2019 - Fellow of the American Statistical Association (ASA)
Cynthia Rudin links relevant scientific disciplines such as Key (lock) and Network architecture in the realm of Computer security. Her research ties Computer security and Key (lock) together. Quantum mechanics is intertwined with Scale (ratio) and Term (time) in her research. Her Scale (ratio) study frequently draws connections to other fields, such as Quantum mechanics. She connects relevant research areas such as Process (computing) and Encoder in the domain of Operating system. In her works, Cynthia Rudin undertakes multidisciplinary study on Process (computing) and Operating system. Her Machine learning study frequently links to adjacent areas such as Interpretability. Her research on Interpretability often connects related areas such as Artificial intelligence. She incorporates Artificial intelligence and Algorithm in her studies.
Many of her studies on Artificial intelligence apply to Interpretability, Pattern recognition (psychology) and Artificial neural network as well. Cynthia Rudin integrates Machine learning with Data mining in her study. She performs multidisciplinary study in Data mining and Machine learning in her work. She combines topics linked to Set (abstract data type) with her work on Programming language. Her work on Set (abstract data type) is being expanded to include thematically relevant topics such as Programming language. She combines Algorithm and Artificial intelligence in her research.
Her research investigates the connection between Troubleshooting and topics such as Operating system that intersect with issues in Process (computing). In her works, she undertakes multidisciplinary study on Process (computing) and Operating system. She integrates several fields in her works, including Programming language, Data structure and Preprocessor. Her Computer vision study has been linked to subjects such as Artifact (error) and Segmentation. Her work on Computer vision expands to the thematically related Segmentation. She performs integrative Artificial intelligence and Data science research in her work. Cynthia Rudin conducted interdisciplinary study in her works that combined Data science and Data visualization. Her Machine learning study frequently involves adjacent topics like Margin (machine learning). She undertakes interdisciplinary study in the fields of Artificial neural network and Reinforcement learning through her works.
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Nature Machine Intelligence (2019)
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
Benjamin Letham;Cynthia Rudin;Tyler H. McCormick;David Madigan.
The Annals of Applied Statistics (2015)
All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
Aaron Fisher;Cynthia Rudin;Francesca Dominici.
Journal of Machine Learning Research (2019)
Supersparse linear integer models for optimized medical scoring systems
Berk Ustun;Cynthia Rudin.
Machine Learning (2016)
Machine Learning for the New York City Power Grid
C. Rudin;D. Waltz;R. N. Anderson;A. Boulanger.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)
This Looks Like That: Deep Learning for Interpretable Image Recognition
Chaofan Chen;Oscar Li;Daniel Tao;Alina Barnett.
neural information processing systems (2019)
The Big Data Newsvendor: Practical Insights from Machine Learning
Gah-Yi Ban;Cynthia Rudin.
Operations Research (2019)
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
Been Kim;Cynthia Rudin;Julie A Shah.
neural information processing systems (2014)
Falling Rule Lists
Fulton Wang;Cynthia Rudin.
international conference on artificial intelligence and statistics (2015)
Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions
Oscar Li;Hao Liu;Chaofan Chen;Cynthia Rudin.
national conference on artificial intelligence (2018)
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