Cynthia D. Rudin | Scholars@Duke

Cynthia D. Rudin
Professor of Computer Science

Cynthia Rudin is an associate professor of computer science, electrical and computer engineering, statistical science and mathematics at Duke University, and directs the Prediction Analysis Lab. Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. She holds an undergraduate degree from the University at Buffalo, and a PhD in applied and computational mathematics from Princeton University. She is the recipient of the 2013 and 2016 INFORMS Innovative Applications in Analytics Awards, an NSF CAREER award, was named as one of the "Top 40 Under 40" by Poets and Quants in 2015, and was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015. Work from her lab has won 10 best paper awards in the last 5 years. She is past chair of the INFORMS Data Mining Section, and is currently chair of the Statistical Learning and Data Science section of the American Statistical Association. She also serves on (or has served on) committees for DARPA, the National Institute of Justice, the National Academy of Sciences (for both statistics and criminology/law), and AAAI.

Current Appointments & Affiliations

Professor of Computer Science, Computer Science, Trinity College of Arts & Sciences 2019
Associate Professor of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2016
Associate Professor of Mathematics, Mathematics, Trinity College of Arts & Sciences 2016
Professor of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2019

Contact Information

LSRC D342, Durham, NC 27708
(919) 660-6555

Background
Education, Training, & Certifications
- Ph.D., Princeton University 2004
Duke Appointment History
- Associate Professor of Computer Science, Computer Science, Trinity College of Arts & Sciences 2016 - 2019
- Associate Professor of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2016 - 2019
- Scholar in Residence of Computer Science, Computer Science, Trinity College of Arts & Sciences 2016
Recognition
In the News
- DEC 10, 2019 Pratt School of Engineering
  
  To Save Lives During Seizures, Grab a Scorecard, Machine Learning Style
- OCT 31, 2019
  
  This A.I. Birdwatcher Lets You ‘See’ Through the Eyes of a Machine
- MAY 21, 2019 Pratt School of Engineering
  
  Stop Gambling with Black Box and Explainable Models on High-Stakes Decisions
- MAR 18, 2019
  
  These Works of Art Were Created by Artificial Intelligence
- DEC 5, 2018
  
  Duke Team Attempts a Real-Life Version of CSI 'Zoom and Enhance'
- NOV 15, 2018
  
  Bard or Bot?
- JUL 19, 2017
  
  Opening the Lid on Criminal Sentencing Software
- MAR 15, 2017 Pratt School of Engineering
  
  Data in, Decisions Out: Pratt's Cynthia Rudin Designs Algorithms to Turn Raw Information Into Informed Choices
- OCT 2, 2016
  
