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Interpretable machine learning has become a strong competitor for traditional black-box models.
Knowledge-based artificial neural networks
Geoffrey G Towell and Jude W Shavlik · 1994
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A desicion-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire · 1995
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The effect of exposure to violence on young children
Joy D Osofsky · 1995
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Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid
Ron Kohavi · 1996
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Robert Tibshirani · 1996
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Hybrid neural systems: from simple coupling to fully integrated neural networks
Kenneth McGarry, Stefan Wermter, and John MacIntyre · 1999
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Symbolic interpretation of artificial neural networks
Ismail A Taha and Joydeep Ghosh · 1999
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A hybrid approach of neural network and memory-based learning to data mining
Chung-Kwan Shin, Ui Tak Yun, Huy Kang Kim, and Sang Chan Park · 2000
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Classification and regression by randomforest
Andy Liaw, Matthew Wiener, et al · 2002
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Prox-method with rate of convergence o ( 1 / t ) o(1/t) for variational inequalities with lipschitz continuous monotone operators and smooth convex-concave saddle point problems
Arkadi Nemirovski · 2004
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Data mining tools see5 and c5. 0
Ross Quinlan · 2004
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A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts
Zhongsheng Hua and Bin Zhang · 2006
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Rweka: an r interface to weka
K Hornik, A Zeileis, T Hothorn, and C Buchta · 2007
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Smoothing technique and its applications in semidefinite optimization
Yurii Nesterov · 2007
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A fast iterative shrinkage-thresholding algorithm for linear inverse problems
Amir Beck and Marc Teboulle · 2009
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Robust stochastic approximation approach to stochastic programming
Arkadi Nemirovski, Anatoli Juditsky, Guanghui Lan, and Alexander Shapiro · 2009
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Movie reviews and revenues: An experiment in text regression
Mahesh Joshi, Dipanjan Das, Kevin Gimpel, and Noah A Smith · 2010
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Finding a short and accurate decision rule in disjunctive normal form by exhaustive search
Peter R Rijnbeek and Jan A Kors · 2010
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A first-order primal-dual algorithm for convex problems with applications to imaging
Antonin Chambolle and Thomas Pock · 2011
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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A hierarchical model for association rule mining of sequential events: An approach to automated medical symptom prediction
Tyler McCormick, Cynthia Rudin, and David Madigan · 2011
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Slim: Sparse linear methods for top-n recommender systems
Xia Ning and George Karypis · 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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Neural-symbolic learning systems: foundations and applications
Artur S d’Avila Garcez, Krysia B Broda, and Dov M Gabbay · 2012
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Understanding big data: Analytics for enterprise class hadoop and streaming data
Paul C Zikopoulos, Chris Eaton, Dirk DeRoos, Thomas Deutsch, and George Lapis · 2012
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Accelerating stochastic gradient descent using predictive variance reduction
Rie Johnson and Tong Zhang · 2013
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Case-based reasoning
Michael M Richter and Rosina O Weber · 2016
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Supersparse linear integer models for optimized medical scoring systems
Berk Ustun and Cynthia Rudin · 2016
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Learning certifiably optimal rule lists
Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, and Cynthia Rudin · 2017
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Classification and regression trees
Leo Breiman · 2017
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A roadmap for a rigorous science of interpretability
Finale Doshi-Velez and Been Kim · 2017
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Distilling a neural network into a soft decision tree
Nicholas Frosst and Geoffrey Hinton · 2017
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Yurii Nesterov · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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C4. 5: programs for machine learning
J Ross Quinlan · 2014
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Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization
Shai Shalev-Shwartz and Tong Zhang · 2014
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Neural-symbolic learning and reasoning: contributions and challenges
A d’Avila Garcez, Tarek R Besold, Luc De Raedt, Peter Földiak, Pascal Hitzler, Thomas Icard, Kai-Uwe Kühnberger, Luis C Lamb, Risto Miikkulainen, and Daniel L Silver · 2015
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A two-step method to construct credit scoring models with data mining techniques
Hian Chye Koh, Wei Chin Tan, and Chwee Peng Goh · 2015
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An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization
Qihang Lin and Lin Xiao · 2015
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Interpretable & explorable approximations of black box models
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, and Jure Leskovec · 2017
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Right for the right reasons: Training differentiable models by constraining their explanations
Andrew Slavin Ross, Michael C Hughes, and Finale Doshi-Velez · 2017
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Detecting statistical interactions from neural network weights
Michael Tsang, Dehua Cheng, and Yan Liu · 2017
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A bayesian framework for learning rule set for interpretable classification
Tong Wang, Cynthia Rudin, F Doshi, Yimin Liu, Erica Klampfl, and Perry MacNeille · 2017
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Scalable bayesian rule lists
Hongyu Yang, Cynthia Rudin, and Margo Seltzer · 2017
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Interpretable classification models for recidivism prediction
Jiaming Zeng, Berk Ustun, and Cynthia Rudin · 2017
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Stochastic primal-dual coordinate method for regularized empirical risk minimization
Yuchen Zhang and Lin Xiao · 2017
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This looks like that: deep learning for interpretable image recognition
Chaofan Chen, Oscar Li, Alina Barnett, Jonathan Su, and Cynthia Rudin · 2018
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International data-sharing norms: from the oecd to the general data protection regulation (gdpr)
Mark Phillips · 2018
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Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
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Please stop explaining black box models for high stakes decisions
Cynthia Rudin · 2018
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Multi-value rule sets for interpretable classification with feature-efficient representations
Tong Wang · 2018
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Interpretable convolutional neural networks
Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu · 2018
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Faithful and customizable explanations of black box models
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, and Jure Leskovec · 2019
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