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

Books

  1. Rudin, C, Turning prediction tools into decision tools, vol. 9356 (January, 2015), ISBN 9783319242811

Papers Published

  1. Falcinelli, SD; Cooper-Volkheimer, AD; Semenova, L; Wu, E; Richardson, A; Ashokkumar, M; Margolis, DM; Archin, NM; Rudin, CD; Murdoch, D; Browne, EP, Impact of Cannabis Use on Immune Cell Populations and the Viral Reservoir in People With HIV on Suppressive Antiretroviral Therapy., J Infect Dis, vol. 228 no. 11 (November, 2023), pp. 1600-1609 [doi]  [abs]
  2. Garrett, BL; Rudin, C, Interpretable algorithmic forensics., Proceedings of the National Academy of Sciences of the United States of America, vol. 120 no. 41 (October, 2023), pp. e2301842120 [doi]  [abs]
  3. Hahn, S; Zhu, R; Mak, S; Rudin, C; Jiang, Y, An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (August, 2023), pp. 4089-4099, ISBN 9798400701030 [doi]  [abs]
  4. Parikh, H; Hoffman, K; Sun, H; Zafar, SF; Ge, W; Jing, J; Liu, L; Sun, J; Struck, A; Volfovsky, A; Rudin, C; Westover, MB, Effects of epileptiform activity on discharge outcome in critically ill patients in the USA: a retrospective cross-sectional study., The Lancet. Digital health, vol. 5 no. 8 (August, 2023), pp. e495-e502 [doi]  [abs]
  5. Peloquin, J; Kirillova, A; Rudin, C; Brinson, LC; Gall, K, Prediction of tensile performance for 3D printed photopolymer gyroid lattices using structural porosity, base material properties, and machine learning, Materials and Design, vol. 232 (August, 2023) [doi]  [abs]
  6. Peloquin, J; Kirillova, A; Mathey, E; Rudin, C; Brinson, LC; Gall, K, Tensile performance data of 3D printed photopolymer gyroid lattices., Data in brief, vol. 49 (August, 2023), pp. 109396 [doi]  [abs]
  7. McDonald, SM; Augustine, EK; Lanners, Q; Rudin, C; Catherine Brinson, L; Becker, ML, Applied machine learning as a driver for polymeric biomaterials design., Nature communications, vol. 14 no. 1 (August, 2023), pp. 4838 [doi]  [abs]
  8. Zhang, R; Xin, R; Seltzer, M; Rudin, C, Optimal Sparse Regression Trees, Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, vol. 37 (June, 2023), pp. 11270-11279, ISBN 9781577358800  [abs]
  9. Wang, C; Han, B; Patel, B; Rudin, C, In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction, Journal of Quantitative Criminology, vol. 39 no. 2 (June, 2023), pp. 519-581 [doi]  [abs]
  10. Ou, YJ; Barnett, AJ; Mitra, A; Schwartz, FR; Chen, C; Grimm, L; Lo, JY; Rudin, C, A user interface to communicate interpretable AI decisions to radiologists, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 12467 (January, 2023), ISBN 9781510660397 [doi]  [abs]
  11. Lanners, Q; Parikh, H; Volfovsky, A; Rudin, C; Page, D, Variable Importance Matching for Causal Inference, Proceedings of Machine Learning Research, vol. 216 (January, 2023), pp. 1174-1184  [abs]
  12. Agnew, E; Qiu, M; Zhu, L; Wiseman, S; Rudin, C, The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation, Proceedings of the Annual Meeting of the Association for Computational Linguistics, vol. 2 (January, 2023), pp. 1627-1638, ISBN 9781959429715  [abs]
  13. Chen, Z; Tan, S; Chajewska, U; Rudin, C; Caruana, R, Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?, Proceedings of Machine Learning Research, vol. 209 (January, 2023), pp. 86-99  [abs]
  14. Garrett, BL; Rudin, C, The Right to a Glass Box: Rethinking the Use of Artificial Intelligence in Criminal Justice, Cornell Law Review (2023)
  15. Rudin, C, Why black box machine learning should be avoided for high-stakes decisions, in brief, Nature Reviews Methods Primers, vol. 2 no. 1 (December, 2022) [doi]
  16. Chen, Z; Ogren, A; Daraio, C; Brinson, LC; Rudin, C, How to see hidden patterns in metamaterials with interpretable machine learning, Extreme Mechanics Letters, vol. 57 (November, 2022) [doi]  [abs]