  Cynthia Rudin: Training Computers to Find Patterns That Humans Miss
Research
Industry Relationships
- Gavilan Biodesign, Inc.
- Harmonia Holdings Group LLC
- Quantum Discoveries, Inc.
Publications & Artistic Works
Selected Publications
- Books
  - Rudin, C. Turning prediction tools into decision tools. Vol. 9356, 2015.
- Academic Articles
  - Wang, Fulton, Cynthia Rudin, Tyler H. Mccormick, and John L. Gore. “Modeling recovery curves with application to prostatectomy..” Biostatistics (Oxford, England) 20, no. 4 (October 2019): 549–64. https://doi.org/10.1093/biostatistics/kxy002.
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  - Rudin, Cynthia, and Yaron Shaposhnik. “Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation,” May 28, 2019.
  - Bravo, Fernanda, Cynthia Rudin, Yaron Shaposhnik, and Yuting Yuan. “Simple Rules for Predicting Congestion Risk in Queueing Systems: Application to ICUs,” May 7, 2019.
  - Dieng, Awa, Yameng Liu, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky. “Interpretable Almost-Exact Matching for Causal Inference..” Proceedings of Machine Learning Research 89 (April 2019): 2445–53.
  - Ban, G. Y., and C. Rudin. “The big Data newsvendor: Practical insights from machine learning.” Operations Research 67, no. 1 (January 1, 2019): 90–108. https://doi.org/10.1287/opre.2018.1757.
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  - Usaid Awan, M., Y. Liu, M. Morucci, S. Roy, C. Rudin, and A. Volfovsky. “Interpretable almost-matching-exactly with instrumental variables.” 35th Conference on Uncertainty in Artificial Intelligence, Uai 2019, January 1, 2019.
  - Rudin, C., and Ş. Ertekin. “Learning customized and optimized lists of rules with mathematical programming.” Mathematical Programming Computation 10, no. 4 (December 1, 2018): 659–702. https://doi.org/10.1007/s12532-018-0143-8.
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  - Rudin, C., and B. Ustunb. “Optimized scoring systems: Toward trust in machine learning for healthcare and criminal justice.” Interfaces 48, no. 5 (September 1, 2018): 449–66. https://doi.org/10.1287/inte.2018.0957.
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  - Vu, Mai-Anh T., Tülay Adalı, Demba Ba, György Buzsáki, David Carlson, Katherine Heller, Conor Liston, et al. “A Shared Vision for Machine Learning in Neuroscience..” J Neurosci 38, no. 7 (February 14, 2018): 1601–7. https://doi.org/10.1523/JNEUROSCI.0508-17.2018.
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  - Angelino, E., N. Larus-Stone, D. Alabi, M. Seltzer, and C. Rudin. “Learning certifiably optimal rule lists for categorical data.” Journal of Machine Learning Research 18 (January 1, 2018): 1–78.
  - Dieng, Awa, Yameng Liu, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky. “Collapsing-Fast-Large-Almost-Matching-Exactly: A Matching Method for Causal Inference..” Corr abs/1806.06802 (2018).
  - Struck, Aaron F., Berk Ustun, Andres Rodriguez Ruiz, Jong Woo Lee, Suzette M. LaRoche, Lawrence J. Hirsch, Emily J. Gilmore, et al. “Association of an Electroencephalography-Based Risk Score With Seizure Probability in Hospitalized Patients..” Jama Neurology 74, no. 12 (December 2017): 1419–24. https://doi.org/10.1001/jamaneurol.2017.2459.
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  - Wang, T., C. Rudin, F. Doshi-Velez, Y. Liu, E. Klampfl, and P. MacNeille. “A Bayesian framework for learning rule sets for interpretable classification.” Journal of Machine Learning Research 18 (August 1, 2017): 1–37.
  - Letham, Benjamin, Portia A. Letham, Cynthia Rudin, and Edward P. Browne. “Erratum: "Prediction uncertainty and optimal experimental design for learning dynamical systems" [Chaos 26, 063110 (2016)]..” Chaos (Woodbury, N.Y.) 27, no. 6 (June 2017). https://doi.org/10.1063/1.4986799.
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  - Zeng, J., B. Ustun, and C. Rudin. “Interpretable classification models for recidivism prediction.” Journal of the Royal Statistical Society. Series A: Statistics in Society 180, no. 3 (June 1, 2017): 689–722. https://doi.org/10.1111/rssa.12227.
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  - Ustun, Berk, Lenard A. Adler, Cynthia Rudin, Stephen V. Faraone, Thomas J. Spencer, Patricia Berglund, Michael J. Gruber, and Ronald C. Kessler. “The World Health Organization Adult Attention-Deficit/Hyperactivity Disorder Self-Report Screening Scale for DSM-5..” Jama Psychiatry 74, no. 5 (May 2017): 520–27. https://doi.org/10.1001/jamapsychiatry.2017.0298.
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  - Roy, Sudeepa, Cynthia Rudin, Alexander Volfovsky, and Tianyu Wang. “FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference..” Corr abs/1707.06315 (2017).
