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Explaining the predictions made by complex machine learning models helps users to understand and accept the predicted outputs with confidence.
An Evaluation of the Human-Interpretability of Explanation
Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Sam Gershman, and Finale Doshi-Velez · 1902
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How Do People Use Analogues to Make Decisions?
Gary A Klein and Roberta Calderwood · 1988
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Strategies of Decision Making
Gary A Klein · 1989
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This Reminds Me of the Time When…: Expectation Failures in Reminding and Explanation
Stephen J Read and Ian L Cesa · 1991
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Gradient-based Learning Applied to Document Recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Case-Based Explanation of Non-Case-Based Learning Methods
Rich Caruana, Hooshang Kangarloo, John David N. Dionisio, Usha Sinha, and David Johnson · 1999
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Learning Question Classifiers
Xin Li and Dan Roth · 2002
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Bernhard Schölkopf, Alexander J Smola, and Francis Bach · 2002
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An Evaluation of the Usefulness of Case-Based Explanation
Pádraig Cunningham, Dónal Doyle, and John Loughrey · 2003
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Working Set Selection Using Second Order Information for Training Support Vector Machines
Rong-En Fan, Pai-Hsuen Chen, and Chih-Jen Lin · 2005
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
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Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky · 2009
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Large-Scale Image Retrieval With Compressed Fisher Vectors
Florent Perronnin, Yan Liu, Jorge Sánchez, and Hervé Poirier · 2010
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Prototype Selection for Interpretable Classification
Jacob Bien and Robert Tibshirani · 2011
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A Comparison of SVM Kernel Functions for Breast Cancer Detection
Muhammad Hussain, Summrina Kanwal Wajid, Ali Elzaart, and Mohammed Berbar · 2011
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Distributed Representations of Words and Phrases and their Compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
Been Kim, Cynthia Rudin, and Julie A Shah · 2014
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Algorithm aversion: People erroneously avoid algorithms after seeing them err
Berkeley J Dietvorst, Joseph P Simmons, and Cade Massey · 2015
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Falling rule lists
Fulton Wang and Cynthia Rudin · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
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The Distance Function Effect on k-Nearest Neighbor Classification for Medical Datasets
Li Yu Hu, Min Wei Huang, Shih Wen Ke, and Chih Fong Tsai · 2016
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Examples Are Not Enough, Learn to Criticize! Criticism for Interpretability
Been Kim, Rajiv Khanna, and Oluwasanmi O Koyejo · 2016
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Rationalizing Neural Predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola · 2016
On the robustness of interpretability methods
David Alvarez-Melis and Tommi S Jaakkola · 2018
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A Survey of Methods for Explaining Black Box Models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi · 2018
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Selection of Kernel Function for Least Squares Support Vector Machines in Downburst Wind Speed Forecasting
Zhou Li and Chunxiang Li · 2018
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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Leland McInnes, John Healy, and James Melville · 2018
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Neural Nearest Neighbors Networks
Tobias Plötz and Stefan Roth · 2018
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MobileNetV2: Inverted Residuals and Linear Bottlenecks
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“Why Should I Trust You” Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Learning certifiably optimal rule lists for categorical data
Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, and Cynthia Rudin · 2017
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A Simple but Tough-to-Beat Baseline for Sentence Embeddings
Sanjeev Arora, Yingyu Liang, and Tengyu Ma · 2017
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Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang Chieh Chen · 2018
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Counterfactual explanations without opening the black box: Automated decisions and the GDPR
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2018
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Representer Point Selection for Explaining Deep Neural Networks
Chih-Kuan Yeh, Joon Kim, Ian En-Hsu Yen, and Pradeep K Ravikumar · 2018
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Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review
Haneen Arafat Abu Alfeilat, Ahmad B.A. Hassanat, Omar Lasassmeh, Ahmad S. Tarawneh, Mahmoud Bashir Alhasanat, Hamzeh S. Eyal Salman, and V.B. Surya Prasath · 2019
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Input Similarity from the Neural Network Perspective
Guillaume Charpiat, Nicolas Girard, Loris Felardos, and Yuliya Tarabalka · 2019
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This Looks Like That: Deep Learning for Interpretable Image Recognition
Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and Jonathan K Su · 2019
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Interpreting Black Box Predictions using Fisher Kernels
Rajiv Khanna, Been Kim, Joydeep Ghosh, and Sanmi Koyejo · 2019
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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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Do Human Rationales Improve Machine Explanations?
Julia Strout, Ye Zhang, and Raymond J. Mooney · 2019
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RelatIF: Identifying Explanatory Training Samples via Relative Influence
Elnaz Barshan, Marc-Etienne Brunet, and Gintare Karolina Dziugaite · 2020
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Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?
Alon Jacovi and Yoav Goldberg · 2020
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Interpretable Machine Learning
Christoph Molnar · 2020
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