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Publications of Cynthia D. Rudin    :chronological  alphabetical  combined  bibtex listing:

Papers Published

  1. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. Ertekin, Ş; Rudin, C, A Bayesian Approach to Learning Scoring Systems, Big Data, vol. 3 no. 4 (December, 2015), pp. 267-276 [doi]
  16. 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]
  17. 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]
  18. 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]
  19. 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]
  20. 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]
  21. Wang, F; Rudin, C, Falling rule lists, Journal of machine learning research : JMLR, vol. 38 (January, 2015), pp. 1013-1022  [abs]
  22. Rudin, C, Turning prediction tools into decision tools, Lecture notes in computer science, vol. 9355 (January, 2015), ISBN 9783319244853  [abs]
  23. Tulabandhula, T; Rudin, C, On combining machine learning with decision making, Machine Learning, vol. 97 no. 1-2 (October, 2014), pp. 33-64 [doi]
  24. 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]
  25. Kim, B; Rudin, C, Learning about meetings, Data Mining and Knowledge Discovery, vol. 28 no. 5-6 (September, 2014), pp. 1134-1157 [doi]
  26. 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]
  27. 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]
  28. Rudin, C; Wagstaff, KL, Machine learning for science and society, Machine Learning, vol. 95 no. 1 (April, 2014), pp. 1-9 [doi]
  29. Ban, GY; Rudin, C, The Big Data Newsvendor: Practical Insights from Machine Learning (February, 2014)
  30. 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]
  31. 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]
  32. 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]
  33. 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]
  34. 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]
  35. Letham, B; Rudin, C; Madigan, D, Sequential event prediction, Machine Learning, vol. 93 no. 2-3 (November, 2013), pp. 357-380 [doi]
  36. 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]
  37. 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]
  38. Tulabandhula, T; Rudin, C, Machine learning with operational costs, Journal of machine learning research : JMLR, vol. 14 (June, 2013), pp. 1989-2028  [abs]
  39. 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
  40. 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]
  41. 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]
  42. 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]
  43. 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
  44. Letham, B; Rudin, C; Heller, KA, Growing a list, Data Mining and Knowledge Discovery, vol. 27 no. 3 (2013), pp. 372-395 [doi]  [abs]
  45. Letham, B; Rudin, C; Heller, KA, Growing a list, Data Mining and Knowledge Discovery, vol. 27 no. 3 (2013), pp. 1-24 [doi]  [abs]
  46. Tulabandhula, T; Rudin, C, The influence of operational cost on estimation, International Symposium on Artificial Intelligence and Mathematics, ISAIM 2012 (December, 2012)  [abs]
  47. 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]
  48. 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]
  49. 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]
  50. 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]
  51. 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]
  52. 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]
  53. 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]
  54. 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]
  55. 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]
  56. Rudin, C; Letham, B; Kogan, E; Madigan, D, A Learning Theory Framework for Association Rules and Sequential Events (June, 2011)
  57. 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]
  58. 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)
  59. 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]
  60. 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]
  61. 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]
  62. 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]
  63. 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]
  64. 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]
  65. 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]
  66. 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]
  67. 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]
  68. 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]
  69. Rudin, C, Ranking with a P-norm push, Lecture notes in computer science, vol. 4005 LNAI (January, 2006), pp. 589-604, ISBN 3540352945  [abs]
  70. 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]
  71. Rudin, C; Schapire, RE; Daubechies, I, Boosting based on a smooth margin, Lecture notes in computer science, vol. 3120 (2004), pp. 502-517  [abs]
  72. 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]
  73. 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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