    Open Access Copy
  - Letham, Benjamin, Portia A. Letham, Cynthia Rudin, and Edward P. Browne. “Prediction uncertainty and optimal experimental design for learning dynamical systems..” Chaos (Woodbury, N.Y.) 26, no. 6 (June 2016). https://doi.org/10.1063/1.4953795.
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  - Moghaddass, R., C. Rudin, and D. Madigan. “The factorized self-controlled case series method: An approach for estimating the effects of many drugs on many outcomes.” Journal of Machine Learning Research 17 (June 1, 2016).
  - Souillard-Mandar, W., R. Davis, C. Rudin, R. Au, D. J. Libon, R. Swenson, C. C. Price, M. Lamar, and D. L. Penney. “Learning classification models of cognitive conditions from subtle behaviors in the digital Clock Drawing Test.” Machine Learning 102, no. 3 (March 1, 2016): 393–441. https://doi.org/10.1007/s10994-015-5529-5.
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  - Ustun, B., and C. Rudin. “Supersparse linear integer models for optimized medical scoring systems.” Machine Learning 102, no. 3 (March 1, 2016): 349–91. https://doi.org/10.1007/s10994-015-5528-6.
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  - Ustun, Berk, M Brandon Westover, Cynthia Rudin, and Matt T. Bianchi. “Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms..” Journal of Clinical Sleep Medicine : Jcsm : Official Publication of the American Academy of Sleep Medicine 12, no. 2 (February 2016): 161–68. https://doi.org/10.5664/jcsm.5476.
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  - Browne, Edward P., Benjamin Letham, and Cynthia Rudin. “A Computational Model of Inhibition of HIV-1 by Interferon-Alpha..” Plos One 11, no. 3 (January 2016). https://doi.org/10.1371/journal.pone.0152316.
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  - Ertekin, Ş., and C. Rudin. “A bayesian approach to learning scoring systems.” Big Data 3, no. 4 (December 1, 2015): 267–76. https://doi.org/10.1089/big.2015.0033.
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  - Moghaddass, R., and C. Rudin. “The latent state hazard model, with application to wind turbine reliability.” Annals of Applied Statistics 9, no. 4 (December 1, 2015): 1823–63. https://doi.org/10.1214/15-AOAS859.
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  - Tulabandhula, T., and C. Rudin. “Generalization bounds for learning with linear, polygonal, quadratic and conic side knowledge.” Machine Learning 100, no. 2–3 (September 17, 2015): 183–216. https://doi.org/10.1007/s10994-014-5478-4.
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  - Wang, T., C. Rudin, D. Wagner, and R. Sevieri. “Finding Patterns with a Rotten Core: Data Mining for Crime Series with Cores.” Big Data 3, no. 1 (March 1, 2015): 3–21. https://doi.org/10.1089/big.2014.0021.
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  - Ertekin, Ş., C. Rudin, and T. H. McCormick. “Reactive point processes: A new approach to predicting power failures in underground electrical systems.” Annals of Applied Statistics 9, no. 1 (January 1, 2015): 122–44. https://doi.org/10.1214/14-AOAS789.
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  - Letham, B., C. Rudin, T. H. McCormick, and D. Madigan. “Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model.” Annals of Applied Statistics 9, no. 3 (January 1, 2015): 1350–71. https://doi.org/10.1214/15-AOAS848.
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  - Tulabandhula, T., and C. Rudin. “Tire changes, fresh air, and yellow flags: Challenges in predictive analytics for professional racing.” Big Data 2, no. 2 (June 1, 2014): 97–112. https://doi.org/10.1089/big.2014.0018.
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  - Ertekin, S., C. Rudin, and H. Hirsh. “Approximating the crowd.” Data Mining and Knowledge Discovery 28, no. 5–6 (January 1, 2014): 1189–1221. https://doi.org/10.1007/s10618-014-0354-1.
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  - Kim, B., and C. Rudin. “Learning about meetings.” Data Mining and Knowledge Discovery 28, no. 5–6 (January 1, 2014): 1134–57. https://doi.org/10.1007/s10618-014-0348-z.
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  - Rudin, C., S. Ertekin, R. Passonneau, A. Radeva, A. Tomar, B. Xie, S. Lewis, M. Riddle, D. Pangsrivinij, and T. McCormick. “Analytics for power grid distribution reliability in New York City.” Interfaces 44, no. 4 (January 1, 2014): 364–82. https://doi.org/10.1287/inte.2014.0748.
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  - Rudin, C., and K. L. Wagstaff. “Machine learning for science and society.” Machine Learning 95, no. 1 (January 1, 2014): 1–9. https://doi.org/10.1007/s10994-013-5425-9.