  17. Behrouz, A; Lécuyer, M; Rudin, C; Seltzer, M, Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design., CEUR workshop proceedings, vol. 3318 (October, 2022), pp. 26  [abs]
  18. Parikh, H; Rudin, C; Volfovsky, A, MALTS: Matching After Learning to Stretch, Journal of Machine Learning Research, vol. 23 (August, 2022)  [abs]
  19. Afnan, M; Afnan, MAM; Liu, Y; Savulescu, J; Mishra, A; Conitzer, V; Rudin, C, Data solidarity for machine learning for embryo selection: a call for the creation of an open access repository of embryo data., Reproductive biomedicine online, vol. 45 no. 1 (July, 2022), pp. 10-13 [doi]  [abs]
  20. Huang, H; Wang, Y; Rudin, C; Browne, EP, Towards a comprehensive evaluation of dimension reduction methods for transcriptomic data visualization., Communications biology, vol. 5 no. 1 (July, 2022), pp. 719 [doi]  [abs]
  21. Semenova, L; Rudin, C; Parr, R, On the Existence of Simpler Machine Learning Models, ACM International Conference Proceeding Series (June, 2022), pp. 1827-1858, ISBN 9781450393522 [doi]  [abs]
  22. Wang, T; Rudin, C, Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effects, INFORMS Journal on Computing, vol. 34 no. 3 (May, 2022), pp. 1626-1643 [doi]  [abs]
  23. Liu, J; Zhong, C; Seltzer, M; Rudin, C, Fast Sparse Classification for Generalized Linear and Additive Models., Proceedings of machine learning research, vol. 151 (March, 2022), pp. 9304-9333  [abs]
  24. Chen, C; Lin, K; Rudin, C; Shaposhnik, Y; Wang, S; Wang, T, A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations, Decision Support Systems, vol. 152 (January, 2022) [doi]  [abs]
  25. Rudin, C; Chen, C; Chen, Z; Huang, H; Semenova, L; Zhong, C, Interpretable machine learning: Fundamental principles and 10 grand challenges, Statistics Surveys, vol. 16 (January, 2022), pp. 1-85 [doi]  [abs]
  26. Li, C; Rudin, C; McCormick, TH, Rethinking Nonlinear Instrumental Variable Models through Prediction Validity, Journal of Machine Learning Research, vol. 23 (January, 2022)  [abs]
  27. McTavish, H; Zhong, C; Achermann, R; Karimalis, I; Chen, J; Rudin, C; Seltzer, M, Fast Sparse Decision Tree Optimization via Reference Ensembles., Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, vol. 36 no. 9 (January, 2022), pp. 9604-9613 [doi]  [abs]
  28. Barnett, AJ; Sharma, V; Gajjar, N; Fang, J; Schwartz, FR; Chen, C; Lo, JY; Rudin, C, Interpretable Deep Learning Models for Better Clinician-AI Communication in Clinical Mammography, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 12035 (January, 2022), ISBN 9781510649453 [doi]  [abs]
  29. Wang, ZJ; Zhong, C; Xin, R; Takagi, T; Chen, Z; Chau, DH; Rudin, C; Seltzer, M, TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization, Proceedings - 2022 IEEE Visualization Conference - Short Papers, VIS 2022 (January, 2022), pp. 60-64, ISBN 9781665488129 [doi]  [abs]
  30. Liu, J; Zhong, C; Li, B; Seltzer, M; Rudin, C, FasterRisk: Fast and Accurate Interpretable Risk Scores, Advances in Neural Information Processing Systems, vol. 35 (January, 2022), ISBN 9781713871088  [abs]
  31. Lobo, E; Singh, H; Petrik, M; Rudin, C; Lakkaraju, H, Data Poisoning Attacks on Off-Policy Policy Evaluation Methods, Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022 (January, 2022), pp. 1264-1274, ISBN 9781713863298  [abs]
  32. Xin, R; Zhong, C; Chen, Z; Takagi, T; Seltzer, M; Rudin, C, Exploring the Whole Rashomon Set of Sparse Decision Trees., Advances in neural information processing systems, vol. 35 (January, 2022), pp. 14071-14084, ISBN 9781713871088  [abs]
  33. Lobo, E; Singh, H; Petrik, M; Rudin, C; Lakkaraju, H, Data Poisoning Attacks on Off-Policy Policy Evaluation Methods, Proceedings of Machine Learning Research, vol. 180 (January, 2022), pp. 1264-1274  [abs]
