Math @ Duke
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Papers Published
- Vu, M-AT; Adalı, T; Ba, D; Buzsáki, G; Carlson, D; Heller, K; Liston, C; Rudin, C; Sohal, VS; Widge, AS; Mayberg, HS; Sapiro, G; Dzirasa, K, A Shared Vision for Machine Learning in Neuroscience.,
The Journal of neuroscience : the official journal of the Society for Neuroscience, vol. 38 no. 7
(February, 2018),
pp. 1601-1607 [doi] [abs]
- Struck, AF; Ustun, B; Ruiz, AR; Lee, JW; LaRoche, SM; Hirsch, LJ; Gilmore, EJ; Vlachy, J; Haider, HA; Rudin, C; Westover, MB, Association of an Electroencephalography-Based Risk Score With Seizure Probability in Hospitalized Patients.,
JAMA Neurology, vol. 74 no. 12
(December, 2017),
pp. 1419-1424 [doi] [abs]
- Ustun, B; Rudin, C, Optimized risk scores,
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. Part F129685
(August, 2017),
pp. 1125-1134, ISBN 9781450348874 [doi] [abs]
- Angelino, E; Larus-Stone, N; Alabi, D; Seltzer, M; Rudin, C, Learning certifiably optimal rule lists,
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. Part F129685
(August, 2017),
pp. 35-44, ISBN 9781450348874 [doi] [abs]
- Wang, T; Rudin, C; Doshi-Velez, F; Liu, Y; Klampfl, E; MacNeille, P, A Bayesian framework for learning rule sets for interpretable classification,
Journal of machine learning research : JMLR, vol. 18
(August, 2017),
pp. 1-37 [abs]
- Letham, B; Letham, PA; Rudin, C; Browne, EP, Erratum: "Prediction uncertainty and optimal experimental design for learning dynamical systems" [Chaos 26, 063110 (2016)].,
Chaos, vol. 27 no. 6
(June, 2017),
pp. 069901 [doi]
- Zeng, J; Ustun, B; Rudin, C, Interpretable classification models for recidivism prediction,
Journal of the Royal Statistical Society: Series A (Statistics in Society), vol. 180 no. 3
(June, 2017),
pp. 689-722 [doi]
- Ustun, B; Adler, LA; Rudin, C; Faraone, SV; Spencer, TJ; Berglund, P; Gruber, MJ; Kessler, RC, The World Health Organization Adult Attention-Deficit/Hyperactivity Disorder Self-Report Screening Scale for DSM-5.,
JAMA Psychiatry, vol. 74 no. 5
(May, 2017),
pp. 520-526 [doi] [abs]
- Wang, T; Rudin, C; Velez-Doshi, F; Liu, Y; Klampfl, E; Macneille, P, Bayesian rule sets for interpretable classification,
Proceedings / IEEE International Conference on Data Mining. IEEE International Conference on Data Mining
(January, 2017),
pp. 1269-1274, ISBN 9781509054725 [doi] [abs]
- Letham, B; Letham, LM; Rudin, C, Bayesian inference of arrival rate and substitution behavior from sales transaction data with stockouts,
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 13-17-August-2016
(August, 2016),
pp. 1695-1704, ISBN 9781450342322 [doi] [abs]
- Moghaddass, R; Rudin, C; Madigan, D, The factorized self-controlled case series method: An approach for estimating the effects of many drugs on many outcomes,
Journal of machine learning research : JMLR, vol. 17
(June, 2016) [abs]
- Letham, B; Letham, PA; Rudin, C; Browne, EP, Prediction uncertainty and optimal experimental design for learning dynamical systems.,
Chaos, vol. 26 no. 6
(June, 2016),
pp. 063110 [doi] [abs]
- Souillard-Mandar, W; Davis, R; Rudin, C; Au, R; Libon, DJ; Swenson, R; Price, CC; Lamar, M; Penney, DL, Learning classification models of cognitive conditions from subtle behaviors in the digital Clock Drawing Test,
Machine Learning, vol. 102 no. 3
(March, 2016),
pp. 393-441 [doi]
- Ustun, B; Rudin, C, Supersparse linear integer models for optimized medical scoring systems,
Machine Learning, vol. 102 no. 3
(March, 2016),
pp. 349-391 [doi]
- Ustun, B; Westover, MB; Rudin, C; Bianchi, MT, 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, vol. 12 no. 2
(February, 2016),
pp. 161-168 [doi] [abs]
- Browne, EP; Letham, B; Rudin, C, A Computational Model of Inhibition of HIV-1 by Interferon-Alpha.,
PloS one, vol. 11 no. 3
