Cynthia D. Rudin
Professor of Computer Science
Cynthia Rudin is a professor of computer science, electrical and computer engineering, and statistical science at Duke University, and directs the Prediction Analysis Lab, whose main focus is in interpretable machine learning. She is also an associate director of the Statistical and Applied Mathematical Sciences Institute (SAMSI). Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. She holds an undergraduate degree from the University at Buffalo, and a PhD from Princeton University. She is a three-time winner of the INFORMS Innovative Applications in Analytics 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. She is past chair of both the INFORMS Data Mining Section and the Statistical Learning and Data Science section of the American Statistical Association. She has also served on committees for DARPA, the National Institute of Justice, and AAAI. She has served on three committees for the National Academies of Sciences, Engineering and Medicine, including the Committee on Applied and Theoretical Statistics, the Committee on Law and Justice, and the Committee on Analytic Research Foundations for the Next-Generation Electric Grid. She is a fellow of the American Statistical Association and a fellow of the Institute of Mathematical Statistics. She is a Thomas Langford Lecturer at Duke University during the 2019-2020 academic year.
Current Appointments & Affiliations
- Professor of Computer Science, Computer Science, Trinity College of Arts & Sciences 2019
- Professor of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2019
- Professor of Mathematics, Mathematics, Trinity College of Arts & Sciences 2019
- Professor of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2019
Contact Information
- LSRC D342, Durham, NC 27708
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(919) 660-6555
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Rudin Lab Website
- Background
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Education, Training, & Certifications
- Ph.D., Princeton University 2004
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Duke Appointment History
- Associate Professor of Computer Science, Computer Science, Trinity College of Arts & Sciences 2016 - 2019
- Associate Professor of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2016 - 2019
- Associate Professor of Mathematics, Mathematics, 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
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In the News
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DEC 15, 2020 -
JUN 11, 2020 -
DEC 10, 2019 Pratt School of Engineering -
OCT 31, 2019 -
MAY 21, 2019 Pratt School of Engineering -
MAR 18, 2019 -
DEC 5, 2018 -
NOV 15, 2018 -
JUL 19, 2017 -
MAR 15, 2017 Pratt School of Engineering -
OCT 2, 2016
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- Research
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External Relationships
- Harmonia Holdings Group LLC
- Ten63 Therapeutics
- VerAI
- Publications & Artistic Works
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Selected Publications
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Books
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Rudin, C. Turning prediction tools into decision tools. Vol. 9356, 2015.
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Academic Articles
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Chen, Z., Y. Bei, and C. Rudin. “Concept whitening for interpretable image recognition.” Nature Machine Intelligence 2, no. 12 (December 1, 2020): 772–82. https://doi.org/10.1038/s42256-020-00265-z.Full Text
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Dong, J., and C. Rudin. “Exploring the cloud of variable importance for the set of all good models.” Nature Machine Intelligence 2, no. 12 (December 1, 2020): 810–24. https://doi.org/10.1038/s42256-020-00264-0.Full Text
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Awan, M Usaid, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky. “Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference.” Corr abs/2003.00964 (2020).
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Fisher, A., C. Rudin, and F. Dominici. “All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously.” Journal of Machine Learning Research 20 (December 1, 2019).
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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.Full Text
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Ustun, B., and C. Rudin. “Learning optimized risk scores.” Journal of Machine Learning Research 20 (June 1, 2019).
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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.
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Bravo, Fernanda, Cynthia Rudin, Yaron Shaposhnik, and Yuting Yuan. “Simple Rules for Predicting Congestion Risk in Queueing Systems: Application to ICUs,” May 7, 2019.
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Rudin, C. “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.” Nature Machine Intelligence 1, no. 5 (May 1, 2019): 206–15. https://doi.org/10.1038/s42256-019-0048-x.Full Text
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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.
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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.Full Text
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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.
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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.Full Text
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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.Full Text
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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.Full Text Link to Item
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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.
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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).
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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.Full Text
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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.
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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): 069901. https://doi.org/10.1063/1.4986799.Full Text
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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.Full Text
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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.Full Text
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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
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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): 063110. https://doi.org/10.1063/1.4953795.Full Text
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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).
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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.Full Text
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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.Full Text
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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.Full Text
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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): e0152316. https://doi.org/10.1371/journal.pone.0152316.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.Full Text
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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.
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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.
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Tulabandhula, T., and C. Rudin. “Machine learning with operational costs.” Journal of Machine Learning Research 14 (June 1, 2013): 1989–2028.
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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.Full Text
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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.Full Text
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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.Full Text
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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.
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Rudin, Cynthia, Benjamin Letham, Eugene Kogan, and David Madigan. “A Learning Theory Framework for Association Rules and Sequential Events,” June 20, 2011.
