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Recently, a method [7] was proposed to generate contrastive explanations for differentiable models such as deep neural networks, where one has complete access to the model.
Information Measures, Information and its Description in Science and Engineering
C. Arndt · 2004
Earlier work this paper cites.
Regularization and variable selection via the elastic net
Hui Zou and Trevor Hastie · 2005
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
Optimal rates for zero-order convex optimization: The power of two function evaluations
John C Duchi, Michael I Jordan, Martin J Wainwright, and Andre Wibisono · 2015
Earlier work this paper cites.
Falling rule lists
Fulton Wang and Cynthia Rudin · 2015
Earlier work this paper cites.
Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
Earlier work this paper cites.
Are you visually intelligent? what you don’t see is as important as what you do see
Amy Herman · 2016
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Examples are not enough, learn to criticize! criticism for interpretability
Been Kim, Rajiv Khanna, and Oluwasanmi Koyejo · 2016
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola · 2016
Earlier work this paper cites.
The mythos of model interpretability
Zachary C Lipton · 2016
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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune · 2016
Earlier work this paper cites.
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2016
Earlier work this paper cites.
"why should i trust you?” explaining the predictions of any classifier
Marco Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Interpretable two-level boolean rule learning for classification
Guolong Su, Dennis Wei, Kush Varshney, and Dmitry Malioutov · 2016
Cited alongside, same era.
Interpreting blackbox models via model extraction
Osbert Bastani, Carolyn Kim, and Hamsa Bastani · 2017
Cited alongside, same era.
UCI machine learning repository, 2017
Dua Dheeru and Efi Karra Taniskidou · 2017
Cited alongside, same era.
Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller · 2017
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Unified framework for interpretable methods
Su-In Lee Scott Lundberg · 2017
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Towards robust interpretability with self-explaining neural networks
David Alvarez Melis and Tommi Jaakkola · 2018
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Explanations based on the missing: Towards contrastive explanations with pertinent negatives
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das · 2018
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Improving simple models with confidence profiles
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, and Peder Olsen · 2018
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Explainable machine learning challenge
FICO · 2018
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Tip: Typifying the interpretability of procedures
Amit Dhurandhar, Vijay Iyengar, Ronny Luss, and Karthikeyan Shanmugam · 2017
Cited alongside, same era.
A kaggle master explains gradient boosting
Ben Gorman · 2017
Cited alongside, same era.
Explainable artificial intelligence (xai)
David Gunning · 2017
Cited alongside, same era.
Protodash: Fast interpretable prototype selection
Karthik Gurumoorthy, Amit Dhurandhar, and Guillermo Cecchi · 2017
Cited alongside, same era.
Supervised item response models for informative prediction
Tsuyoshi Idé and Amit Dhurandhar · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2017
Cited alongside, same era.
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Learning how to explain neural networks: Patternnet and patternattribution
Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, and Sven Dähne · 2018
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Zeroth-order online alternating direction method of multipliers: Convergence analysis and applications
Sijia Liu, Jie Chen, Pin-Yu Chen, and Alfred O Hero · 2018
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Zeroth-order stochastic variance reduction for nonconvex optimization
Sijia Liu, Bhavya Kailkhura, Pin-Yu Chen, Paishun Ting, Shiyu Chang, and Lisa Amini · 2018
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Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
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Kaggle, 2018
SkyServer · 2018
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Analysis: Article 29 working party guidelines on automated decision making under gdpr
Philip N. Yannella and Odia Kagan · 2018
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Interpreting neural network judgments via minimal, stable, and symbolic corrections
Xin Zhang, Armando Solar-Lezama, and Rishabh Singh · 2018
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