(January, 2016),
pp. e0152316 [doi] [abs]
- Ertekin, Ş; Rudin, C, A Bayesian Approach to Learning Scoring Systems,
Big Data, vol. 3 no. 4
(December, 2015),
pp. 267-276 [doi]
- Moghaddass, R; Rudin, C, The latent state hazard model, with application to wind turbine reliability,
The annals of applied statistics, vol. 9 no. 4
(December, 2015),
pp. 1823-1863 [doi]
- Tulabandhula, T; Rudin, C, Generalization bounds for learning with linear, polygonal, quadratic and conic side knowledge,
Machine Learning, vol. 100 no. 2-3
(September, 2015),
pp. 183-216 [doi]
- Letham, B; Rudin, C; McCormick, TH; Madigan, D, Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model,
The annals of applied statistics, vol. 9 no. 3
(September, 2015),
pp. 1350-1371 [doi]
- Wang, T; Rudin, C; Wagner, D; Sevieri, R, Finding Patterns with a Rotten Core: Data Mining for Crime Series with Cores,
Big Data, vol. 3 no. 1
(March, 2015),
pp. 3-21 [doi]
- Ertekin, Ş; Rudin, C; McCormick, TH, Reactive point processes: A new approach to predicting power failures in underground electrical systems,
The annals of applied statistics, vol. 9 no. 1
(March, 2015),
pp. 122-144 [doi]
- Wang, F; Rudin, C, Falling rule lists,
Journal of machine learning research : JMLR, vol. 38
(January, 2015),
pp. 1013-1022 [abs]
- Rudin, C, Turning prediction tools into decision tools,
Lecture notes in computer science, vol. 9355
(January, 2015), ISBN 9783319244853 [abs]
- Tulabandhula, T; Rudin, C, On combining machine learning with decision making,
Machine Learning, vol. 97 no. 1-2
(October, 2014),
pp. 33-64 [doi]
- Ertekin, Ş; Rudin, C; Hirsh, H, Approximating the crowd,
Data Mining and Knowledge Discovery, vol. 28 no. 5-6
(September, 2014),
pp. 1189-1221 [doi]
- Kim, B; Rudin, C, Learning about meetings,
Data Mining and Knowledge Discovery, vol. 28 no. 5-6
(September, 2014),
pp. 1134-1157 [doi]
- Rudin, C; Ertekin, Ş; Passonneau, R; Radeva, A; Tomar, A; Xie, B; Lewis, S; Riddle, M; Pangsrivinij, D; McCormick, T, Analytics for Power Grid Distribution Reliability in New York City,
Interfaces, vol. 44 no. 4
(August, 2014),
pp. 364-383 [doi]
- Tulabandhula, T; Rudin, C, Tire Changes, Fresh Air, and Yellow Flags: Challenges in Predictive Analytics for Professional Racing,
Big Data, vol. 2 no. 2
(June, 2014),
pp. 97-112 [doi]
- Rudin, C; Wagstaff, KL, Machine learning for science and society,
Machine Learning, vol. 95 no. 1
(April, 2014),
pp. 1-9 [doi]
- Ban, GY; Rudin, C, The Big Data Newsvendor: Practical Insights from Machine Learning
(February, 2014)
- Huggins, JH; Rudin, C, A statistical learning theory framework for supervised pattern discovery,
SIAM International Conference on Data Mining 2014, SDM 2014, vol. 1
(January, 2014),
pp. 506-514, ISBN 9781510811515 [doi] [abs]
- Kim, B; Rudin, C; Shah, J, The Bayesian case model: A generative approach for case-based reasoning and prototype classification,
Advances in Neural Information Processing Systems, vol. 3 no. January
(January, 2014),
pp. 1952-1960 [abs]
- Goh, ST; Rudin, C, Box drawings for learning with imbalanced data,
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
(January, 2014),
pp. 333-342, ISBN 9781450329569 [doi] [abs]
- Wang, D; Passonneau, RJ; Collins, M; Rudin, C, Modeling Weather Impact on a Secondary Electrical Grid,
Procedia Computer Science, vol. 32
(2014),
pp. 631-638 [doi]
- Letham, B; Rudin, C; Heller, KA, Growing a list,
Data Mining and Knowledge Discovery, vol. 27 no. 3
(December, 2013),
pp. 372-395 [doi] [abs]
- Rudin, C; Letham, B; Madigan, D, Learning theory analysis for association rules and sequential event prediction,
Journal of machine learning research : JMLR, vol. 14
(November, 2013),
pp. 3441-3492 [abs]
- Letham, B; Rudin, C; Madigan, D, Sequential event prediction,
Machine Learning, vol. 93 no. 2-3