  34. Guo, Z; Ding, C; Hu, X; Rudin, C, A supervised machine learning semantic segmentation approach for detecting artifacts in plethysmography signals from wearables., Physiological measurement, vol. 42 no. 12 (December, 2021) [doi]  [abs]
  35. Barnett, AJ; Schwartz, FR; Tao, C; Chen, C; Ren, Y; Lo, JY; Rudin, C, A case-based interpretable deep learning model for classification of mass lesions in digital mammography, Nature Machine Intelligence, vol. 3 no. 12 (December, 2021), pp. 1061-1070 [doi]  [abs]
  36. Coker, B; Rudin, C; King, G, A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results, Management Science, vol. 67 no. 10 (October, 2021), pp. 6174-6197 [doi]  [abs]
  37. Afnan, MAM; Rudin, C; Conitzer, V; Savulescu, J; Mishra, A; Liu, Y; Afnan, M, Ethical Implementation of Artificial Intelligence to Select Embryos in in Vitro Fertilization, AIES 2021 - Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (July, 2021), pp. 316-326, ISBN 9781450384735 [doi]  [abs]
  38. Wang, J; Zhang, X; Zhou, Y; Suh, C; Rudin, C, There once was a really bad poet, it was automated but you didn’t know it, Transactions of the Association for Computational Linguistics, vol. 9 (July, 2021), pp. 605-620 [doi]  [abs]
  39. Barnett, AJ; Schwartz, FR; Tao, C; Chen, C; Ren, Y; Lo, JY; Rudin, C, IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography (March, 2021)  [abs]
  40. Gupta, NR; Orlandi, V; Chang, C-R; Wang, T; Morucci, M; Dey, P; Howell, TJ; Sun, X; Ghosal, A; Roy, S; Rudin, C; Volfovsky, A, dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference, vol. abs/2101.01867 (January, 2021)  [abs]
  41. Wang, Y; Huang, H; Rudin, C; Shaposhnik, Y, Understanding how dimension reduction tools work: An empirical approach to deciphering T-SNE, UMAP, TriMap, and PaCMAP for data visualization, Journal of Machine Learning Research, vol. 22 (January, 2021)  [abs]
  42. Traca, S; Rudin, C; Yan, W, Regulating greed over time in multi-armed bandits, Journal of Machine Learning Research, vol. 22 (January, 2021)  [abs]
  43. Koyyalagunta, D; Sun, A; Draelos, RL; Rudin, C, Playing codenames with language graphs and word embeddings, Journal of Artificial Intelligence Research, vol. 71 (January, 2021), pp. 319-346 [doi]  [abs]
  44. Afnan, MAM; Liu, Y; Conitzer, V; Rudin, C; Mishra, A; Savulescu, J; Afnan, M, Interpretable, not black-box, artificial intelligence should be used for embryo selection., Human reproduction open, vol. 2021 no. 4 (January, 2021), pp. hoab040 [doi]  [abs]
  45. Wang, T; Morucci, M; Awan, MU; Liu, Y; Roy, S; Rudin, C; Volfovsky, A, FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference., J. Mach. Learn. Res., vol. 22 (2021), pp. 31:1-31:1  [abs]
  46. Chen, Z; Bei, Y; Rudin, C, Concept whitening for interpretable image recognition, Nature Machine Intelligence, vol. 2 no. 12 (December, 2020), pp. 772-782 [doi]  [abs]
  47. Dong, J; Rudin, C, Exploring the cloud of variable importance for the set of all good models, Nature Machine Intelligence, vol. 2 no. 12 (December, 2020), pp. 810-824 [doi]  [abs]
  48. Huang, Q; Zhou, Y; Du, X; Chen, R; Wang, J; Rudin, C; Bartesaghi, A, Cryo-ZSSR: multiple-image super-resolution based on deep internal learning, vol. abs/2011.11020 (November, 2020)  [abs]
  49. Wang, T; Ye, W; Geng, D; Rudin, C, Towards Practical Lipschitz Bandits, FODS 2020 - Proceedings of the 2020 ACM-IMS Foundations of Data Science Conference (October, 2020), pp. 129-138, ISBN 9781450381031 [doi]  [abs]
  50. Rich, AS; Rudin, C; Jacoby, DMP; Freeman, R; Wearn, OR; Shevlin, H; Dihal, K; ÓhÉigeartaigh, SS; Butcher, J; Lippi, M; Palka, P; Torroni, P; Wongvibulsin, S; Begoli, E; Schneider, G; Cave, S; Sloane, M; Moss, E; Rahwan, I; Goldberg, K; Howard, D; Floridi, L; Stilgoe, J, AI reflections in 2019, Nature Machine Intelligence, vol. 2 no. 1 (January, 2020), pp. 2-9, Springer Science and Business Media LLC [doi]