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  - Letham, B., C. Rudin, and K. A. Heller. “Growing a list.” Data Mining and Knowledge Discovery 27, no. 3 (December 1, 2013): 372–95. https://doi.org/10.1007/s10618-013-0329-7.
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  - Letham, B., C. Rudin, and D. Madigan. “Sequential event prediction.” Machine Learning 93, no. 2–3 (November 1, 2013): 357–80. https://doi.org/10.1007/s10994-013-5356-5.
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  - Rudin, C., B. Letham, and D. Madigan. “Learning theory analysis for association rules and sequential event prediction.” Journal of Machine Learning Research 14 (November 1, 2013): 3441–92.
  - Mukherjee, I., C. Rudin, and R. E. Schapire. “The rate of convergence of AdaBoost.” Journal of Machine Learning Research 14 (August 1, 2013): 2315–47.
  - Tulabandhula, T., and C. Rudin. “Machine learning with operational costs.” Journal of Machine Learning Research 14 (June 1, 2013): 1989–2028.
  - Chang, A., C. Rudin, M. Cavaretta, M. Robert Thomas, and M. Gloria Chou. “How to reverse-engineer quality rankings.” Machine Learning 88, no. 3 (September 1, 2012): 369–98. https://doi.org/10.1007/s10994-012-5295-6.
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  - McCormick, T. H., C. Rudin, and D. Madigan. “Bayesian hierarchical rule modeling for predicting medical conditions.” Annals of Applied Statistics 6, no. 2 (June 1, 2012): 652–68. https://doi.org/10.1214/11-AOAS522.
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  - Rudin, C., D. Waltz, R. Anderson, A. Boulanger, A. Salleb-Aouissi, M. Chow, H. Dutta, et al. “Machine learning for the New York City power grid.” Ieee Transactions on Pattern Analysis and Machine Intelligence 34, no. 2 (January 1, 2012): 328–45. https://doi.org/10.1109/TPAMI.2011.108.
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  - Ertekin, S., and C. Rudin. “On equivalence relationships between classification and ranking algorithms.” Journal of Machine Learning Research 12 (October 1, 2011): 2905–29.
  - Rudin, Cynthia, Benjamin Letham, Eugene Kogan, and David Madigan. “A Learning Theory Framework for Association Rules and Sequential Events,” June 20, 2011.
  - Rudin, C., R. J. Passonneau, A. Radeva, S. Ierome, and D. F. Isaac. “21st-century data miners meet 19th-century electrical cables.” Computer 44, no. 6 (June 1, 2011): 103–5. https://doi.org/10.1109/MC.2011.164.
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  - McCormick, Tyler, Cynthia Rudin, and David Madigan. “A Hierarchical Model for Association Rule Mining of Sequential Events: An Approach to Automated Medical Symptom Prediction,” January 6, 2011.
  - Rudin, C., R. J. Passonneau, A. Radeva, H. Dutta, S. Ierome, and D. Isaac. “A process for predicting manhole events in Manhattan.” Machine Learning 80, no. 1 (July 1, 2010): 1–31. https://doi.org/10.1007/s10994-009-5166-y.
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  - Rudin, C. “The P-norm push: A simple convex ranking algorithm that concentrates at the top of the list.” Journal of Machine Learning Research 10 (November 30, 2009): 2233–71.
  - Rudin, C., and R. E. Schapire. “Margin-based ranking and an equivalence between AdaBoost and RankBoost.” Journal of Machine Learning Research 10 (November 30, 2009): 2193–2232.
  - Rudin, C., R. E. Schapire, and I. Daubechies. “Analysis of boosting algorithms using the smooth margin function.” Annals of Statistics 35, no. 6 (December 1, 2007): 2723–68. https://doi.org/10.1214/009053607000000785.
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  - Rudin, C., R. E. Schapire, and I. Daubechies. “Boosting based on a smooth margin.” Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) 3120 (December 9, 2004): 502–17.
  - Rudin, C., I. Daubechies, and R. E. Schapire. “The dynamics of AdaBoost: Cyclic behavior and convergence of margins.” Journal of Machine Learning Research 5 (December 1, 2004): 1557–95.
  - Parikh, Harsh, Cynthia Rudin, and Alexander Volfovsky. “MALTS: Matching After Learning to Stretch,” n.d.
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- Conference Papers
  - Rudin, Cynthia. “Do Simpler Models Exist and How Can We Find Them?.” In Proceedings of the 25th Acm Sigkdd International Conference on Knowledge Discovery & Data Mining Kdd ’19. ACM Press, 2019. https://doi.org/10.1145/3292500.3330823.
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  - Tracà, S., C. Rudin, and W. Yan. “Reducing exploration of dying arms in mortal bandits.” In 35th Conference on Uncertainty in Artificial Intelligence, Uai 2019, 2019.