(November, 2013),
pp. 357-380 [doi]
- Letham, B; Rudin, C; Heller, KA, Growing a list,
Data Mining and Knowledge Discovery, vol. 27 no. 3
(November, 2013),
pp. 372-395 [doi] [abs]
- Wang, T; Rudin, C; Wagner, D; Sevieri, R, Learning to detect patterns of crime,
Lecture notes in computer science, vol. 8190 LNAI no. PART 3
(October, 2013),
pp. 515-530, ISBN 9783642409936 [doi] [abs]
- Mukherjee, I; Rudin, C; Schapire, RE, The rate of convergence of AdaBoost,
Journal of machine learning research : JMLR, vol. 14
(August, 2013),
pp. 2315-2347 [abs]
- Tulabandhula, T; Rudin, C, Machine learning with operational costs,
Journal of machine learning research : JMLR, vol. 14
(June, 2013),
pp. 1989-2028 [abs]
- Ertekin, S; Rudin, C; McCormick, TH, Predicting power failures with reactive point processes,
AAAI Workshop - Technical Report, vol. WS-13-17
(January, 2013),
pp. 23-25, ISBN 9781577356288
- Kim, B; Rudin, C, Machine learning for meeting analysis,
AAAI Workshop - Technical Report, vol. WS-13-17
(January, 2013),
pp. 59-61, ISBN 9781577356288 [abs]
- Letham, B; Rudin, C; McCormick, TH; Madigan, D, An interpretable stroke prediction model using rules and Bayesian analysis,
AAAI Workshop - Technical Report, vol. WS-13-17
(January, 2013),
pp. 65-67, ISBN 9781577356288 [abs]
- Wang, T; Rudin, C; Wagner, D; Sevieri, R, Detecting patterns of crime with Series Finder,
AAAI Workshop - Technical Report, vol. WS-13-17
(January, 2013),
pp. 140-142, ISBN 9781577356288 [abs]
- Ustun, B; Tracà, S; Rudin, C, Supersparse linear integer models for predictive scoring systems,
AAAI Workshop - Technical Report, vol. WS-13-17
(January, 2013),
pp. 128-130, ISBN 9781577356288
- Tulabandhula, T; Rudin, C, The influence of operational cost on estimation,
International Symposium on Artificial Intelligence and Mathematics, ISAIM 2012
(December, 2012) [abs]
- Bertsimas, D; Chang, A; Rudin, C, An integer optimization approach to associative classification,
Advances in Neural Information Processing Systems, vol. 4
(December, 2012),
pp. 3302-3310, ISBN 9781627480031 [abs]
- Ertekin, S; Hirsh, H; Rudin, C, Selective sampling of labelers for approximating the crowd,
AAAI Fall Symposium - Technical Report, vol. FS-12-06
(December, 2012),
pp. 7-13 [abs]
- Chang, A; Rudin, C; Cavaretta, M; Thomas, R; Chou, G, How to reverse-engineer quality rankings,
Machine Learning, vol. 88 no. 3
(September, 2012),
pp. 369-398 [doi]
- McCormick, TH; Rudin, C; Madigan, D, Bayesian hierarchical rule modeling for predicting medical conditions,
The annals of applied statistics, vol. 6 no. 2
(June, 2012),
pp. 652-668 [doi]
- Rudin, C; Waltz, D; Anderson, RN; Boulanger, A; Salleb-Aouissi, A; Chow, M; Dutta, H; Gross, PN; Huang, B; Ierome, S; Isaac, DF; Kressner, A; Passonneau, RJ; Radeva, A; Wu, L, Machine Learning for the New York City Power Grid,
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34 no. 2
(February, 2012),
pp. 328-345 [doi]
- Tulabandhula, T; Rudin, C; Jaillet, P, The machine learning and traveling repairman problem,
Lecture notes in computer science, vol. 6992 LNAI
(October, 2011),
pp. 262-276, ISBN 9783642248726 [doi] [abs]
- Ertekin, S; Rudin, C, On equivalence relationships between classification and ranking algorithms,
Journal of machine learning research : JMLR, vol. 12
(October, 2011),
pp. 2905-2929 [abs]
- Wu, L; Kaiser, G; Rudin, C; Anderson, R, Data quality assurance and performance measurement of data mining for preventive maintenance of power grid,
Proceedings of the 1st International Workshop on Data Mining for Service and Maintenance, KDD4Service 2011 - Held in Conjunction with SIGKDD'11
(September, 2011),
pp. 28-32, ISBN 9781450308427 [doi] [abs]
- Wu, L; Teravainen, T; Kaiser, G; Anderson, R; Boulanger, A; Rudin, C, Estimation of system reliability using a semiparametric model,