  51. Morucci, M; Orlandi, V; Rudin, C; Roy, S; Volfovsky, A, Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation, edited by Adams, RP; Gogate, V, Proceedings of Machine Learning Research, vol. 124 (January, 2020), pp. 1089-1098, AUAI Press  [abs]
  52. Gregory, H; Li, S; Mohammadi, P; Tarn, N; Draelos, R; Rudin, C, A transformer approach to contextual sarcasm detection in twitter, Proceedings of the Annual Meeting of the Association for Computational Linguistics (January, 2020), pp. 270-275, ISBN 9781952148125 [doi]  [abs]
  53. Awan, MU; Morucci, M; Orlandi, V; Roy, S; Rudin, C; Volfovsky, A, Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference, edited by Chiappa, S; Calandra, R, Proceedings of Machine Learning Research, vol. 108 (January, 2020), pp. 3252-3262, PMLR  [abs]
  54. Menon, S; Damian, A; Hu, S; Ravi, N; Rudin, C, PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (January, 2020), pp. 2434-2442 [doi]  [abs]
  55. Wang, T; Rudin, C, Bandits for bmo functions, 37th International Conference on Machine Learning, ICML 2020, vol. PartF168147-13 (January, 2020), pp. 9938-9948, ISBN 9781713821120  [abs]
  56. Lin, J; Zhong, C; Hu, D; Rudin, C; Seltzer, M, Generalized and scalable optimal sparse decision trees, 37th International Conference on Machine Learning, ICML 2020, vol. PartF168147-8 (January, 2020), pp. 6106-6116, ISBN 9781713821120  [abs]
  57. Morucci, M; Orlandi, V; Rudin, C; Roy, S; Volfovsky, A, Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation, CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE (UAI 2020), vol. 124 (2020), pp. 1089-1098
  58. Awan, MU; Roy, S; Morucci, M; Rudin, C; Orlandi, V; Volfovsky, A, Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference, INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 108, vol. 108 (2020), pp. 3252-3261
  59. Gregory, H; Li, S; Mohammadi, P; Tarn, N; Draelos, R; Rudin, C, A Transformer Approach to Contextual Sarcasm Detection in Twitter, FIGURATIVE LANGUAGE PROCESSING (2020), pp. 270-275
  60. Wang, F; Rudin, C; Mccormick, TH; Gore, JL, Modeling recovery curves with application to prostatectomy., Biostatistics (Oxford, England), vol. 20 no. 4 (October, 2019), pp. 549-564 [doi]  [abs]
  61. Rudin, C, Do Simpler Models Exist and How Can We Find Them?, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (July, 2019), ACM, ISBN 9781450362016 [doi]
  62. Ustun, B; Rudin, C, Learning optimized risk scores, Journal of Machine Learning Research, vol. 20 (June, 2019)  [abs]
  63. Rudin, C; Shaposhnik, Y, Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation, https://www.jmlr.org/papers/, vol. 24 (May, 2019)
  64. Bravo, F; Rudin, C; Shaposhnik, Y; Yuan, Y, Simple Rules for Predicting Congestion Risk in Queueing Systems: Application to ICUs (May, 2019)
  65. Rudin, C, Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead., Nature machine intelligence, vol. 1 no. 5 (May, 2019), pp. 206-215 [doi]  [abs]
  66. Dieng, A; Liu, Y; Roy, S; Rudin, C; Volfovsky, A, Interpretable Almost-Exact Matching for Causal Inference., Proceedings of machine learning research, vol. 89 (April, 2019), pp. 2445-2453  [abs]
  67. Ban, GY; Rudin, C, The big Data newsvendor: Practical insights from machine learning, Operations Research, vol. 67 no. 1 (January, 2019), pp. 90-108 [doi]  [abs]
  68. Usaid Awan, M; Liu, Y; Morucci, M; Roy, S; Rudin, C; Volfovsky, A, Interpretable almost-matching-exactly with instrumental variables, 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019 (January, 2019)  [abs]
