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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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From predictions to prescriptions: A data-driven response to COVID-19 Dimitris Bertsimas L. Boussioux Ryan Cory-Wright A. Delarue V. Digalakis ... Bartolomeo Stellato H. Tazi Bouardi Kimberly Villalobos Carballo H. Wiberg C. Zeng OOD 62 73 0 30 Jun 2020
True to the Model or True to the Data? Hugh Chen Joseph D. Janizek Scott M. Lundberg Su-In Lee TDI FAtt 175 168 0 29 Jun 2020
Interpreting and Disentangling Feature Components of Various Complexity from DNNs Jie Ren Mingjie Li Zexu Liu Quanshi Zhang CoGe 77 18 0 29 Jun 2020
Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors Ruihan Zhang Prashan Madumal Tim Miller Krista A. Ehinger Benjamin I. P. Rubinstein FAtt 103 106 0 27 Jun 2020
Causality Learning: A New Perspective for Interpretable Machine Learning Guandong Xu Tri Dung Duong Q. Li S. Liu Xianzhi Wang XAI OOD CML 62 52 0 27 Jun 2020
Counterfactual explanation of machine learning survival models M. Kovalev Lev V. Utkin CML OffRL 119 19 0 26 Jun 2020
SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization Haofan Wang Rakshit Naidu J. Michael Soumya Snigdha Kundu FAtt 126 80 0 25 Jun 2020
Generative causal explanations of black-box classifiers Matthew R. O’Shaughnessy Gregory H. Canal Marissa Connor Mark A. Davenport Christopher Rozell CML 103 73 0 24 Jun 2020
Investor Emotions and Earnings Announcements Domonkos F. Vamossy AIFin 60 26 0 24 Jun 2020
Self-supervised edge features for improved Graph Neural Network training Arijit Sehanobish N. Ravindra David van Dijk SSL 87 6 0 23 Jun 2020
On Counterfactual Explanations under Predictive Multiplicity Martin Pawelczyk Klaus Broelemann Gjergji Kasneci 144 87 0 23 Jun 2020
Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks Arijit Sehanobish N. Ravindra David van Dijk SSL 45 16 0 23 Jun 2020
Lumos: A Library for Diagnosing Metric Regressions in Web-Scale Applications Jamie Pool Ebrahim Beyrami Vishak Gopal A. Aazami J. Gupchup Jeff Rowland Binlong Li Pritesh Kanani Ross Cutler J. Gehrke 29 11 0 23 Jun 2020
Improving Workflow Integration with xPath: Design and Evaluation of a Human-AI Diagnosis System in Pathology H. Gu Yuan Liang Yifan Xu Christopher Kazu Williams S. Magaki ... Wenzhong Yan X. R. Zhang Yang Li Mohammad Haeri Xiang Ánthony' Chen 91 31 0 23 Jun 2020
Improving LIME Robustness with Smarter Locality Sampling Sean Saito Eugene Chua Nicholas Capel Rocco Hu FAtt AAML 65 22 0 22 Jun 2020
How does this interaction affect me? Interpretable attribution for feature interactions Michael Tsang Sirisha Rambhatla Yan Liu FAtt 75 88 0 19 Jun 2020
Image classification in frequency domain with 2SReLU: a second harmonics superposition activation function Thomio Watanabe D. Wolf 74 23 0 18 Jun 2020
LimeOut: An Ensemble Approach To Improve Process Fairness Vaishnavi Bhargava Miguel Couceiro A. Napoli FaML 66 21 0 17 Jun 2020
Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks Federico Baldassarre Kevin Smith Josephine Sullivan Hossein Azizpour 95 25 0 16 Jun 2020
Efficient nonparametric statistical inference on population feature importance using Shapley values B. Williamson Jean Feng FAtt 70 72 0 16 Jun 2020
Model Explanations with Differential Privacy Neel Patel Reza Shokri Yair Zick SILM FedML 143 32 0 16 Jun 2020
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks Kirill Bykov Marina M.-C. Höhne Klaus-Robert Muller Shinichi Nakajima Marius Kloft UQCV FAtt 112 31 0 16 Jun 2020
High Dimensional Model Explanations: an Axiomatic Approach Neel Patel Martin Strobel Yair Zick FAtt 53 20 0 16 Jun 2020
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey Arun Das P. Rad XAI 188 608 0 16 Jun 2020
Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach A. Moawad E. Islam Namdoo Kim R. Vijayagopal A. Rousseau Wei Biao Wu 80 5 0 15 Jun 2020
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping Cher Bass Mariana da Silva Carole Sudre Petru-Daniel Tudosiu Stephen M. Smith E. C. Robinson FAtt 58 40 0 15 Jun 2020
Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances Sijie He Xinyan Li T. DelSole Pradeep Ravikumar A. Banerjee AI4Cl 129 44 0 14 Jun 2020
Hindsight Logging for Model Training Rolando Garcia Eric Liu Vikram Sreekanti Bobby Yan Anusha Dandamudi Joseph E. Gonzalez J. M. Hellerstein Koushik Sen VLM 77 10 0 12 Jun 2020
Generalized SHAP: Generating multiple types of explanations in machine learning Dillon Bowen L. Ungar FAtt 79 43 0 12 Jun 2020
Getting a CLUE: A Method for Explaining Uncertainty Estimates Javier Antorán Umang Bhatt T. Adel Adrian Weller José Miguel Hernández-Lobato UQCV BDL 110 117 0 11 Jun 2020
How Interpretable and Trustworthy are GAMs? C. Chang S. Tan Benjamin J. Lengerich Anna Goldenberg R. Caruana FAtt 127 80 0 11 Jun 2020
OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithms Giorgio Visani Enrico Bagli F. Chesani FAtt 78 60 0 10 Jun 2020
Adversarial Infidelity Learning for Model Interpretation Jian Liang Bing Bai Yuren Cao Kun Bai Fei Wang AAML 103 18 0 09 Jun 2020
The Penalty Imposed by Ablated Data Augmentation Frederick Liu A. Najmi Mukund Sundararajan 56 6 0 08 Jun 2020
Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approach Christoph Molnar Gunnar Konig B. Bischl Giuseppe Casalicchio 90 84 0 08 Jun 2020
X-SHAP: towards multiplicative explainability of Machine Learning Luisa Bouneder Yannick Léo A. Lachapelle FAtt 38 9 0 08 Jun 2020
Propositionalization and Embeddings: Two Sides of the Same Coin Nada Lavrac Blaž Škrlj Marko Robnik-Šikonja 130 26 0 08 Jun 2020
Black-box Explanation of Object Detectors via Saliency Maps Vitali Petsiuk R. Jain Varun Manjunatha Vlad I. Morariu Ashutosh Mehra Vicente Ordonez Kate Saenko FAtt 75 125 0 05 Jun 2020
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images S. Chatterjee Fatima Saad Chompunuch Sarasaen Suhita Ghosh Valerie Krug ... Petia Radeva G. Rose Sebastian Stober Oliver Speck A. Nürnberger 104 26 0 03 Jun 2020
ExKMC: Expanding Explainable $k$ -Means Clustering Nave Frost Michal Moshkovitz Cyrus Rashtchian 78 56 0 03 Jun 2020
Local Interpretability of Calibrated Prediction Models: A Case of Type 2 Diabetes Mellitus Screening Test Simon Kocbek Primož Kocbek Leona Cilar Gregor Stiglic 53 2 0 02 Jun 2020
Shapley explainability on the data manifold Christopher Frye Damien de Mijolla T. Begley Laurence Cowton Megan Stanley Ilya Feige FAtt TDI 87 103 0 01 Jun 2020
Aligning Faithful Interpretations with their Social Attribution Alon Jacovi Yoav Goldberg 77 106 0 01 Jun 2020
Predicting Engagement in Video Lectures Sahan Bulathwela Maria Perez-Ortiz Aldo Lipani Emine Yilmaz John Shawe-Taylor 43 26 0 31 May 2020
Evaluations and Methods for Explanation through Robustness Analysis Cheng-Yu Hsieh Chih-Kuan Yeh Xuanqing Liu Pradeep Ravikumar Seungyeon Kim Sanjiv Kumar Cho-Jui Hsieh XAI 70 58 0 31 May 2020
Consistent feature selection for neural networks via Adaptive Group Lasso L. Ho Vu C. Dinh OOD 154 10 0 30 May 2020
Explainable Artificial Intelligence: a Systematic Review Giulia Vilone Luca Longo XAI 110 271 0 29 May 2020
A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers Kevin Fauvel Véronique Masson Elisa Fromont AI4TS 77 17 0 29 May 2020
On the Detection of Disinformation Campaign Activity with Network Analysis Luis Vargas Patrick Emami Patrick Traynor 54 69 0 27 May 2020
CausaLM: Causal Model Explanation Through Counterfactual Language Models Amir Feder Nadav Oved Uri Shalit Roi Reichart CML LRM 161 162 0 27 May 2020