Growing a List
Benjamin Letham, Katherine Heller, and Cynthia Rudin. Growing a List. Technical Report, MIT Operations Research Center.
pdf bibMachine Learning that Incorporates Operational Costs
Theja Tulabandhula and Cynthia Rudin. The Influence of Operational Cost on Estimation. Proceedings of the International Symposium on Artificial Intelligence and Mathematics (ISAIM), 2012.
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Longer Version
pdf bibTheja Tulabandhula, Cynthia Rudin, Patrick Jaillet. The Machine Learning and Traveling Repairman Problem. Proceedings of the Second International Conference on Algorithmic Decision Theory (ADT), 2011.
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Longer Version
pdf bibCollective Intelligence
Seyda Ertekin, Haym Hirsh, Cynthia Rudin. Approximating the Wisdom of the Crowd. Proceedings of the Second Workshop on Computational Social Science and the Wisdom of Crowds (NIPS 2011).
pdf bib Haym's Slides
Seyda Ertekin, Haym Hirsh, Cynthia Rudin. Learning to Predict the Wisdom of Crowds. Proceedings of Collective Intelligence, 2011.
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Longer VersionSequential Event Prediction and/or Association Rules
Benjamin Letham, Cynthia Rudin, Tyler McCormick and David Madigan. Building Interpretable Classifiers with Rules using Bayesian Analysis. Submitted, available as tech report:
pdf bibAllison Chang, Cynthia Rudin, and Dimitris Bertsimas. Ordered Rules for Classification: A Discrete Optimization Approach to Associative Classification. Shorter version accepted to NIPS 2012, available on DSPACE here: OR 386-11
pdf bibBenjamin Letham, Cynthia Rudin, and David Madigan. Sequential Event Prediction. Submitted, available on DSPACE, here: OR 387-11
pdf bibCynthia Rudin, Benjamin Letham, Ansaf Salleb-Aouissi, Eugene Kogan and David Madigan. Sequential Event Prediction with Association Rules. Proceedings of the 24th Annual Conference on Learning Theory (COLT), 2011.
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Cynthia Rudin, Benjamin Letham, Eugene Kogan and David Madigan. A Learning Theory Framework for Association Rules and Sequential Events (longer version of COLT paper above)
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Tyler McCormick, Cynthia Rudin, David Madigan. A Hierarchical Model for Association Rule Mining of Sequential Events: An Approach to Automated Medical Symptom Prediction. Annals of Applied Statistics, 2012.
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Energy Grid Projects
Cynthia Rudin, David Waltz, Roger N. Anderson, Albert Boulanger, Ansaf Salleb-Aouissi, Maggie Chow, Haimonti Dutta, Philip Gross, Bert Huang, Steve Ierome, Delfine Isaac, Arthur Kressner, Rebecca J. Passonneau, Axinia Radeva, Leon Wu. Machine Learning for the New York City Power Grid. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No 2. February 2012. (Spotlight Paper for the February 2012 Issue.)
pdf bibCynthia Rudin, Rebecca Passonneau, Axinia Radeva, Haimonti Dutta, Steve Ierome, Delfina Isaac. A Process for Predicting Manhole Events in Manhattan. Machine Learning, Volume 80, pages 1-31, 2010.
pdf bibCynthia Rudin, Rebecca Passonneau, Axinia Radeva, Steve Ierome, Delfina Isaac. 21st-Century Data Miners Meet 19th-Century Electrical Cables. IEEE Computer, volume 44 no. 6, pages 103-105, June 2011. (One of three articles featured on the cover.)
Link pdf bibRebecca Passonneau, Cynthia Rudin, Axinia Radeva, Ashish Tomar and Boyi Xie. Treatment Effect of Repairs to an Electrical Grid: Leveraging a Machine Learned Model of Structure Vulnerability. Proceedings of the KDD Workshop on Data Mining Applications in Sustainability (SustKDD), 17th Annual ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2011.
pdf bibRebecca Passonneau, Cynthia Rudin, Axinia Radeva, Zhi An Liu. Reducing Noise in Labels and Features for a Real World Dataset: Application of NLP Corpus Annotation Methods. Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing, 2009.
pdf bibAxinia Radeva, Cynthia Rudin, Rebecca Passonneau and Delfina Isaac. Report Cards for Manholes: Eliciting Expert Feedback for a Machine Learning Task. Proceedings of the International Conference on Machine Learning and Applications, 2009. (Winner of Best Poster Award.)
pdf bibHaimonti Dutta, Cynthia Rudin, Becky Passonneau, Fred Seibel, Nandini Bhardwaj, Axinia Radeva, Zhi An Liu, Steve Ierome, Delfina Isaac. Visualization of Manhole and Precursor-Type Events for the Manhattan Electrical Distribution System. Workshop on GeoVisualization of Dynamics, Movement and Change, 11th AGILE International Conference on Geographic Information Science, 2008.
pdf bibLeon Wu, Timothy Teravainen, Gail Kaiser, Roger Anderson, Albert Boulanger, and Cynthia Rudin. Estimation of System Reliability Using a Semiparametric Model. Proceedings of IEEE EnergyTech, 2011.
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Leon Wu, Gail Kaiser, Cynthia Rudin, David Waltz, Roger Anderson, Albert Boulanger, Ansaf Salleb-Aouissi, Haimonti Dutta, and Manoj Poolery. Evaluating Machine Learning for Improving Power Grid Reliability. Proceedings of the ICML 2011 workshop on "Machine Learning for Global Challenges," International Conference on Machine Learning, 2011.