  69. Fisher, A; Rudin, C; Dominici, F, 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 : JMLR, vol. 20 (January, 2019), pp. 177  [abs]
  70. Parikh, H; Rudin, C; Volfovsky, A, An Application of Matching After Learning To Stretch (MALTS) to the ACIC 2018 Causal Inference Challenge Data, Observational Studies, vol. 5 no. 2 (January, 2019), pp. 118-130 [doi]  [abs]
  71. Tracà, S; Rudin, C; Yan, W, Reducing exploration of dying arms in mortal bandits, 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019 (January, 2019)  [abs]
  72. Usaid Awan, M; Liu, Y; Morucci, M; Roy, S; Rudin, C; Volfovsky, A, Interpretable almost-matching-exactly with instrumental variables, 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019 (January, 2019)  [abs]
  73. Tracà, S; Rudin, C; Yan, W, Reducing exploration of dying arms in mortal bandits, 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019 (January, 2019)  [abs]
  74. Chen, C; Li, O; Tao, C; Barnett, AJ; Su, J; Rudin, C, This looks like that: Deep learning for interpretable image recognition, Advances in Neural Information Processing Systems, vol. 32 (January, 2019)  [abs]
  75. Hu, X; Rudin, C; Seltzer, M, Optimal sparse decision trees, Advances in Neural Information Processing Systems, vol. 32 (January, 2019)  [abs]
  76. Hase, P; Chen, C; Li, O; Rudin, C, Interpretable Image Recognition with Hierarchical Prototypes, Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, vol. 7 (January, 2019), pp. 32-40, ISBN 9781577358206 [doi]  [abs]
  77. Awan, MU; Liu, Y; Morucci, M; Roy, S; Rudin, C; Volfovsky, A, Interpretable Almost-Matching-Exactly With Instrumental Variables, Proceedings of Machine Learning Research, vol. 115 (January, 2019), pp. 1116-1126  [abs]
  78. Tracà, S; Rudin, C; Yan, W, Reducing Exploration of Dying Arms in Mortal Bandits, Proceedings of Machine Learning Research, vol. 115 (January, 2019), pp. 156-163  [abs]
  79. Bei, Y; Damian, A; Hu, S; Menon, S; Ravi, N; Rudin, C, New techniques for preserving global structure and denoising with low information loss in single-image super-resolution, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2018-June (December, 2018), pp. 987-994, ISBN 9781538661000 [doi]  [abs]
  80. Timofte, R; Gu, S; Wu, J; Van Gool, L; Zhang, L; Yang, MH; Haris, M; Shakhnarovich, G; Ukita, N; Hu, S; Bei, Y; Hui, Z; Jiang, X; Gu, Y; Liu, J; Wang, Y; Perazzi, F; McWilliams, B; Sorkine-Hornung, A; Sorkine-Hornung, O; Schroers, C; Yu, J; Fan, Y; Yang, J; Xu, N; Wang, Z; Wang, X; Huang, TS; Yu, K; Hui, TW; Dong, C; Lin, L; Loy, CC; Park, D; Kim, K; Chun, SY; Zhang, K; Liu, P; Zuo, W; Guo, S; Xu, J; Liu, Y; Xiong, F; Dong, Y; Bai, H; Damian, A; Ravi, N; Menon, S; Rudin, C; Seo, J; Jeon, T; Koo, J; Jeon, S; Kim, SY; Choi, JS; Ki, S; Seo, S; Sim, H; Kim, S; Kim, M; Chen, R; Zeng, K; Guo, J; Qu, Y; Li, C; Ahn, N; Kang, B; Sohn, KA; Yuan, Y; Zhang, J; Pang, J; Xu, X; Zhao, Y; Deng, W; Ul Hussain, S; Aadil, M; Rahim, R; Cai, X; Huang, F; Xu, Y; Michelini, PN; Zhu, D; Liu, H; Kim, JH; Lee, JS; Huang, Y; Qiu, M; Jing, L; Zeng, J; Sharma, M; Mukhopadhyay, R; Upadhyay, A; Koundinya, S; Shukla, A; Chaudhury, S; Zhang, Z; Hu, YH, NTIRE 2018 challenge on single image super-resolution: Methods and results, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2018-June (December, 2018), pp. 965-976, IEEE, ISBN 9781538661000 [doi]  [abs]
  81. 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]
  82. Parikh, H; Rudin, C; Volfovsky, A, MALTS: Matching After Learning to Stretch, Journal.of.Machine.Learning.Research 23(240) (2022) 1-42 (November, 2018)  [abs]
  83. Rudin, C; Ustunb, B, Optimized scoring systems: Toward trust in machine learning for healthcare and criminal justice, Interfaces, vol. 48 no. 5 (September, 2018), pp. 449-466, Institute for Operations Research and the Management Sciences (INFORMS) [doi]  [abs]
