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A Unified Approach to Interpreting Model Predictions

22 May 2017

Papers citing "A Unified Approach to Interpreting Model Predictions"

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Title
Statistical stability indices for LIME: obtaining reliable explanations for Machine Learning models Giorgio Visani Enrico Bagli F. Chesani A. Poluzzi D. Capuzzo FAtt 66 170 0 31 Jan 2020
TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions Benjamin Regler Matthias Scheffler L. Ghiringhelli 74 2 0 30 Jan 2020
One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency Kacper Sokol Peter A. Flach 49 178 0 27 Jan 2020
An interpretable semi-supervised classifier using two different strategies for amended self-labeling Isel Grau Dipankar Sengupta M. Lorenzo A. Nowé SSL 93 4 0 26 Jan 2020
Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience Bhavya Ghai Q. V. Liao Yunfeng Zhang Rachel K. E. Bellamy Klaus Mueller 94 29 0 24 Jan 2020
Visual Summary of Value-level Feature Attribution in Prediction Classes with Recurrent Neural Networks Chuan-Chi Wang Xumeng Wang K. Ma FAtt HAI 35 1 0 23 Jan 2020
Adequate and fair explanations Nicholas M. Asher Soumya Paul Chris Russell 68 9 0 21 Jan 2020
Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach Carlos Fernandez F. Provost Xintian Han CML 74 72 0 21 Jan 2020
An interpretable neural network model through piecewise linear approximation Mengzhuo Guo Qingpeng Zhang Xiuwu Liao D. Zeng MILM FAtt 51 8 0 20 Jan 2020
Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition S. Raschka Benjamin Kaufman AI4CE 81 67 0 17 Jan 2020
Adapting Grad-CAM for Embedding Networks Lei Chen Jianhui Chen Hossein Hajimirsadeghi Greg Mori 73 57 0 17 Jan 2020
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks Q. Huang M. Yamada Yuan Tian Dinesh Singh D. Yin Yi-Ju Chang FAtt 104 361 0 17 Jan 2020
"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans Vivian Lai Han Liu Chenhao Tan 93 143 0 14 Jan 2020
Interpretable feature subset selection: A Shapley value based approach Sandhya Tripathi N. Hemachandra Prashant Trivedi TDI FAtt 73 2 0 12 Jan 2020
Explaining the Explainer: A First Theoretical Analysis of LIME Damien Garreau U. V. Luxburg FAtt 61 183 0 10 Jan 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 94 318 0 08 Jan 2020
Questioning the AI: Informing Design Practices for Explainable AI User Experiences Q. V. Liao D. Gruen Sarah Miller 144 734 0 08 Jan 2020
Gradient Boosting on Decision Trees for Mortality Prediction in Transcatheter Aortic Valve Implantation Marco Mamprin J. Zelis P. Tonino S. Zinger Peter H. N. de With 24 7 0 08 Jan 2020
Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making Yunfeng Zhang Q. V. Liao Rachel K. E. Bellamy 126 688 0 07 Jan 2020
A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data Patrick Schwab W. Karlen 70 26 0 02 Jan 2020
A New Approach for Explainable Multiple Organ Annotation with Few Data Régis Pierrard Jean-Philippe Poli C´eline Hudelot 29 8 0 30 Dec 2019
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution Nikaash Puri Sukriti Verma Piyush B. Gupta Dhruv Kayastha Shripad Deshmukh Balaji Krishnamurthy Sameer Singh FAtt AAML 84 79 0 23 Dec 2019
Regularized Operating Envelope with Interpretability and Implementability Constraints Qiyao Wang Haiyan Wang Chetan Gupta Susumu Serita 14 0 0 21 Dec 2019
Explainability and Adversarial Robustness for RNNs Alexander Hartl Maximilian Bachl J. Fabini Tanja Zseby AAML 59 32 0 20 Dec 2019
When Explanations Lie: Why Many Modified BP Attributions Fail Leon Sixt Maximilian Granz Tim Landgraf BDL FAtt XAI 96 132 0 20 Dec 2019
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting Bryan Lim Sercan O. Arik Nicolas Loeff Tomas Pfister AI4TS 147 1,500 0 19 Dec 2019
