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

Papers Published

  1. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. 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]
  18. 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]
  19. Ertekin, Ş; Rudin, C, A Bayesian Approach to Learning Scoring Systems, Big Data, vol. 3 no. 4 (December, 2015), pp. 267-276 [doi]
  20. 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]
  21. 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]
  22. 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]
  23. 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]
  24. 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]
  25. Wang, F; Rudin, C, Falling rule lists, Journal of Machine Learning Research, vol. 38 (January, 2015), pp. 1013-1022  [abs]
  26. 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]
  27. Tulabandhula, T; Rudin, C, On combining machine learning with decision making, Machine Learning, vol. 97 no. 1-2 (October, 2014), pp. 33-64 [doi]
  28. 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]
  29. Kim, B; Rudin, C, Learning about meetings, Data Mining and Knowledge Discovery, vol. 28 no. 5-6 (September, 2014), pp. 1134-1157 [doi]
  30. 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]
  31. 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]
  32. Rudin, C; Wagstaff, KL, Machine learning for science and society, Machine Learning, vol. 95 no. 1 (April, 2014), pp. 1-9 [doi]
  33. Ban, GY; Rudin, C, The Big Data Newsvendor: Practical Insights from Machine Learning (February, 2014)
  34. 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]
  35. 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]
  36. 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]
  37. 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]
  38. 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]
  39. 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]
  40. Letham, B; Rudin, C; Madigan, D, Sequential event prediction, Machine Learning, vol. 93 no. 2-3 (November, 2013), pp. 357-380 [doi]
  41. 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]
  42. 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 (October, 2013), pp. 515-530, ISBN 9783642409936 [doi]  [abs]
  43. 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]
  44. Tulabandhula, T; Rudin, C, Machine learning with operational costs, Journal of Machine Learning Research, vol. 14 (June, 2013), pp. 1989-2028  [abs]
  45. 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
  46. 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]
  47. 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]
  48. 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]
  49. 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
  50. Tulabandhula, T; Rudin, C, The influence of operational cost on estimation, International Symposium on Artificial Intelligence and Mathematics, ISAIM 2012 (December, 2012)  [abs]
  51. 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]
  52. 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]
  53. 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]
  54. 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]
  55. 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]
  56. 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 (October, 2011), pp. 262-276, ISBN 9783642248726 [doi]  [abs]
  57. 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]
  58. 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]
  59. 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]
  60. Rudin, C; Letham, B; Kogan, E; Madigan, D, A Learning Theory Framework for Association Rules and Sequential Events (June, 2011)
  61. 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]
  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. Rudin, C; Letham, B; Salleb-Aouissi, A; Kogan, E; 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. 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]
  66. 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]
  67. 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]
  68. 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]
  69. 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]
  70. 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 (July, 2009), pp. 86-97, ISBN 3642003818 [doi]  [abs]
  71. 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]
  72. Rudin, C; Schapire, RE; Daubechies, I, Analysis of boosting algorithms using the smooth margin function, The Annals of Statistics, vol. 35 no. 6 (2007), pp. 2723-2768 [doi]  [abs]
  73. 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]
  74. 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 (December, 2005), pp. 63-78, ISBN 3540265562  [abs]
  75. 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  [abs]
  76. 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]
  77. Rudin, C; Daubechies, I; Schapire, RE, On the Dynamics of Boosting., edited by Thrun, S; Saul, LK; Schölkopf, B, NIPS (2003), pp. 1101-1108, M I T PRESS, ISBN 0-262-20152-6  [abs]

 

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