  84. Liu, Y; Dieng, A; Roy, S; Rudin, C; Volfovsky, A, Interpretable Almost Matching Exactly for Causal Inference, vol. abs/1806.06802 (June, 2018)  [abs]
  85. 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., J Neurosci, vol. 38 no. 7 (February, 2018), pp. 1601-1607, Society for Neuroscience [doi]  [abs]
  86. 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]
  87. Li, O; Liu, H; Chen, C; Rudin, C, Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions, 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (January, 2018), pp. 3530-3537, ISBN 9781577358008  [abs]
  88. Chen, C; Rudin, C, An optimization approach to learning falling rule lists, International Conference on Artificial Intelligence and Statistics, AISTATS 2018 (January, 2018), pp. 604-612  [abs]
  89. Rudin, C; Wang, Y, Direct learning to rank and rerank, International Conference on Artificial Intelligence and Statistics, AISTATS 2018 (January, 2018), pp. 775-783  [abs]
  90. 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]
  91. 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]
  92. 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, ACM Press, ISBN 9781450348874 [doi]  [abs]
  93. 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]
  94. 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]
  95. 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]  [abs]
  96. 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. 520-527 [doi]  [abs]
  97. 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]
  98. Lakkaraju, H; Rudin, C, Learning cost-effective and interpretable treatment regimes, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017 (January, 2017)  [abs]
  99. Lakkaraju, H; Rudin, C, Learning cost-effective and interpretable treatment regimes, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017 (January, 2017)  [abs]
  100. 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, ACM Press, ISBN 9781450342322 [doi]  [abs]
  101. 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 (July, 2016), pp. 1269-1274, ISBN 9781509054725 [doi]  [abs]
  102. 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]
  103. 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]
  104. 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]  [abs]
  105. 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]  [abs]
  106. 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]
  107. 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]
  108. Garg, VK; Rudin, C; Jaakkola, T, CRAFT: ClusteR-specific Assorted Feature selecTion, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016 (January, 2016), pp. 305-313  [abs]
  109. 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]  [abs]
  110. Moghaddass, R; Rudin, C, The latent state hazard model, with application to wind turbine reliability, Annals of Applied Statistics, vol. 9 no. 4 (December, 2015), pp. 1823-1863, Institute of Mathematical Statistics [doi]  [abs]
  111. 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]  [abs]
  112. Letham, B; Rudin, C; McCormick, TH; Madigan, D, Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model, Annals of Applied Statistics, vol. 9 no. 3 (September, 2015), pp. 1350-1371, Institute of Mathematical Statistics [doi]  [abs]
  113. 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]  [abs]
  114. Ertekin, Ş; Rudin, C; McCormick, TH, Reactive point processes: A new approach to predicting power failures in underground electrical systems, Annals of Applied Statistics, vol. 9 no. 1 (January, 2015), pp. 122-144, Institute of Mathematical Statistics [doi]  [abs]
  115. Wang, F; Rudin, C, Falling rule lists, Journal of Machine Learning Research, vol. 38 (January, 2015), pp. 1013-1022  [abs]