  - Bei, Y., A. Damian, S. Hu, S. Menon, N. Ravi, and C. Rudin. “New techniques for preserving global structure and denoising with low information loss in single-image super-resolution.” In Ieee Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2018-June:987–94, 2018. https://doi.org/10.1109/CVPRW.2018.00132.
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  - Timofte, Radu, Shuhang Gu, Luc Van Gool, Lei Zhang, and Ming-Hsuan Yang. “NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results.” In 2018 Ieee/Cvf Conference on Computer Vision and Pattern Recognition Workshops (Cvprw). IEEE, 2018. https://doi.org/10.1109/cvprw.2018.00130.
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  - Chen, C., and C. Rudin. “An optimization approach to learning falling rule lists.” In International Conference on Artificial Intelligence and Statistics, Aistats 2018, 604–12, 2018.
  - Li, O., H. Liu, C. Chen, and C. Rudin. “Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions.” In 32nd Aaai Conference on Artificial Intelligence, Aaai 2018, 3530–37, 2018.
  - Rudin, C., and Y. Wang. “Direct learning to rank and rerank.” In International Conference on Artificial Intelligence and Statistics, Aistats 2018, 775–83, 2018.
  - Angelino, E., N. Larus-Stone, D. Alabi, M. Seltzer, and C. Rudin. “Learning certifiably optimal rule lists.” In Proceedings of the Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, Part F129685:35–44, 2017. https://doi.org/10.1145/3097983.3098047.
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  - Ustun, B., and C. Rudin. “Optimized risk scores.” In Proceedings of the Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, Part F129685:1125–34, 2017. https://doi.org/10.1145/3097983.3098161.
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  - Wang, T., C. Rudin, F. Velez-Doshi, Y. Liu, E. Klampfl, and P. Macneille. “Bayesian rule sets for interpretable classification.” In Proceedings Ieee International Conference on Data Mining, Icdm, 1269–74, 2017. https://doi.org/10.1109/ICDM.2016.130.
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  - Lakkaraju, H., and C. Rudin. “Learning cost-effective and interpretable treatment regimes.” In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Aistats 2017, 2017.
  - Yang, H., C. Rudin, and M. Seltzer. “Scalable Bayesian rule lists.” In 34th International Conference on Machine Learning, Icml 2017, 8:5971–80, 2017.
  - Letham, B., L. M. Letham, and C. Rudin. “Bayesian inference of arrival rate and substitution behavior from sales transaction data with stockouts.” In Proceedings of the Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 13-17-August-2016:1695–1704, 2016. https://doi.org/10.1145/2939672.2939810.
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  - Garg, V. K., C. Rudin, and T. Jaakkola. “CRAFT: ClusteR-specific Assorted Feature selecTion.” In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, Aistats 2016, 305–13, 2016.
  - Rudin, C. “Turning prediction tools into decision tools.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9355, 2015.
  - Wang, F., and C. Rudin. “Falling rule lists.” In Journal of Machine Learning Research, 38:1013–22, 2015.
  - Goh, S. T., and C. Rudin. “Box drawings for learning with imbalanced data.” In Proceedings of the Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 333–42, 2014. https://doi.org/10.1145/2623330.2623648.
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  - Huggins, J. H., and C. Rudin. “A statistical learning theory framework for supervised pattern discovery.” In Siam International Conference on Data Mining 2014, Sdm 2014, 1:506–14, 2014. https://doi.org/10.1137/1.9781611973440.58.
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  - Huggins, J., and C. Rudin. “Toward a theory of pattern discovery.” In International Symposium on Artificial Intelligence and Mathematics, Isaim 2014, 2014.
  - Kim, B., C. Rudin, and J. Shah. “The Bayesian case model: A generative approach for case-based reasoning and prototype classification.” In Advances in Neural Information Processing Systems, 3:1952–60, 2014.
  - Tulabandhula, T., and C. Rudin. “On combining machine learning with decision making.” In Machine Learning, 97:33–64, 2014. https://doi.org/10.1007/s10994-014-5459-7.
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  - Tulabandhula, T., and C. Rudin. “Robust optimization using machine learning for uncertainty sets.” In International Symposium on Artificial Intelligence and Mathematics, Isaim 2014, 2014.
  - Tulabandhula, T., and C. Rudin. “Generalization bounds for learning with linear and quadratic side knowledge.” In International Symposium on Artificial Intelligence and Mathematics, Isaim 2014, 2014.
  - Wang, D., R. J. Passonneau, M. Collins, and C. Rudin. “Modeling weather impact on a secondary electrical grid.” In Procedia Computer Science, 32:631–38, 2014. https://doi.org/10.1016/j.procs.2014.05.470.