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Leon Wu, Gail Kaiser, Cynthia Rudin, Roger Anderson. Data Quality Assurance and Performance Measurement of Data Mining for Preventive Maintenance of Power Grid. Proceedings of the KDD Workshop on Data Mining for Service and Maintenance (KDD4Service), 17th Annual ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2011.
pdf bibPapular press articles about this topic:
- Energy Daily article: MIT Sloan Professor's Ranking of Manholes Prioritizes Repairs and Maintenance
- Science News article: Machine vs. Manhole, appearing also in U.S. News and World Report, WIRED Science, Slashdot, Discovery News / Discovery Channel
- CIO Magazine: "Don't blow your top," Finish section, Sept 1 issue, 2010
Supervised Ranking
Allison Chang, Cynthia Rudin, Michael Cavaretta, Robert Thomas and Gloria Chou. How to Reverse-Engineer Quality Rankings.
Machine Learning. DOI 10.1007/s10994-012-5295-6
pdf bibPapular press article about this topic:
- Businessweek article: How to Improve Product Rankings
- MIT Sloan Experts blog article: Product quality ratings: New research shows secret formulas yield questionable resultsSeyda Ertekin and Cynthia Rudin. On Equivalence Relationships Between Classification and Ranking Algorithms. Journal of Machine Learning Research, Volume 12, pages 2905-2929, 2011.
pdf bibDimitris Bertsimas, Allison Chang, Cynthia Rudin. A Discrete Optimization Approach to Supervised Ranking. Proceedings of the 5th INFORMS Workshop on Data Mining and Health Informatics (DM-HI 2010), 2010.
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Longer Version on DSPACE, paper OR 388-11 here. Finalist for Data Mining Best Student Paper Award, INFORMS 2011.
pdf bibCynthia Rudin. The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List. Journal of Machine Learning Research, Volume 10, pages 2233-2271, 2009.
pdf bibCynthia Rudin. Ranking with a P-Norm Push. Proceedings of the Nineteenth Annual Conference on Computational Learning Theory (COLT), pages 589-604, 2006.
pdf bibHeng Ji, Cynthia Rudin, Ralph Grishman. Re-Ranking Algorithms for Name Tagging. In Proc. Human Language Technology conference - North American chapter of the Association for Computational Linguistics annual meeting (HLT-NAACL) Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing, 2006.
pdf bibCynthia Rudin and Robert E. Schapire. Margin-Based Ranking and an Equivalence Between AdaBoost and RankBoost. Journal of Machine Learning Research, Volume 10, pages 2193-2232, 2009.
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Cynthia Rudin, Corinna Cortes, Mehryar Mohri, Robert E. Schapire. Margin-Based Ranking and Boosting Meet in the Middle. Proceedings of the Eighteenth Annual Conference on Computational Learning Theory (COLT), pages 63-78, 2005.
pdf bibConvergence of Boosting Algorithms
Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire. Does AdaBoost Always Cycle? JMLR: Workshop and Conference Proceedings, Published as a COLT Open Problem.
pdf bibIndraneel Mukherjee, Cynthia Rudin, and Robert Schapire. The Rate of Convergence of AdaBoost. Proceedings of the Twenty-fourth Annual Conference on Learning Theory (COLT), 2011.
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Longer Version, accepted with minor revision to JMLR
Link pdf bibCynthia Rudin, Ingrid Daubechies, Robert E. Schapire. The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins. Journal of Machine Learning Research, 5 (Dec): 1557-1595, 2004.
pdf bibCynthia Rudin, Ingrid Daubechies, and Rob Schapire. On the Dynamics of Boosting. Advances in Neural Information Processing Systems (NIPS) 16, 2003.
pdf bibCynthia Rudin, Robert E. Schapire, Ingrid Daubechies. Analysis of Boosting Algorithms using the Smooth Margin Function. Annals of Statistics, Volume 35, Number 6, pages 2723-2768, 2007.
pdf bibCynthia Rudin, Robert E. Schapire, and Ingrid Daubechies. Precise Statements of Convergence for AdaBoost and arc-gv. In Proc. AMS-IMS-SIAM Joint Summer Research Conference: Machine Learning, Statistics, and Discovery 131-145, 2007.
pdf bibCynthia Rudin, Robert E. Schapire, and Ingrid Daubechies. Boosting Based on a Smooth Margin. Proceedings of the Seventeenth Annual Conference on Computational Learning Theory (COLT), 2004.
pdf bibOther Papers
Cynthia Rudin. Teaching "Prediction: Machine Learning and Statistics." Proceedings of the ICML Workshop on Teaching ML, 2012.
pdf bibRyan Roth, Owen Rambow, Nizar Habash, Mona Diab, and Cynthia Rudin. Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking. The 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL/HLT), 2008.
pdf bibCynthia Rudin. Stability of Learning algorithms. Computer Science ArXiV.
Cynthia Rudin and Brian Spencer. Equilibrium Island Arrays in Strained Solid Films. Journal of Applied Physics, November 15, 1999 - Volume 86, Issue 10, pages 5530-5536.
Edited Collections
Eds. Peter Qian, Yilu Zhou, and Cynthia Rudin. Proceedings of the 2011 INFORMS Data Mining and Health Informatics (DM-HI) Workshop
