Elamur Research⌕
☰
home / authors / Cynthia D Rudin / all works / page 3

Cynthia D Rudin — all works

330 works · page 3 of 4 · 201–300 · ← Cynthia D Rudin

1 2 3 4

  1. Embryo Selection by “Black-Box” Artificial Intelligence: The Ethical and Epistemic Considerations ↗Fertility & Reproduction · no summary yetYanhe LIU et al.2022 · 2 citations
  2. Effects of Epileptiform Activity on Discharge Outcome in Critically Ill Patients: A Retrospective Cross-Sectional Study ↗SSRN Electronic Journal · no summary yetHarsh Parikh et al.2022 · 2 citations
  3. Prediction uncertainty and optimal experimental design for learning dynamical systems ↗Carolina Digital Repository (University of North Carolina at Chapel Hill) · no summary yetCynthia D Rudin et al.2020 · 2 citations
  4. Interpretable Almost Matching Exactly for Causal Inference ↗arXiv (Cornell University) · no summary yetYameng Liu et al.2018 · 2 citations
  5. Bayesian Patchworks: An Approach to Case-Based Reasoning ↗arXiv (Cornell University) · no summary yetRamin Moghaddass, Cynthia D Rudin2018 · 2 citations
  6. Learning Cost-Effective and Interpretable Treatment Regimes for Judicial Bail Decisions ↗no summary yetHimabindu Lakkaraju, Cynthia D Rudin2016 · 2 citations
  7. Open Problem: Does AdaBoost Always Cycle? ↗no summary yetCynthia D Rudin, Robert E. Schapire, Ingrid C. Daubechies2012 · 2 citations
  8. Progressive Clustering with Learned Seeds: An Event Categorization System for Power Grid. ↗no summary yetBoyi Xie et al.2012 · 2 citations
  9. A DISCRETE OPTIMIZATION APPROACH TO SUPERVISED RANKING ↗no summary yetDimitris Bertsimas, Allison Chang, Cynthia D Rudin2010 · 2 citations
  10. Falling Trees: A Model Class for Interpretable Risk Prioritization ↗arXiv (Cornell University) · no summary yetVarun Babbar et al.2026 · 1 citation
  11. Resolving Predictive Multiplicity for the Rashomon Set ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetParian Haghighat, Hadis Anahideh, Cynthia D Rudin2026 · 1 citation
  12. Data-hugging shields proprietary AI models from research that could disprove them ↗npj Artificial Intelligence · no summary yetAnish Karpurapu et al.2026 · 1 citation
  13. Cosine Similarity is Almost All You Need (for Prototypical-Part Models) ↗IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · no summary yetLuke Moffett et al.2026 · 1 citation
  14. REALITrees: Rashomon Ensemble Active Learning for Interpretable Trees ↗arXiv (Cornell University) · no summary yetSimon Nguyen et al.2026 · 1 citation
  15. AutoSchA: Automatic Hierarchical Music Representations via Multi-Relational Node Isolation ↗arXiv (Cornell University) · no summary yetStephen Ni-Hahn et al.2025 · 1 citation
  16. ProGress: Structured Music Generation via Graph Diffusion and Hierarchical Music Analysis ↗arXiv (Cornell University) · no summary yetStephen Ni-Hahn et al.2025 · 1 citation
  17. A Computational Framework to design 3D stiffness gradient acoustic metamaterials for impedance matching ↗Computer Methods in Applied Mechanics and Engineering · no summary yetRayehe Karimi Mahabadi et al.2025 · 1 citation
  18. Leo Breiman, the Rashomon Effect, and the Occam Dilemma ↗arXiv (Cornell University) · no summary yetCynthia D Rudin2025 · 1 citation
  19. Phononic Materials with Effectively Scale-Separated Hierarchical Features Using Interpretable Machine Learning ↗SSRN Electronic Journal · no summary yetMary V. Bastawrous et al.2024 · 1 citation
  20. SiamQuality: A ConvNet-Based Foundation Model for Imperfect Physiological Signals ↗arXiv (Cornell University) · no summary yetCheng Ding et al.2024 · 1 citation
  21. Testing AI ↗SSRN Electronic Journal · no summary yetBrandon L. Garrett, Cynthia D Rudin2024 · 1 citation
  22. FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models ↗neural information processing systems · no summary yetJiachang Liu, Rui Zhang, Cynthia D Rudin2024 · 1 citation
  23. ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges ↗arXiv (Cornell University) · no summary yetTang, Dennis et al.2023 · 1 citation
  24. Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data ↗arXiv (Cornell University) · no summary yetSrikar Katta et al.2023 · 1 citation
