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

Papers Published

  1. Rudin, C; Ertekin, Ş, Learning customized and optimized lists of rules with mathematical programming, Mathematical Programming Computation, vol. 10 no. 4 (December, 2018), pp. 659-702, Springer Nature America, Inc [doi]  [abs]
  2. Rudin, C; Ustun, B, Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice, Interfaces, vol. 48 no. 5 (October, 2018), pp. 449-466, Institute for Operations Research and the Management Sciences (INFORMS) [doi]  [abs]
  3. 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]
  4. Angelino, E; Larus-Stone, N; Alabi, D; Seltzer, M; Rudin, C, Learning certifiably optimal rule lists for categorical data, Journal of Machine Learning Research, vol. 18 (January, 2018), pp. 1-78  [abs]
  5. 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]
  6. 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]
  7. 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, vol. 18 (August, 2017), pp. 1-37  [abs]
  8. Letham, B; Letham, PA; Rudin, C; Browne, EP, Erratum: "Prediction uncertainty and optimal experimental design for learning dynamical systems" [Chaos 26, 063110 (2016)]., Chaos (Woodbury, N.Y.), vol. 27 no. 6 (June, 2017), pp. 069901 [doi]
  9. 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, WILEY [doi]
  10. 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]
  11. 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, Icdm (January, 2017), pp. 1269-1274, ISBN 9781509054725 [doi]  [abs]
  12. Yang, H; Rudin, C; Seltzer, M, Scalable Bayesian rule lists, 34th International Conference on Machine Learning, Icml 2017, vol. 8 (January, 2017), pp. 5971-5980, ISBN 9781510855144  [abs]
  13. Angelino, E; Larus-Stone, N; Alabi, D; Seltzer, M; Rudin, C, Learning Certifiably Optimal Rule Lists, Proceedings of the 23rd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining Kdd '17, vol. Part F129685 (2017), pp. 35-44, ACM Press, ISBN 9781450348874 [doi]  [abs]
  14. 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, vol. 17 (June, 2016)  [abs]
  15. Letham, B; Letham, PA; Rudin, C; Browne, EP, Prediction uncertainty and optimal experimental design for learning dynamical systems., Chaos (Woodbury, N.Y.), vol. 26 no. 6 (June, 2016), pp. 063110 [doi]  [abs]
  16. 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, Springer Nature [doi]
  17. Ustun, B; Rudin, C, Supersparse linear integer models for optimized medical scoring systems, Machine Learning, vol. 102 no. 3 (March, 2016), pp. 349-391, Springer Nature [doi]
  18. 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]
  19. 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]
  20. Letham, B; Letham, LM; Rudin, C, Bayesian Inference of Arrival Rate and Substitution Behavior from Sales Transaction Data with Stockouts, Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining Kdd '16, vol. 13-17-August-2016 (2016), pp. 1695-1704, ACM Press, ISBN 9781450342322 [doi]  [abs]
  21. Ertekin, Ş; Rudin, C, A Bayesian Approach to Learning Scoring Systems, Big Data, vol. 3 no. 4 (December, 2015), pp. 267-276, MARY ANN LIEBERT, INC [doi]
  22. 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, Institute of Mathematical Statistics [doi]
  23. 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, Springer Nature [doi]
  24. 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, Institute of Mathematical Statistics [doi]
  25. 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, MARY ANN LIEBERT, INC [doi]
  26. 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, Institute of Mathematical Statistics [doi]
  27. Wang, F; Rudin, C, Falling rule lists, Journal of Machine Learning Research, vol. 38 (January, 2015), pp. 1013-1022  [abs]
  28. Rudin, C, Turning prediction tools into decision tools, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9355 (January, 2015), ISBN 9783319244853  [abs]
  29. Tulabandhula, T; Rudin, C, On combining machine learning with decision making, Machine Learning, vol. 97 no. 1-2 (October, 2014), pp. 33-64, Springer Nature [doi]
  30. Ertekin, Ş; Rudin, C; Hirsh, H, Approximating the crowd, Data Mining and Knowledge Discovery, vol. 28 no. 5-6 (September, 2014), pp. 1189-1221, Springer Nature [doi]
  31. Kim, B; Rudin, C, Learning about meetings, Data Mining and Knowledge Discovery, vol. 28 no. 5-6 (September, 2014), pp. 1134-1157, Springer Nature [doi]
  32. 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, Institute for Operations Research and the Management Sciences (INFORMS) [doi]
  33. 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, MARY ANN LIEBERT, INC [doi]
  34. Huggins, JH; Rudin, C, A Statistical Learning Theory Framework for Supervised Pattern Discovery, Proceedings of the 2014 Siam International Conference on Data Mining, vol. 1 (April, 2014), pp. 506-514, Society for Industrial and Applied Mathematics, ISBN 9781510811515 [doi]  [abs]
  35. Rudin, C; Wagstaff, KL, Machine learning for science and society, Machine Learning, vol. 95 no. 1 (April, 2014), pp. 1-9, Springer Nature [doi]
  36. Ban, GY; Rudin, C, The Big Data Newsvendor: Practical Insights from Machine Learning (February, 2014)
  37. 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]
  38. Goh, ST; Rudin, C, Box drawings for learning with imbalanced data, Proceedings of the 20th Acm Sigkdd International Conference on Knowledge Discovery and Data Mining Kdd '14 (2014), pp. 333-342, ACM Press, ISBN 9781450329569 [doi]  [abs]
  39. 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, Elsevier BV [doi]
  40. 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]
