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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
XDeep: An Interpretation Tool for Deep Neural Networks Fan Yang Zijian Zhang Haofan Wang Yuening Li Helen Zhou XAI HAI 37 2 0 04 Nov 2019
Explaining black box decisions by Shapley cohort refinement Masayoshi Mase Art B. Owen Benjamin B. Seiler 67 52 0 01 Nov 2019
EnergyStar++: Towards more accurate and explanatory building energy benchmarking P. Arjunan K. Poolla Clayton Miller 34 116 0 30 Oct 2019
Weight of Evidence as a Basis for Human-Oriented Explanations David Alvarez-Melis Hal Daumé Jennifer Wortman Vaughan Hanna M. Wallach XAI FAtt 91 20 0 29 Oct 2019
Feature relevance quantification in explainable AI: A causal problem Dominik Janzing Lenon Minorics Patrick Blobaum FAtt CML 109 286 0 29 Oct 2019
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control Mo Yu Shiyu Chang Yang Zhang Tommi Jaakkola 143 146 0 29 Oct 2019
bLIMEy: Surrogate Prediction Explanations Beyond LIME Kacper Sokol Alexander Hepburn Raúl Santos-Rodríguez Peter A. Flach FAtt 145 38 0 29 Oct 2019
A Game Theoretic Approach to Class-wise Selective Rationalization Shiyu Chang Yang Zhang Mo Yu Tommi Jaakkola 66 62 0 28 Oct 2019
CXPlain: Causal Explanations for Model Interpretation under Uncertainty Patrick Schwab W. Karlen FAtt CML 143 211 0 27 Oct 2019
Data Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Network Ibrahim Saad Ali Mamdouh Farouk Mohamed Y. B. Mahdy GAN MedIm 49 122 0 25 Oct 2019
Seeing What a GAN Cannot Generate David Bau Jun-Yan Zhu Jonas Wulff William S. Peebles Hendrik Strobelt Bolei Zhou Antonio Torralba GAN 113 310 0 24 Oct 2019
Predicting In-game Actions from Interviews of NBA Players Nadav Oved Amir Feder Roi Reichart 79 2 0 24 Oct 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 267 6,386 0 22 Oct 2019
Contextual Prediction Difference Analysis for Explaining Individual Image Classifications Jindong Gu Volker Tresp FAtt 51 8 0 21 Oct 2019
Many Faces of Feature Importance: Comparing Built-in and Post-hoc Feature Importance in Text Classification Vivian Lai Zheng Jon Cai Chenhao Tan FAtt 60 19 0 18 Oct 2019
Personalized Treatment for Coronary Artery Disease Patients: A Machine Learning Approach Dimitris Bertsimas Agni Orfanoudaki R. Weiner 65 41 0 18 Oct 2019
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan O. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 331 307 0 17 Oct 2019
Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms Z. Q. Lin M. Shafiee S. Bochkarev Michael St. Jules Xiao Yu Wang A. Wong FAtt 83 81 0 16 Oct 2019
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability Christopher Frye C. Rowat Ilya Feige 105 184 0 14 Oct 2019
Measuring Unfairness through Game-Theoretic Interpretability Juliana Cesaro Fabio Gagliardi Cozman FAtt 75 13 0 12 Oct 2019
NLS: an accurate and yet easy-to-interpret regression method Victor Coscrato M. Inácio T. Botari Rafael Izbicki FAtt 55 4 0 11 Oct 2019
Explaining image classifiers by removing input features using generative models Chirag Agarwal Anh Totti Nguyen FAtt 100 15 0 09 Oct 2019
Who's responsible? Jointly quantifying the contribution of the learning algorithm and training data G. Yona Amirata Ghorbani James Zou TDI 61 13 0 09 Oct 2019
Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations Oana-Maria Camburu Brendan Shillingford Pasquale Minervini Thomas Lukasiewicz Phil Blunsom AAML GAN 114 97 0 07 Oct 2019
Testing and verification of neural-network-based safety-critical control software: A systematic literature review Jin Zhang Jingyue Li 85 48 0 05 Oct 2019
Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods Oana-Maria Camburu Eleonora Giunchiglia Jakob N. Foerster Thomas Lukasiewicz Phil Blunsom FAtt AAML 115 61 0 04 Oct 2019
The Bouncer Problem: Challenges to Remote Explainability Erwan Le Merrer Gilles Tredan 66 8 0 03 Oct 2019
Silas: High Performance, Explainable and Verifiable Machine Learning Hadrien Bride Zhe Hou Jie Dong J. Dong Seyedali Mirjalili 66 7 0 03 Oct 2019
Synthesizing Action Sequences for Modifying Model Decisions Goutham Ramakrishnan Yun Chan Lee Aws Albarghouthi 121 33 0 30 Sep 2019
MonoNet: Towards Interpretable Models by Learning Monotonic Features An-phi Nguyen María Rodríguez Martínez FAtt 60 13 0 30 Sep 2019
Interpretations are useful: penalizing explanations to align neural networks with prior knowledge Laura Rieger Chandan Singh W. James Murdoch Bin Yu FAtt 111 215 0 30 Sep 2019
Decision Explanation and Feature Importance for Invertible Networks Juntang Zhuang Nicha Dvornek Xiaoxiao Li Junlin Yang James S. Duncan AAML FAtt 61 5 0 30 Sep 2019
Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data P. Widera P. Welsing C. Ladel J. Loughlin F. Lafeber F. Dop J. Larkin H. Weinans A. Mobasheri J. Bacardit 80 54 0 30 Sep 2019
Interpreting Undesirable Pixels for Image Classification on Black-Box Models Sin-Han Kang Hong G Jung Seong-Whan Lee FAtt 50 3 0 27 Sep 2019
Towards Explainable Artificial Intelligence Wojciech Samek K. Müller XAI 93 449 0 26 Sep 2019
Data Valuation using Reinforcement Learning Jinsung Yoon Sercan O. Arik Tomas Pfister TDI 93 183 0 25 Sep 2019
Explaining and Interpreting LSTMs L. Arras Jose A. Arjona-Medina Michael Widrich G. Montavon Michael Gillhofer K. Müller Sepp Hochreiter Wojciech Samek FAtt AI4TS 76 79 0 25 Sep 2019
Model-Agnostic Linear Competitors -- When Interpretable Models Compete and Collaborate with Black-Box Models Hassan Rafique Tong Wang Qihang Lin 51 4 0 23 Sep 2019
Towards Interpreting Recurrent Neural Networks through Probabilistic Abstraction Guoliang Dong Jingyi Wang Jun Sun Yang Zhang Xinyu Wang Ting Dai J. Dong Xingen Wang FaML 49 3 0 22 Sep 2019
FACE: Feasible and Actionable Counterfactual Explanations Rafael Poyiadzi Kacper Sokol Raúl Santos-Rodríguez T. D. Bie Peter A. Flach 84 372 0 20 Sep 2019
Representation Learning for Electronic Health Records W. Weng Peter Szolovits 81 19 0 19 Sep 2019
InterpretML: A Unified Framework for Machine Learning Interpretability Harsha Nori Samuel Jenkins Paul Koch R. Caruana AI4CE 171 490 0 19 Sep 2019
Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment Jaap Jumelet Willem H. Zuidema Dieuwke Hupkes LRM 73 37 0 19 Sep 2019
The Explanation Game: Explaining Machine Learning Models Using Shapley Values Luke Merrick Ankur Taly FAtt TDI 59 33 0 17 Sep 2019
Measure Contribution of Participants in Federated Learning Guan Wang Charlie Xiaoqian Dang Ziye Zhou FedML 111 200 0 17 Sep 2019
Towards a Rigorous Evaluation of XAI Methods on Time Series U. Schlegel Hiba Arnout Mennatallah El-Assady Daniela Oelke Daniel A. Keim XAI AI4TS 117 174 0 16 Sep 2019
Shapley Interpretation and Activation in Neural Networks Yadong Li Xin Cui TDI FAtt LLMSV 50 3 0 13 Sep 2019
FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency Kacper Sokol Raúl Santos-Rodríguez Peter A. Flach 55 37 0 11 Sep 2019
Interpretable Biomanufacturing Process Risk and Sensitivity Analyses for Quality-by-Design and Stability Control Wei Xie Bo Wang Cheng Li D. Xie Jared R. Auclair 22 20 0 10 Sep 2019
NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks Isaac Ahern Adam Noack Luis Guzman-Nateras Dejing Dou Boyang Albert Li Jun Huan FAtt 55 40 0 10 Sep 2019