  116. 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]
  117. 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]  [abs]
  118. Ertekin, S; Rudin, C; Hirsh, H, Approximating the crowd, Data Mining and Knowledge Discovery, vol. 28 no. 5-6 (January, 2014), pp. 1189-1221, Springer Nature [doi]  [abs]
  119. Kim, B; Rudin, C, Learning about meetings, Data Mining and Knowledge Discovery, vol. 28 no. 5-6 (January, 2014), pp. 1134-1157, Springer Nature [doi]  [abs]
  120. Rudin, C; Ertekin, S; 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 (January, 2014), pp. 364-382, Institute for Operations Research and the Management Sciences (INFORMS) [doi]  [abs]
  121. Rudin, C; Wagstaff, KL, Machine learning for science and society, Machine Learning, vol. 95 no. 1 (January, 2014), pp. 1-9, Springer Nature [doi]  [abs]
  122. Tulabandhula, T; Rudin, C, On combining machine learning with decision making, Machine Learning, vol. 97 no. 1-2 (January, 2014), pp. 33-64, Springer Nature [doi]  [abs]
  123. 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, Society for Industrial and Applied Mathematics, ISBN 9781510811515 [doi]  [abs]
  124. 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]
  125. 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, ACM Press, ISBN 9781450329569 [doi]  [abs]
  126. Wang, D; Passonneau, RJ; Collins, M; Rudin, C, Modeling weather impact on a secondary electrical grid, Procedia Computer Science, vol. 32 (January, 2014), pp. 631-638, Elsevier BV [doi]  [abs]
  127. Tulabandhula, T; Rudin, C, Robust optimization using machine learning for uncertainty sets, International Symposium on Artificial Intelligence and Mathematics, ISAIM 2014 (January, 2014)  [abs]
  128. Tulabandhula, T; Rudin, C, Generalization bounds for learning with linear and quadratic side knowledge, International Symposium on Artificial Intelligence and Mathematics, ISAIM 2014 (January, 2014)  [abs]
  129. Huggins, J; Rudin, C, Toward a theory of pattern discovery, International Symposium on Artificial Intelligence and Mathematics, ISAIM 2014 (January, 2014)  [abs]
  130. Tulabandhula, T; Rudin, C, Robust optimization using machine learning for uncertainty sets, International Symposium on Artificial Intelligence and Mathematics, ISAIM 2014 (January, 2014)  [abs]
  131. Huggins, J; Rudin, C, Toward a theory of pattern discovery, International Symposium on Artificial Intelligence and Mathematics, ISAIM 2014 (January, 2014)  [abs]
  132. Tulabandhula, T; Rudin, C, Generalization bounds for learning with linear and quadratic side knowledge, International Symposium on Artificial Intelligence and Mathematics, ISAIM 2014 (January, 2014)  [abs]
  133. 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]
  134. 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]
  135. Letham, B; Rudin, C; Madigan, D, Sequential event prediction, Machine Learning, vol. 93 no. 2-3 (November, 2013), pp. 357-380, Springer Nature [doi]  [abs]
  136. 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, Springer Berlin Heidelberg, ISBN 9783642409936 [doi]  [abs]
  137. 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]
  138. Tulabandhula, T; Rudin, C, Machine learning with operational costs, Journal of Machine Learning Research, vol. 14 (June, 2013), pp. 1989-2028  [abs]
  139. 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
  140. 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]
  141. 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]
  142. 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]
  143. 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
  144. Tulabandhula, T; Rudin, C, The influence of operational cost on estimation, International Symposium on Artificial Intelligence and Mathematics, ISAIM 2012 (December, 2012)  [abs]