Clusters in Explanation Space: Inferring disease subtypes from model explanations Marc-Andre Schulz M. Chapman-Rounds Manisha Verma D. Bzdok K. Georgatzis 22 2 0 18 Dec 2019
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation Tianhong Dai Kai Arulkumaran Tamara Gerbert Samyakh Tukra Feryal M. P. Behbahani Anil Anthony Bharath 87 28 0 18 Dec 2019
On the Explanation of Machine Learning Predictions in Clinical Gait Analysis D. Slijepcevic Fabian Horst Sebastian Lapuschkin Anna-Maria Raberger Matthias Zeppelzauer Wojciech Samek C. Breiteneder W. Schöllhorn B. Horsak 115 51 0 16 Dec 2019
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group) Zied Bouraoui Antoine Cornuéjols Thierry Denoeux Sebastien Destercke Didier Dubois ... Jérôme Mengin H. Prade Steven Schockaert M. Serrurier Christel Vrain 128 14 0 13 Dec 2019
An Empirical Study on the Relation between Network Interpretability and Adversarial Robustness Adam Noack Isaac Ahern Dejing Dou Boyang Albert Li OOD AAML 158 10 0 07 Dec 2019
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers Divyat Mahajan Chenhao Tan Amit Sharma OOD CML 147 207 0 06 Dec 2019
Counterfactual Explanation Algorithms for Behavioral and Textual Data Yanou Ramon David Martens F. Provost Theodoros Evgeniou FAtt 126 88 0 04 Dec 2019
Explainable artificial intelligence model to predict acute critical illness from electronic health records S. Lauritsen Mads Kristensen Mathias Vassard Olsen Morten Skaarup Larsen K. M. Lauritsen Marianne Johansson Jørgensen Jeppe Lange B. Thiesson 65 305 0 03 Dec 2019
Automated Dependence Plots David I. Inouye Liu Leqi Joon Sik Kim Bryon Aragam Pradeep Ravikumar 73 1 0 02 Dec 2019
Learning Word Ratings for Empathy and Distress from Document-Level User Responses João Sedoc Sven Buechel Yehonathan Nachmany Anneke Buffone L. Ungar 40 29 0 02 Dec 2019
EMAP: Explanation by Minimal Adversarial Perturbation M. Chapman-Rounds Marc-Andre Schulz Erik Pazos K. Georgatzis AAML FAtt 47 6 0 02 Dec 2019
ACE -- An Anomaly Contribution Explainer for Cyber-Security Applications Xiao Zhang Manish Marwah I-Ta Lee M. Arlitt Dan Goldwasser 42 14 0 01 Dec 2019
A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors Edward J. Kim D. Gopinath C. Păsăreanu Sanjit A. Seshia 47 26 0 01 Dec 2019
Sanity Checks for Saliency Metrics Richard J. Tomsett Daniel Harborne Supriyo Chakraborty Prudhvi K. Gurram Alun D. Preece XAI 125 170 0 29 Nov 2019
AR-Net: A simple Auto-Regressive Neural Network for time-series Oskar Triebe N. Laptev Ram Rajagopal AI4TS AI4CE 102 59 0 27 Nov 2019
The relationship between trust in AI and trustworthy machine learning technologies Ehsan Toreini Mhairi Aitken Kovila P. L. Coopamootoo Karen Elliott Carlos Vladimiro Gonzalez Zelaya Aad van Moorsel FaML 87 262 0 27 Nov 2019
Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey Vanessa Buhrmester David Münch Michael Arens MLAU FaML XAI AAML 120 369 0 27 Nov 2019
Explaining Models by Propagating Shapley Values of Local Components Hugh Chen Scott M. Lundberg Su-In Lee FAtt FedML 85 110 0 27 Nov 2019
Improving Feature Attribution through Input-specific Network Pruning Ashkan Khakzar Soroosh Baselizadeh Saurabh Khanduja Christian Rupprecht S. T. Kim Nassir Navab FAtt 58 11 0 25 Nov 2019
A psychophysics approach for quantitative comparison of interpretable computer vision models F. Biessmann D. Refiano 60 5 0 24 Nov 2019
Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response Sheikh Rabiul Islam W. Eberle S. Ghafoor Ambareen Siraj Mike Rogers 69 39 0 22 Nov 2019
LionForests: Local Interpretation of Random Forests Ioannis Mollas Nick Bassiliades I. Vlahavas Grigorios Tsoumakas 90 12 0 20 Nov 2019
"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations Himabindu Lakkaraju Osbert Bastani 90 258 0 15 Nov 2019
ERASER: A Benchmark to Evaluate Rationalized NLP Models Jay DeYoung Sarthak Jain Nazneen Rajani Eric P. Lehman Caiming Xiong R. Socher Byron C. Wallace 163 640 0 08 Nov 2019