IEEE 2011 EnergyTech, ENERGYTECH 2011
(August, 2011), ISBN 9781457707773 [doi] [abs]
- Rudin, C; Letham, B; Kogan, E; Madigan, D, A Learning Theory Framework for Association Rules and Sequential Events
(June, 2011)
- Rudin, C; Passonneau, RJ; Radeva, A; Ierome, S; Isaac, DF, 21st-Century Data Miners Meet 19th-Century Electrical Cables,
Computer, vol. 44 no. 6
(June, 2011),
pp. 103-105 [doi]
- McCormick, T; Rudin, C; Madigan, D, A Hierarchical Model for Association Rule Mining of Sequential Events: An Approach to Automated Medical Symptom Prediction
(January, 2011)
- Rudin, C; Letham, B; Salleb-Aouissi, A; Kogan, E; Madigan, D, Sequential event prediction with association rules,
Journal of machine learning research : JMLR, vol. 19
(January, 2011),
pp. 615-634 [abs]
- Mukherjee, I; Rudin, C; Schapire, RE, The rate of convergence of AdaBoost,
Journal of machine learning research : JMLR, vol. 19
(January, 2011),
pp. 537-557 [abs]
- Rudin, C; Passonneau, RJ; Radeva, A; Dutta, H; Ierome, S; Isaac, D, A process for predicting manhole events in Manhattan,
Machine Learning, vol. 80 no. 1
(July, 2010),
pp. 1-31 [doi]
- Pelossof, R; Jones, M; Vovsha, I; Rudin, C, Online coordinate boosting,
2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
(December, 2009),
pp. 1354-1361, ISBN 9781424444427 [doi] [abs]
- Radeva, A; Rudin, C; Passonneau, R; Isaac, D, Report cards for manholes: Eliciting expert feedback for a learning task,
8th International Conference on Machine Learning and Applications, ICMLA 2009
(December, 2009),
pp. 719-724, ISBN 9780769539263 [doi] [abs]
- Rudin, C; Schapire, RE, Margin-based ranking and an equivalence between AdaBoost and RankBoost,
Journal of machine learning research : JMLR, vol. 10
(November, 2009),
pp. 2193-2232 [abs]
- Rudin, C, The P-norm push: A simple convex ranking algorithm that concentrates at the top of the list,
Journal of machine learning research : JMLR, vol. 10
(November, 2009),
pp. 2233-2271 [abs]
- Passonneau, RJ; Rudin, C; Radeva, A; Liu, ZA, Reducing noise in labels and features for a real world dataset: Application of NLP corpus annotation methods,
Lecture notes in computer science, vol. 5449 LNCS
(July, 2009),
pp. 86-97, ISBN 3642003818 [doi] [abs]
- Roth, R; Rambow, O; Habash, N; Diab, M; Rudin, C, Arabic morphological tagging, diacritization, and lemmatization using lexeme models and feature ranking,
ACL-08: HLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference
(December, 2008),
pp. 117-120, ISBN 9781932432046 [abs]
- Rudin, C; Schapire, RE; Daubechies, I, Analysis of boosting algorithms using the smooth margin function,
Annals of statistics, vol. 35 no. 6
(2007),
pp. 2723-2768 [doi] [abs]
- Rudin, C, Ranking with a P-norm push,
Lecture notes in computer science, vol. 4005 LNAI
(January, 2006),
pp. 589-604, ISBN 3540352945 [abs]
- Rudin, C; Cortes, C; Mohri, M; Schapire, RE, Margin-based ranking meets boosting in the middle,
Lecture notes in computer science, vol. 3559 LNAI
(December, 2005),
pp. 63-78, ISBN 3540265562 [abs]
- Rudin, C; Schapire, RE; Daubechies, I, Boosting based on a smooth margin,
Lecture notes in computer science, vol. 3120
(2004),
pp. 502-517 [abs]
- Rudin, C; Daubechies, I; Schapire, RE, The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins.,
Journal of Machine Learning Research, vol. 5
(2004),
pp. 1557-1595 [abs]
- Rudin, C; Daubechies, I; Schapire, RE, On the Dynamics of Boosting., edited by Thrun, S; Saul, LK; Schölkopf, B,
NIPS
(2003),
pp. 1101-1108, MIT Press, ISBN 0-262-20152-6 [abs]
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dept@math.duke.edu
ph: 919.660.2800
fax: 919.660.2821
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Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320
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