  25. Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metrics ↗arXiv (Cornell University) · no summary yetMarco Morucci, Cynthia D Rudin, Alexander Volfovsky2023 · 1 citation
  26. Uncertainty Quantification of Bandgaps in Acoustic Metamaterials with Stochastic Geometric Defects and Material Properties ↗arXiv (Cornell University) · no summary yetHan Zhang et al.2023 · 1 citation
  27. There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes ↗arXiv (Cornell University) · no summary yetYishay Mansour, Michal Moshkovitz, Cynthia D Rudin2022 · 1 citation
  28. BacHMMachine: An Interpretable and Scalable Model for Algorithmic Harmonization for Four-part Baroque Chorales ↗arXiv (Cornell University) · no summary yetZhu, Yunyao et al.2021 · 1 citation
  29. Cryo-ZSSR: multiple-image super-resolution based on deep internal learning ↗arXiv (Cornell University) · no summary yetQinwen Huang et al.2020 · 1 citation
  30. Bandits for BMO Functions ↗arXiv (Cornell University) · no summary yetTianyu Wang, Cynthia D Rudin2020 · 1 citation
  31. Learning Cost-Effective and Interpretable Regimes for Treatment Recommendation ↗arXiv (Cornell University) · no summary yetHimabindu Lakkaraju, Cynthia D Rudin2016 · 1 citation
  32. Regulating Greed Over Time in Multi-Armed Bandits ↗arXiv (Cornell University) · no summary yetStefano Tracà, Cynthia D Rudin, Weiyu Yan2015 · 1 citation
  33. Computational Models of Cultural Behavior for Human-Agent Interaction (Dagstuhl Seminar 14,131) ↗no summary yetJohannes Fürnkranz et al.2014 · 1 citation
  34. The big data newsvendor: Practical insights from machine learning. working paper ↗no summary yetCynthia D Rudin et al.2014 · 1 citation
  35. Generalization bounds for learning with linear, polygonal, quadratic and conic side knowledge ↗Machine Learning · no summary yetTheja Tulabandhula, Cynthia D Rudin2014 · 1 citation
  36. Selective Sampling of Labelers for Approximating the Crowd ↗OpenMETU (Middle East Technical University) · no summary yetŞeyda Ertekin, Haym Hirsh, Cynthia D Rudin2012 · 1 citation
  37. Treatment Effect of Repairs to an Electrical Grid Leveraging a Machine Learned Model of Structure Vulnerability ↗no summary yetAxinia Radeva et al.2012 · 1 citation
  38. Supplement to “Bayesian hierarchical rule modeling for predicting medical conditions.” DOI:10.1214 ↗no summary yetH. Mccormick, Cynthia D Rudin, David Madigan2011 · 1 citation
  39. Visualization of manhole and precursor-type events for the Manhattan electrical distribution system ↗no summary yetHaimonti Dutta et al.2008 · 1 citation
  40. Conditional genome-wide associations reveal novel genes ↗bioRxiv (Cold Spring Harbor Laboratory) · no summary yetEmily S. Bellis et al.2026
  41. Trustworthy Feature Importance Avoids Unrestricted Permutations ↗arXiv (Cornell University) · no summary yetEmanuele Borgonovo et al.2026
  42. FAIR Data and Interpretable AI Framework for Architectured Metamaterials ↗no summary yetL. Catherine Brinson, Duke Univ., Durham, NC (United States), Cynthia D Rudin2026
  43. ArborEnum: Decision Tree Rashomon Sets over Continuous Features ↗arXiv (Cornell University) · no summary yetZakk Heile et al.2026
  44. Multistage Defer Trees for Hybrid Interpretability: If at First You Can't Succeed, Tree Again ↗arXiv (Cornell University) · no summary yetZakk Heile et al.2026
  45. Fine annotation loss and top‑k analysis in interpretable models for breast cancer prediction ↗SPIE Medical Imaging: Image Perception, Observer Performance, and Technology Assessment · no summary yetGiyoung Kim et al.2026
  46. Interpretable Processing–Structure Relationships in Spirulina Bioplastics through Automated Spectral and Microstructural Analysis ↗ChemRxiv · no summary yetKuotian Liao et al.2026
  47. Do Judges Behave like Algorithms? ↗Proceedings of the AAAI/ACM Conference on AI Ethics and Society · no summary yetRiya Manchanda et al.2026
  48. Sustainable AI: Deployment and Opportunities for Statistical Practice ↗Zenodo (CERN European Organization for Nuclear Research) · no summary yetElizabeth Mannshardt et al.2026