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  - Wang, T., C. Rudin, D. Wagner, and R. Sevieri. “Learning to detect patterns of crime.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8190 LNAI:515–30, 2013. https://doi.org/10.1007/978-3-642-40994-3_33.
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  - Ertekin, S., C. Rudin, and T. H. McCormick. “Predicting power failures with reactive point processes.” In Aaai Workshop Technical Report, WS-13-17:23–25, 2013.
  - Kim, B., and C. Rudin. “Machine learning for meeting analysis.” In Aaai Workshop Technical Report, WS-13-17:59–61, 2013.
  - Letham, B., C. Rudin, T. H. McCormick, and D. Madigan. “An interpretable stroke prediction model using rules and Bayesian analysis.” In Aaai Workshop Technical Report, WS-13-17:65–67, 2013.
  - Ustun, B., S. Tracà, and C. Rudin. “Supersparse linear integer models for predictive scoring systems.” In Aaai Workshop Technical Report, WS-13-17:128–30, 2013.
  - Wang, T., C. Rudin, D. Wagner, and R. Sevieri. “Detecting patterns of crime with Series Finder.” In Aaai Workshop Technical Report, WS-13-17:140–42, 2013.
  - Bertsimas, D., A. Chang, and C. Rudin. “An integer optimization approach to associative classification.” In Advances in Neural Information Processing Systems, 4:3302–10, 2012.
  - Ertekin, S., H. Hirsh, and C. Rudin. “Selective sampling of labelers for approximating the crowd.” In Aaai Fall Symposium Technical Report, FS-12-06:7–13, 2012.
  - Tulabandhula, T., and C. Rudin. “The influence of operational cost on estimation.” In International Symposium on Artificial Intelligence and Mathematics, Isaim 2012, 2012.
  - Tulabandhula, T., C. Rudin, and P. Jaillet. “The machine learning and traveling repairman problem.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6992 LNAI:262–76, 2011. https://doi.org/10.1007/978-3-642-24873-3_20.
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  - Wu, L., G. Kaiser, C. Rudin, and R. Anderson. “Data quality assurance and performance measurement of data mining for preventive maintenance of power grid.” In Proceedings of the 1st International Workshop on Data Mining for Service and Maintenance, Kdd4service 2011 Held in Conjunction With Sigkdd’11, 28–32, 2011. https://doi.org/10.1145/2018673.2018679.
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  - Wu, L., T. Teravainen, G. Kaiser, R. Anderson, A. Boulanger, and C. Rudin. “Estimation of system reliability using a semiparametric model.” In Ieee 2011 Energytech, Energytech 2011, 2011. https://doi.org/10.1109/EnergyTech.2011.5948537.
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  - Mukherjee, I., C. Rudin, and R. E. Schapire. “The rate of convergence of AdaBoost.” In Journal of Machine Learning Research, 19:537–57, 2011.
  - Rudin, C., B. Letham, A. Salleb-Aouissi, E. Kogan, and D. Madigan. “Sequential event prediction with association rules.” In Journal of Machine Learning Research, 19:615–34, 2011.
  - Pelossof, R., M. Jones, I. Vovsha, and C. Rudin. “Online coordinate boosting.” In 2009 Ieee 12th International Conference on Computer Vision Workshops, Iccv Workshops 2009, 1354–61, 2009. https://doi.org/10.1109/ICCVW.2009.5457454.
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  - Radeva, A., C. Rudin, R. Passonneau, and D. Isaac. “Report cards for manholes: Eliciting expert feedback for a learning task.” In 8th International Conference on Machine Learning and Applications, Icmla 2009, 719–24, 2009. https://doi.org/10.1109/ICMLA.2009.72.
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  - Passonneau, R. J., C. Rudin, A. Radeva, and Z. A. Liu. “Reducing noise in labels and features for a real world dataset: Application of NLP corpus annotation methods.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5449 LNCS:86–97, 2009. https://doi.org/10.1007/978-3-642-00382-0_7.
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  - Roth, R., O. Rambow, N. Habash, M. Diab, and C. Rudin. “Arabic morphological tagging, diacritization, and lemmatization using lexeme models and feature ranking.” In Acl 08: Hlt 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, 117–20, 2008.
  - Rudin, C. “Ranking with a P-norm push.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4005 LNAI:589–604, 2006.
  - Rudin, C., C. Cortes, M. Mohri, and R. E. Schapire. “Margin-based ranking meets boosting in the middle.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3559 LNAI:63–78, 2005.
  - Rudin, C., I. Daubechies, and R. E. Schapire. “On the dynamics of boosting.” In Advances in Neural Information Processing Systems, 2004.
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