  145. 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]
  146. 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]
  147. Chang, A; Rudin, C; Cavaretta, M; Robert Thomas, ; Gloria Chou, , How to reverse-engineer quality rankings, Machine Learning, vol. 88 no. 3 (September, 2012), pp. 369-398, Springer Nature [doi]  [abs]
  148. McCormick, TH; Rudin, C; Madigan, D, Bayesian hierarchical rule modeling for predicting medical conditions, Annals of Applied Statistics, vol. 6 no. 2 (June, 2012), pp. 652-668, Institute of Mathematical Statistics [doi]  [abs]
  149. Rudin, C; Waltz, D; Anderson, R; Boulanger, A; Salleb-Aouissi, A; Chow, M; Dutta, H; Gross, P; 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 (January, 2012), pp. 328-345, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  150. 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, Springer Berlin Heidelberg, ISBN 9783642248726 [doi]  [abs]
  151. 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]
  152. 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, ACM Press, ISBN 9781450308427 [doi]  [abs]
  153. 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), IEEE, ISBN 9781457707773 [doi]  [abs]
  154. Rudin, C; Letham, B; Kogan, E; Madigan, D, A Learning Theory Framework for Association Rules and Sequential Events (June, 2011)
  155. 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]  [abs]
  156. 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)
  157. 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]
  158. 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]
  159. 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]  [abs]
  160. 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]
  161. 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, IEEE, ISBN 9780769539263 [doi]  [abs]
  162. 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]
  163. 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]
  164. 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, Springer Berlin Heidelberg, ISBN 9783642003813 [doi]  [abs]
  165. 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 (January, 2008), pp. 117-120, ISBN 9781932432046 [doi]  [abs]
  166. Roth, R; Rambow, O; Habash, N; Diab, M; Rudin, C, Arabic morphological tagging, diacritization, and lemmatization using lexeme models and feature ranking, Proceedings of the Annual Meeting of the Association for Computational Linguistics (January, 2008), pp. 117-120  [abs]
  167. Rudin, C; Schapire, RE; Daubechies, I, Analysis of boosting algorithms using the smooth margin function, Annals of Statistics, vol. 35 no. 6 (December, 2007), pp. 2723-2768, Institute of Mathematical Statistics [doi]  [abs]
  168. 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 9783540352945 [doi]  [abs]
  169. Ji, H; Rudin, C; Grishman, R, Re-Ranking Algorithms for Name Tagging, HLT-NAACL 2006 - Computationally Hard Problems and Joint Inference in Speech and Language Processing, Proceedings of the Workshop (January, 2006), pp. 49-56  [abs]
  170. 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 (January, 2005), pp. 63-78, Springer Berlin Heidelberg, ISBN 9783540265566 [doi]  [abs]
  171. Rudin, C; Daubechies, I; Schapire, RE, The dynamics of AdaBoost: Cyclic behavior and convergence of margins, Journal of Machine Learning Research, vol. 5 (December, 2004), pp. 1557-1595  [abs]
  172. Rudin, C; Schapire, RE; Daubechies, I, Boosting based on a smooth margin, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), vol. 3120 (January, 2004), pp. 502-517, Springer Berlin Heidelberg [doi]  [abs]
  173. 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 9780262201520  [abs]

 

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