  49. CICADAS: A simulation-based framework for randomized trial emulation in critical-care seizure treatment ↗Intelligence-Based Medicine · no summary yetMitchell McCauley et al.2026
  50. Applying a Cost-Benefit Analysis to Geofence Searches ↗SSRN Electronic Journal · no summary yetAbraham Meltzer, Cynthia D Rudin, Brandon L. Garrett2026
  51. Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods ↗arXiv (Cornell University) · no summary yetMichal Moshkovitz et al.2026
  52. Fast Rashomon Sets of Sparse Rule Sets ↗Machine Learning · no summary yetCristina Molero-Río et al.2026
  53. The Rashomon Effect for Visualizing High-Dimensional Data ↗arXiv (Cornell University) · no summary yetYiyang Sun et al.2026
  54. CLARITree: Cholesky and Lookahead Accelerations for Regression with Interpretable Piecewise Linear Trees ↗arXiv (Cornell University) · no summary yetYixiao Wang et al.2026
  55. Distinct Transcriptomic Profiles in ART-Treated People With HIV Are Associated With NF-κB–Regulated Gene Expression and Inflammatory Cytokine Signatures ↗JAIDS Journal of Acquired Immune Deficiency Syndromes · no summary yetYingfan Wang et al.2026
  56. Getting Motif-ated: Controllable AI Compositions from Injected Motif Prompts ↗arXiv (Cornell University) · no summary yetChao Peter Yang et al.2026
  57. A Multimodal Benchmark for Evaluating Cause-of-Death Inference Using Child Health and Mortality Data ↗medRxiv · no summary yetJunhe Yang et al.2026
  58. Learning Metamaterial Eigenmodes with Wavelet-Encoded Fourier Neural Operators ↗arXiv (Cornell University) · no summary yetHan Zhang et al.2026
  59. The National Artificial Intelligence Advisory Committee (NAIAC)- Recommendation: Expand the AI Use Case Inventory by Limiting the 'Common Commercial Products' Exception ↗SSRN Electronic Journal · no summary yetArmando Aguilar et al.2025
  60. The National Artificial Intelligence Advisory Committee (NAIAC)- Recommendation: Field Testing of Law Enforcement AI Tools ↗SSRN Electronic Journal · no summary yetArmando Aguilar et al.2025
  61. Discussing a Framework for the Responsible Use of Facial Recognition Technology in Law Enforcement - The National Artificial Intelligence Advisory Committee Law Enforcement Subcommittee (NAIAC-LE Subcommittee) ↗SSRN Electronic Journal · no summary yetJane R. Yakowitz Bambauer et al.2025
  62. The National Artificial Intelligence Advisory Committee (NAIAC)- Recommendation: Require Public Summary Reporting on Use of High-Risk AI ↗SSRN Electronic Journal · no summary yetJane R. Yakowitz Bambauer et al.2025
  63. The National Artificial Intelligence Advisory Committee (NAIAC)- Recommendation: Require Public Use Policies for High-Risk AI ↗SSRN Electronic Journal · no summary yetJane R. Yakowitz Bambauer et al.2025
  64. Revisiting Broken Windows Theory ↗arXiv (Cornell University) · no summary yetZiyao Cui et al.2025
  65. Data Fusion for Partial Identification of Causal Effects ↗neural information processing systems · no summary yetQuinn Lanners et al.2025
  66. How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetIshaan Maitra et al.2025
  67. What You See is (Usually) What You Get: Multimodal Prototype Networks that Abstain from Expensive Modalities ↗arXiv (Cornell University) · no summary yetBahng, Muchang et al.2025
  68. It's LIT! Reliability-Optimized LLMs with Inspectable Tools ↗arXiv (Cornell University) · no summary yetZhang, Ruixin et al.2025
  69. Improving annotation efficiency for fully labeling a breast mass segmentation dataset ↗Journal of medical imaging · no summary yetVaibhav Sharma et al.2025
  70. This EEG Looks Like These EEGs: Interpretable Interictal Epileptiform Discharge Detection With ProtoEEG-kNN ↗Lecture notes in computer science · no summary yetDennis Tang et al.2025
  71. NodMAISI: Nodule-Oriented Medical AI for Synthetic Imaging ↗arXiv (Cornell University) · no summary yetFakrul Islam Tushar et al.2025
  72. NF-κB dependent gene expression and plasma IL-1β, TNFα and GCSF drive transcriptomic diversity and CD4:CD8 ratio in people with HIV on ART ↗bioRxiv (Cold Spring Harbor Laboratory) · no summary yetYingfan Wang et al.2025
  73. Models That Are Interpretable But Not Transparent ↗arXiv (Cornell University) · no summary yetChudi Zhong, Chen, Panyu, Cynthia D Rudin2025
