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Edited Series

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.

Book Chapters

C. Tang and C. Monteleoni, “On the Convergence Rate of Stochastic Gradient Descent for Strongly Convex Functions,”

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,”

R. L. Glicksman, D. L. Markell, and C. Monteleoni, “Technological Innovation, Data Analytics, and Environmental Enforcement,”

A. Choromanska, K. Choromanski, G. Jagannathan, and C. Monteleoni, “Differentially-Private Learning of Low Dimensional Manifolds,”

C. Tang and C. Monteleoni, “Can Topic Modeling Shed Light on Climate Extremes?”

C. Monteleoni, G. Schmidt, S. McQuade, “Climate Informatics: Accelerating Discovery in Climate Science with Machine Learning,”

C. Monteleoni, G. Schmidt, S. Saroha, and E. Asplund, “Tracking Climate Models,”

K. Chaudhuri, C. Monteleoni, and A. Sarwate, “Differentially Private Empirical Risk Minimization,”

S. Dasgupta, A.T. Kalai, and C. Monteleoni, “Analysis of Perceptron-Based Active Learning,”

Refereed Proceedings

M. Mohan and C. Monteleoni, “Beyond the Nyström approximation: Speeding up spectral clustering using uniform sampling and weighted kernel k-means,”

M. Mohan and C. Monteleoni, “Exploiting Sparsity to Improve the Accuracy of Nyström-based Large Scale Spectral Clustering,”

C. Tang and C. Monteleoni, “Convergence rate of stochastic k-means,”

S. McQuade and C. Monteleoni, “Online learning of volatility from multiple option term lengths,”

C. Tang and C. Monteleoni, “On Lloyd's algorithm: new theoretical insights for clustering in practice,”

S. McQuade and C. Monteleoni, “Multi-Task Learning from a Single Task: Can Different Forecast Periods be Used to Improve Each Other?”

M. Mohan, C. Tang, C. Monteleoni, T. DelSole, and B. Cash, “Seasonal Prediction Using Unsupervised Feature Learning and Regression,”

T. DelSole, C. Monteleoni, S. McQuade, M. K. Tippett, K. Pegion, and J. Shukla, “Tracking Seasonal Prediction Models,”

C. Tang and C. Monteleoni, “Detecting Extreme Events from Climate Time-Series via Topic Modeling,”

M. Mohan, D. Gálvez-López, C. Monteleoni, and G. Sibley, “Environment Selection And Hierarchical Place Recognition,”

G. Jagannathan, C. Monteleoni, and K. Pillaipakkamnatt, “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,”

A. Choromanska, K. Choromanski, G. Jagannathan, and C. Monteleoni, “Differentially-Private Learning of Low Dimensional Manifolds,”

M. Ghafarianzadeh and C. Monteleoni, “Climate Prediction via Matrix Completion,”

S. McQuade and C. Monteleoni, “Global Climate Model Tracking using Geospatial Neighborhoods,”

A. Choromanska and C. Monteleoni, “Online Clustering with Experts,”

C. Monteleoni, G. Schmidt, and S. Saroha, “Tracking Climate Models,”

N. Ailon, R. Jaiswal, and C. Monteleoni, “Streaming k-means approximation,”

K. Chaudhuri and C. Monteleoni, “Privacy-preserving logistic regression,”

S. Dasgupta, D. Hsu, and C. Monteleoni, “A general agnostic active learning algorithm,”

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.

S. Dasgupta, A.T. Kalai, and C. Monteleoni, “Analysis of perceptron-based active learning,”

C. Monteleoni and T. Jaakkola, “Online Learning of Non-stationary Sequences,”

C. Boutilier, M. Goldszmidt, C. Monteleoni, and B. Sabata, "Resource Allocation using Sequential Auctions,"

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,"

Workshop Papers

C. Tang and C. Monteleoni, “The convergence rate of stochastic k-means,”

C. Tang and C. Monteleoni, “On Lloyd's algorithm: new theoretical insights for clustering in practice,”

C. Tang and C. Monteleoni, “Scalable constant k-means approximation via heuristics on well-clusterable data,”

C. Tang and C. Monteleoni, “Scaling up Lloyd’s algorithm: stochastic and parallel block-wise optimization perspectives,”

S. McQuade and C. Monteleoni, “MRF-Based Spatial Expert Tracking of the Multi-Model Ensemble,”

M. Ghafarianzadeh and C. Monteleoni, “Climate Prediction via Matrix Completion,”

M. Ghafarianzadeh and C. Monteleoni, “Climate Prediction via Matrix Completion,”

C. Tang and C. Monteleoni, “Convergence analysis of stochastic gradient descent on strongly convex objective functions,”

S. McQuade and C. Monteleoni, “MRF-Based Spatial Expert Tracking of the Multi-Model Ensemble,”

M. Ghafarianzadeh and C. Monteleoni, “Climate Prediction via Matrix Completion,”

C. Tang and C. Monteleoni, “Convergence analysis of stochastic gradient descent on strongly convex objective functions,”

S. McQuade and C. Monteleoni, “Global Climate Model Tracking using Geospatial Neighborhoods,”

S. McQuade and C. Monteleoni, “Global Climate Model Tracking using Geospatial Neighborhoods,”

A. Choromanska and C. Monteleoni, “Online Clustering with Experts,”

A. Choromanska and C. Monteleoni, “Online Clustering with Experts,”

G. Jagannathan, C. Monteleoni, and Krishnan Pillaipakkamnatt , “A Semi-Supervised Learning Approach to Differential Privacy,”

A. Choromanska and C. Monteleoni, “Online Clustering with Experts,”

A. Choromanska and C. Monteleoni, “Online Clustering with Experts,”

C. Monteleoni, S. Saroha, and G. Schmidt, “Tracking Climate Models,”

C. Monteleoni, S. Saroha, and G. Schmidt, “Tracking Climate Models,”

N. Ailon, R. Jaiswal, and C. Monteleoni, “One-pass approximate k-means optimization,”

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

C. Monteleoni and M. Kääriäinen, "Active Learning under Arbitrary Distributions"

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.
*