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We provide a novel notion of what it means to be interpretable, looking past the usual association with human understanding.
The magical number seven, plus or minus two: Some limits on our capacity for processing information
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Storage of 7 plus/minus 2 short-term memories in oscillatory subcycles
John E Lisman and Marco AP Idiart · 1995
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Decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E. Schapire · 1997
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Reinforcement Learning: An Introduction
R. Sutton and A. Barto · 1998
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Statistical Learning Theory
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Minimization of boolean complexity in human concept learning
Jacob Feldman · 2000
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Causality: Models, Reasoning, and Inference
J. Pearl · 2000
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Transforming classifier scores into accurate multiclass probability estimates
Bianca Zadrozny and Charles Elkan · 2002
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Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Human rademacher complexity
Xiaojin Zhu, Bryan R Gibson, and Timothy T Rogers · 2009
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An in-depth assessment of a diagnosis-based risk adjustment model based on national health insurance claimsl the application of the johns hopkins adjusted clinical group case-mix system in taiwan
Hsien-Yen Chang and Jonathan P Weiner · 2010
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Prototype Selection for Interpretable Classification
J. Bien and R. Tibshirani · 2011
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An Overview of Recent Progress in the Study of Distributed Multi-agent Coordination
Yongcan Cao, Wenwu Yu, Wei Ren, and Guanrong Chen · 2012
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A survey on automated dynamic malware-analysis techniques and tools
Manuel Egele, Theodoor Scholte, Engin Kirda, and Christopher Kruegel · 2012
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Data Clustering: Algorithms and Applications
C. Aggarwal and C. Reddy · 2013
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Do deep nets really need to be deep?
Lei Jimmy Ba and Rich Caurana · 2013
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Introduction to the Theory of Computation 3rd
M. Sipser · 2013
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Efficient and accurate methods for updating generalized linear models with multiple feature additions
Amit Dhurandhar and Marek Petrik · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad · 2015
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Distilling the knowledge in a neural network
Jeff Dean Geoffrey Hinton, Oriol Vinyals · 2015
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Deep residual learning for image recognition
Shaoqing Ren Jian Sun Kaiming He, Xiangyu Zhang · 2015
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2016
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Interpretable two-level boolean rule learning for classification
Guolong Su, Dennis Wei, Kush Varshney, and Dmitry Malioutov · 2016
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Engineering safety in machine learning
Kush Varshney · 2016
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Interpreting blackbox models via model extraction
Osbert Bastani, Carolyn Kim, and Hamsa Bastani · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2015
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Falling rule lists
Fulton Wang and Cynthia Rudin · 2015
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Algorithms for Fitting the Constrained Lasso
B. R. Gaines · 2016
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Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
Cited alongside, same era.
Examples are not enough, learn to criticize! criticism for interpretability
Been Kim, Rajiv Khanna, and Oluwasanmi Koyejo · 2016
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Interpretable decision sets: A joint framework for description and prediction
Himabindu Lakkaraju, Stephen H. Bach, and Jure Leskovec · 2016
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola · 2016
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Learning with changing features
Amit Dhurandhar, Steve Hanneke, and Liu Yang · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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Accountability of AI under the law: The role of explanation
Finale Doshi-Velez, Mason Kortz, Ryan Budish, Chris Bavitz, Sam Gershman, David O’Brien, Stuart Schieber, James Waldo, David Weinberger, and Alexandra Wood · 2017
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Keras mnist mlp implementation
Matsuyamax Fchollet and Kemaswill · 2017
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Keras mnist cnn implementations
Smerity Fchollet, Matsuyamax and Kemaswill · 2017
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Protodash: Fast interpretable prototype selection
Karthik Gurumoorthy, Amit Dhurandhar, and Guillermo Cecchi · 2017
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Supervised item response models for informative prediction
Tsuyoshi Idé and Amit Dhurandhar · 2017
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Predicting human olfactory perception from chemical features of odor molecules
Andreas Keller, Richard C. Gerkin, Yuanfang Guan, Amit Dhurandhar, Gabor Turu, Bence Szalai, Joel D. Mainland, Yusuke Ihara, Chung Wen Yu, Russ Wolfinger, Celine Vens, Leander Schietgat, Kurt De Grave, Raquel Norel, Gustavo Stolovitzky, Guillermo A. Cecchi, Leslie B. Vosshall, and Pablo Meyer · 2017
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Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2017
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Unified framework for interpretable methods
Su-In Lee Scott Lundberg · 2017
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Explainable machine learning challenge
FICO · 2018
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Predicting natural language descriptions of smells
Elkin Gutierrez, Amit Dhurandhar, Andreas Keller, Guillermo Cecchi, and Pablo Meyer · 2018
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An Overview of Machine Teaching
Xiaojin Zhu, Adish Singla, Sandra Zilles, and Anna N. Rafferty · 2018
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