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Machine learning models that first learn a representation of a domain in terms of human-understandable concepts, then use it to make predictions, have been proposed to facilitate interpretation and interaction with models trained on high-dimensional data.
Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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Combining active learning and semi-supervised learning using gaussian fields and harmonic functions
Xiaojin Zhu, John Lafferty, and Zoubin Ghahramani · 2003
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Active learning with feedback on features and instances
Hema Raghavan, Omid Madani, and Rosie Jones · 2006
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Learning from labeled features using generalized expectation criteria
Gregory Druck, Gideon Mann, and Andrew McCallum · 2008
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Overview based example selection in end user interactive concept learning
Saleema Amershi, James Fogarty, Ashish Kapoor, and Desney Tan · 2009
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Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record
Marylyn D. Ritchie, Joshua C. Denny, Dana C. Crawford, Andrea H. Ramirez, Justin B. Weiner, Jill M. Pulley, Melissa A. Basford, Kristin Brown-Gentry, Jeffrey R. Balser, Daniel R. Masys, Jonathan L. Haines, and Dan M. Roden · 2010
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Interactively building a discriminative vocabulary of nameable attributes
D. Parikh and K. Grauman · 2011
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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A systematic review of validated methods for identifying depression using administrative data
Lisa Townsend, James T Walkup, Stephen Crystal, and Mark Olfson · 2012
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Collinearity: a review of methods to deal with it and a simulation study evaluating their performance
Carsten F. Dormann, Jane Elith, Sven Bacher, Carsten Buchmann, Gudrun Carl, Gabriel Carré, Jaime R. García Marquéz, Bernd Gruber, Bruno Lafourcade, Pedro J. Leitão, Tamara Münkemüller, Colin McClean, Patrick E. Osborne, Björn Reineking, Boris Schröder, Andrew K. Skidmore, Damaris Zurell, and Sven Lautenbach · 2013
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Interactive topic modeling
Yuening Hu, Jordan Boyd-Graber, Brianna Satinoff, and Alison Smith · 2014
Cited alongside, same era.
Validation of electronic health record phenotyping of bipolar disorder cases and controls
Victor M. Castro, Jessica Minnier, Shawn N. Murphy, Isaac Kohane, Susanne E. Churchill, Vivian Gainer, Tianxi Cai, Alison G. Hoffnagle, Yael Dai, Stefanie Block, Sydney R. Weill, Mireya Nadal-Vicens, Alisha R. Pollastri, J. Niels Rosenquist, Sergey Goryachev, Dost Ongur, Pamela Sklar, Roy H. Perlis, Jordan W. Smoller, , Jordan W. Smoller, Roy H. Perlis, Phil Hyoun Lee, Victor M. Castro, Alison G. Hoffnagle, Pamela Sklar, Eli A. Stahl, Shaun M. Purcell, Douglas M. Ruderfer, Alexander W. Charney, Panos Roussos, Carlos Pato, Michele Pato, Helen Medeiros, Janet Sobel, Nick Craddock, Ian Jones, Liz Forty, Arianna DiFlorio, Elaine Green, Lisa Jones, Katherine Dunjewski, Mikael Landén, Christina Hultman, Anders Juréus, Sarah Bergen, Oscar Svantesson, Steven McCarroll, Jennifer Moran, Jordan W. Smoller, Kimberly Chambert, and Richard A. Belliveau · 2015
Cited alongside, same era.
Flock: Hybrid crowd-machine learning classifiers
Justin Cheng and Michael S. Bernstein · 2015
Cited alongside, same era.
Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model
Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, and David Madigan · 2015
Cited alongside, same era.
Learning disentangled representations with semi-supervised deep generative models
Siddharth Narayanaswamy, T. Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah Goodman, Pushmeet Kohli, Frank Wood, and Philip Torr · 2017
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Interpretable latent spaces for learning from demonstration
Yordan Hristov, Alex Lascarides, and Subramanian Ramamoorthy · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory sayres · 2018
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Labeled anchors and a scalable, transparent, and interactive classifier
Jeffrey Lund, Stephen Cowley, Wilson Fearn, Emily Hales, and Kevin Seppi · 2018
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Manipulating and measuring model interpretability
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, and Hanna M. Wallach · 2018
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Interpretable decision sets: A joint framework for description and prediction
Himabindu Lakkaraju, Stephen H. Bach, and Jure Leskovec · 2016
Cited alongside, same era.
The mythos of model interpretability
Zachary Chase Lipton · 2016
Cited alongside, same era.
Supersparse linear integer models for optimized medical scoring systems
Berk Ustun and Cynthia Rudin · 2016
Cited alongside, same era.
Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav), 2017
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres · 2017
Cited alongside, same era.
Tandem anchoring: a multiword anchor approach for interactive topic modeling
Jeffrey Lund, Connor Cook, Kevin Seppi, and Jordan Boyd-Graber · 2017
Cited alongside, same era.
Later among the works it cites.
Adaflock: Adaptive feature discovery for human-in-the-loop predictive modeling
Ryusuke Takahama, Yukino Baba, Nobuyuki Shimizu, Sumio Fujita, and Hisashi Kashima · 2018
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Human evaluation of models built for interpretability
Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Sam Gershman, and Finale Doshi-Velez · 2019
Later among the works it cites.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Concept bottleneck models
Pang Wei Koh, Thao Nguyen, Stephen Tang Yew Siang, Mussmann, Pierson Emma, Been Kim, and Percy Liang · 2020
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