  41. Rudin, C; Letham, B; Madigan, D, Learning theory analysis for association rules and sequential event prediction, Journal of Machine Learning Research, vol. 14 (November, 2013), pp. 3441-3492  [abs]
  42. Letham, B; Rudin, C; Madigan, D, Sequential event prediction, Machine Learning, vol. 93 no. 2-3 (November, 2013), pp. 357-380, Springer Nature [doi]
  43. Letham, B; Rudin, C; Heller, KA, Growing a list, Data Mining and Knowledge Discovery, vol. 27 no. 3 (November, 2013), pp. 372-395, Springer Nature [doi]  [abs]
  44. Mukherjee, I; Rudin, C; Schapire, RE, The rate of convergence of AdaBoost, Journal of Machine Learning Research, vol. 14 (August, 2013), pp. 2315-2347  [abs]
  45. Tulabandhula, T; Rudin, C, Machine learning with operational costs, Journal of Machine Learning Research, vol. 14 (June, 2013), pp. 1989-2028  [abs]
  46. 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
  47. 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]
  48. 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]
  49. 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]
  50. 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
  51. Wang, T; Rudin, C; Wagner, D; Sevieri, R, Learning to Detect Patterns of Crime, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8190 LNAI no. PART 3 (2013), pp. 515-530, Springer Berlin Heidelberg, ISBN 9783642387081 [doi]  [abs]
  52. Tulabandhula, T; Rudin, C, The influence of operational cost on estimation, International Symposium on Artificial Intelligence and Mathematics, Isaim 2012 (December, 2012)  [abs]
  53. 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]
  54. 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]
  55. 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, Springer Nature [doi]
  56. 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, Institute of Mathematical Statistics [doi]
  57. 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, Institute of Electrical and Electronics Engineers (IEEE) [doi]
  58. Ertekin, S; Rudin, C, On equivalence relationships between classification and ranking algorithms, Journal of Machine Learning Research, vol. 12 (October, 2011), pp. 2905-2929  [abs]
  59. Rudin, C; Letham, B; Kogan, E; Madigan, D, A Learning Theory Framework for Association Rules and Sequential Events (June, 2011)
  60. 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, Institute of Electrical and Electronics Engineers (IEEE) [doi]
  61. Wu, L; Teravainen, T; Kaiser, G; Anderson, R; Boulanger, A; Rudin, C, Estimation of system reliability using a semiparametric model, Ieee 2011 Energytech (May, 2011), IEEE, ISBN 9781457707773 [doi]  [abs]
  62. 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)
  63. Madigan, D, Sequential event prediction with association rules, Journal of Machine Learning Research, vol. 19 (January, 2011), pp. 615-634  [abs]
  64. Mukherjee, I; Rudin, C; Schapire, RE, The rate of convergence of AdaBoost, Journal of Machine Learning Research, vol. 19 (January, 2011), pp. 537-557  [abs]
  65. Tulabandhula, T; Rudin, C; Jaillet, P, The Machine Learning and Traveling Repairman Problem, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6992 LNAI (2011), pp. 262-276, Springer Berlin Heidelberg, ISBN 9783642248726 [doi]  [abs]
  66. 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 First International Workshop on Data Mining for Service and Maintenance Kdd4service '11 (2011), pp. 28-32, ACM Press, ISBN 9781450308427 [doi]  [abs]
  67. 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, Springer Nature [doi]
  68. 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]
  69. Radeva, A; Rudin, C; Passonneau, R; Isaac, D, Report Cards for Manholes: Eliciting Expert Feedback for a Learning Task, 2009 International Conference on Machine Learning and Applications (December, 2009), pp. 719-724, IEEE, ISBN 9780769539263 [doi]  [abs]
  70. Rudin, C; Schapire, RE, Margin-based ranking and an equivalence between AdaBoost and RankBoost, Journal of Machine Learning Research, vol. 10 (November, 2009), pp. 2193-2232  [abs]
  71. Rudin, C, The P-norm push: A simple convex ranking algorithm that concentrates at the top of the list, Journal of Machine Learning Research, vol. 10 (November, 2009), pp. 2233-2271  [abs]
  72. 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 (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5449 LNCS (2009), pp. 86-97, Springer Berlin Heidelberg, ISBN 9783642003813 [doi]  [abs]
  73. 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]
  74. Rudin, C; Schapire, RE; Daubechies, I, Analysis of boosting algorithms using the smooth margin function, The Annals of Statistics, vol. 35 no. 6 (December, 2007), pp. 2723-2768, Institute of Mathematical Statistics [doi]  [abs]
  75. Rudin, C, Ranking with a P-norm push, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4005 LNAI (January, 2006), pp. 589-604, ISBN 3540352945  [abs]
  76. Rudin, C; Cortes, C; Mohri, M; Schapire, RE, Margin-Based Ranking Meets Boosting in the Middle, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3559 LNAI (2005), pp. 63-78, Springer Berlin Heidelberg, ISBN 9783540265566 [doi]  [abs]
  77. Rudin, C; Daubechies, I; Schapire, RE, On the dynamics of boosting, edited by Thrun, S; Saul, LK; Schölkopf, B, Advances in Neural Information Processing Systems (January, 2004), pp. 1101-1108, M I T PRESS, ISBN 0262201526  [abs]
  78. Rudin, C; Schapire, RE; Daubechies, I, Boosting Based on a Smooth Margin, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3120 (2004), pp. 502-517, Springer Berlin Heidelberg [doi]  [abs]
  79. 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]

 

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