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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.Full Text
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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.
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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.Full Text
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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.
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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.
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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.Full Text
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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.
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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 (January 1, 2004): 502–17. https://doi.org/10.1007/978-3-540-27819-1_35.Full Text
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Parikh, Harsh, Cynthia Rudin, and Alexander Volfovsky. “MALTS: Matching After Learning to Stretch,” n.d.Link to Item
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Conference Papers
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Wang, T., W. Ye, D. Geng, and C. Rudin. “Towards Practical Lipschitz Bandits.” In Fods 2020 Proceedings of the 2020 Acm Ims Foundations of Data Science Conference, 129–38, 2020. https://doi.org/10.1145/3412815.3416885.Full Text
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Menon, S., A. Damian, S. Hu, N. Ravi, and C. Rudin. “PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models.” In Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition, 2434–42, 2020. https://doi.org/10.1109/CVPR42600.2020.00251.Full Text
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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. ACM, 2019. https://doi.org/10.1145/3292500.3330823.Full Text
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Chen, C., O. Li, C. Tao, A. J. Barnett, J. Su, and C. Rudin. “This looks like that: Deep learning for interpretable image recognition.” In Advances in Neural Information Processing Systems, Vol. 32, 2019.
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Hu, X., C. Rudin, and M. Seltzer. “Optimal sparse decision trees.” In Advances in Neural Information Processing Systems, Vol. 32, 2019.
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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.
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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.
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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.” In 35th Conference on Uncertainty in Artificial Intelligence, Uai 2019, 2019.
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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.Full Text
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Timofte, Radu, Shuhang Gu, Jiqing Wu, Luc Van Gool, Lei Zhang, Ming-Hsuan Yang, Muhammad Haris, et al. “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.Full Text
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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.
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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.
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Rudin, C., and Y. Wang. “Direct learning to rank and rerank.” In International Conference on Artificial Intelligence and Statistics, Aistats 2018, 775–83, 2018.
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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.Full Text
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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.Full Text
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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.Full Text
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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.
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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.
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Yang, H., C. Rudin, and M. Seltzer. “Scalable Bayesian rule lists.” In 34th International Conference on Machine Learning, Icml 2017, 8:5971–80, 2017.
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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.Full Text
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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.
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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.
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Wang, F., and C. Rudin. “Falling rule lists.” In Journal of Machine Learning Research, 38:1013–22, 2015.
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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.Full Text
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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.Full Text
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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.
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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.
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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.
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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.Full Text
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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.
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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.
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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.
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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.
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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.Full Text
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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.Full Text
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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.
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Kim, B., and C. Rudin. “Machine learning for meeting analysis.” In Aaai Workshop Technical Report, WS-13-17:59–61, 2013.
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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.
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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.
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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.
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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.
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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.
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Tulabandhula, T., and C. Rudin. “The influence of operational cost on estimation.” In International Symposium on Artificial Intelligence and Mathematics, Isaim 2012, 2012.
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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.Full Text
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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.Full Text
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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.Full Text
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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.
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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.
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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.Full Text
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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.Full Text
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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.Full Text
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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. https://doi.org/10.3115/1557690.1557721.Full Text
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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. https://doi.org/10.1007/11776420_43.Full Text
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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. https://doi.org/10.1007/11503415_5.Full Text
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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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- Teaching & Mentoring
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Recent Courses
- COMPSCI 393: Research Independent Study 2021
- COMPSCI 474: Data Science Competition 2021
- ECE 899: Special Readings in Electrical Engineering 2021
- ME 555: Advanced Topics in Mechanical Engineering 2021
- COMPSCI 290: Topics in Computer Science 2020
- COMPSCI 391: Independent Study 2020
- COMPSCI 393: Research Independent Study 2020
- COMPSCI 394: Research Independent Study 2020
- COMPSCI 671D: Machine Learning - Introductory PhD Level 2020
- COMPSCI 891: Special Readings in Computer Science 2020
- ECE 687D: Theory and Algorithms for Machine Learning 2020
- ECE 899: Special Readings in Electrical Engineering 2020
- STA 493: Research Independent Study 2020
- STA 671D: Machine Learning - Introductory PhD Level 2020
- STA 993: Independent Study 2020
- COMPSCI 391: Independent Study 2019
- COMPSCI 393: Research Independent Study 2019
- COMPSCI 394: Research Independent Study 2019
- COMPSCI 671D: Machine Learning - Introductory PhD Level 2019
- COMPSCI 891: Special Readings in Computer Science 2019
- ECE 590D: Advanced Topics in Electrical and Computer Engineering 2019
- STA 671D: Machine Learning - Introductory PhD Level 2019
- STA 993: Independent Study 2019
- Scholarly, Clinical, & Service Activities
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Outreach & Engaged Scholarship
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