  74. "What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts ↗arXiv (Cornell University) · no summary yetVarun Babbar, Zhicheng Guo, Cynthia D Rudin2024
  75. Data Poisoning Attacks on Off-Policy Policy Evaluation Methods ↗arXiv (Cornell University) · no summary yetElita Lobo et al.2024
  76. A robust framework for the generation of random metamaterials based on a graph algorithm ↗Metamaterials, Metadevices, and Metasystems · no summary yetRayehe Karimi Mahabadi et al.2024
  77. Interpretable deep learning in medical imaging ↗SPIE Medical Imaging: Computer-Aided Diagnosis · no summary yetCynthia D Rudin2024
  78. Improving Decision Sparsity ↗neural information processing systems · no summary yetYiyang Sun, Tong Wang, Cynthia D Rudin2024
  79. Reconsideration on evaluation of machine learning models in continuous monitoring using wearables ↗arXiv (Cornell University) · no summary yetCheng Ding et al.2023
  80. Safe and Interpretable Estimation of Optimal Treatment Regimes ↗arXiv (Cornell University) · no summary yetHarsh Parikh et al.2023
  81. Prediction of 3d Printed Photopolymer Lattice Mechanical Performance Using Structural Porosity, Base Material Properties, and Machine Learning ↗SSRN Electronic Journal · no summary yetJacob Peloquin et al.2023
  82. Understanding How Dimension Reduction Tools Work ↗Proceedings of the International Conference on Statistics, Theory and Applications (ICSTA ...) · no summary yetCynthia D Rudin2023
  83. Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design ↗PubMed · no summary yetAli Behrouz et al.2022
  84. Why Interpretable Causal Inference is Important for High-Stakes Medical Decision Making in Neurology and How to Do it ↗SSRN Electronic Journal · no summary yetHarsh Parikh et al.2022
  85. Collaborative Research: Framework: Data: HDR: Nanocomposites to Metamaterials: A Knowledge Graph Framework ↗Zenodo (CERN European Organization for Nuclear Research) · no summary yetLinda S. Schadler et al.2022
  86. Sparse Density Trees and Lists: An Interpretable Alternative to High-Dimensional Histograms ↗INFORMS Journal on Data Science · no summary yetSiong Thye Goh, Lesia Semenova, Cynthia D Rudin2021
  87. Interpretable Matching for Causal Inference [R package FLAME version 2.1.0] ↗no summary yetVittorio Orlandi et al.2021
  88. Matching Bounds: How Choice of Matching Algorithm Impacts Treatment Effects Estimates and What to Do About It. ↗The Journal of Politics · no summary yetMarco Morucci, Cynthia D Rudin2020
  89. Interpretable Matching for Causal Inference [R package FLAME version 2.0.0] ↗no summary yetVittorio Orlandi et al.2020
  90. Foundations of Sequential Learning ↗no summary yetRonald Parr, Kamesh Munagala, Cynthia D Rudin2018
  91. Pre-trial Risk Analysis ↗no summary yetCynthia D Rudin, Kristin Bechtel, Stuart Buck2018
  92. A Practical Risk Score for EEG Seizures in Hospitalized Patients (S11.002) ↗Neurology · no summary yetAaron F. Struck et al.2018
  93. O4‐12‐03: Using the Digital Clock Drawing Test and Machine Learning to Improve Accuracy of Cognitive Screening ↗Alzheimer s & Dementia · no summary yetWilliam Souillard‐Mandar et al.2016
  94. TD‐P‐003: Using the Digital Clock Drawing Test and Machine Learning to Improve Accuracy of Cognitive Screening ↗Alzheimer s & Dementia · no summary yetWilliam Souillard‐Mandar et al.2016
  95. 1 The Rate of Convergence of AdaBoost The Rate of Convergence of AdaBoost ↗no summary yetIndraneel Mukherjee, Cynthia D Rudin, Robert E. Schapire2016
  96. 解釈可能分類のためのBayesルール集合【Powered by NICT】 ↗IEEE Conference Proceedings · no summary yetWang Tong et al.2016
  97. Cascaded High Dimensional Histograms: A Generative Approach to Density Estimation ↗arXiv (Cornell University) · no summary yetSiong Thye Goh, Cynthia D Rudin2015
  98. Report Cards for Manholes ↗no summary yetAxinia Radeva et al.2015
  99. Approximating the crowd · Aggregating opinions ↗no summary yetCynthia D Rudin et al.2015
  100. Systems at Columbia University ↗no summary yetCynthia D Rudin et al.2015

1 2 3 4

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/authors/cynthia-d-rudin/works/page/3/