symposium, New York Academy of Sciences, 2011.听听Student Paper Award, Third Place.
  • 听and C. Monteleoni,听听鈥淥nline Clustering with Experts,鈥澨齣n听, ICML 2011.听听
  • C. Monteleoni,听S. Saroha,听and听,听听鈥淭racking Climate Models,鈥澨齣n听, 2010.听听
  • C. Monteleoni,听S. Saroha,听and听,听听鈥淐an machine learning techniques improve forecasts?鈥澨齣n Intergovernmental Panel on Climate Change (IPCC) Expert Meeting on Assessing and Combining听Multi Model Climate Projections, Boulder, 2010.
  • C. Monteleoni,听S. Saroha,听and听,听听鈥淭racking Climate Models,鈥澨齣n Workshop on Temporal Segmentation: Perspectives from Statistics, Machine Learning, and Signal Processing, NIPS 2009.听
  • H. Dutta, D. Waltz, A. Moschitti, D. Pighin, P. Gross, C. Monteleoni,听A. Salleb-Aouissi, A. Boulanger, M. Pooleery, and R. Anderson,听鈥淓stimating the Time Between Failures of Electrical Feeders in the New York Power Grid,鈥澨齣n Next Generation Data Mining Summit, 2009.
  • ,听, and C. Monteleoni, 鈥淥ne-pass approximate听k-means optimization,鈥澨齣n Workshop on On-line Learning with Limited Feedback, ICML/UAI/COLT 2009.
  • C. Monteleoni,听,听, and听,听鈥淩eal-Time Prediction Using Online Learning: Application to Energy Management in Wireless Networks.鈥澨齣n Forum on Analytics, San Diego, 2007.听听Long version:听鈥淢anaging the 802.11 Energy/Performance Tradeoff with Machine Learning,鈥澨齣n MIT-LCS-TR-971听Technical Report, MIT Computer Science and Artificial Intelligence Lab, 2004.
  • ,听, and C. Monteleoni,听鈥淎 general agnostic active learning algorithm,鈥澨齣n听Workshop for Women in Machine Learning (WiML), Orlando, 2007.听
  • C. Monteleoni and听,听"Active Learning under Arbitrary Distributions"听in听,听NIPS 2005.

Theses