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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
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data Tian Guo Tao R. Lin Nino Antulov-Fantulin AI4TS 94 156 0 28 May 2019
Infusing domain knowledge in AI-based "black box" models for better explainability with application in bankruptcy prediction Sheikh Rabiul Islam W. Eberle Sid Bundy S. Ghafoor MLAU 69 23 0 27 May 2019
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments Vasilis Syrgkanis Victor Lei Miruna Oprescu Maggie Hei Keith Battocchi Greg Lewis CML 50 73 0 24 May 2019
Computationally Efficient Feature Significance and Importance for Machine Learning Models Enguerrand Horel K. Giesecke FAtt 55 9 0 23 May 2019
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations R. Mothilal Amit Sharma Chenhao Tan CML 140 1,033 0 19 May 2019
Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees Summer Devlin Chandan Singh W. James Murdoch Bin Yu FAtt 62 14 0 18 May 2019
From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices Jessica Morley Luciano Floridi Libby Kinsey Anat Elhalal 83 57 0 15 May 2019
Learning Policies from Self-Play with Policy Gradients and MCTS Value Estimates Dennis J. N. J. Soemers Éric Piette Matthew Stephenson C. Browne 62 8 0 14 May 2019
Modelling urban networks using Variational Autoencoders Kira Kempinska R. Murcio GNN 42 39 0 14 May 2019
Explainable AI for Trees: From Local Explanations to Global Understanding Scott M. Lundberg G. Erion Hugh Chen A. DeGrave J. Prutkin B. Nair R. Katz J. Himmelfarb N. Bansal Su-In Lee FAtt 113 291 0 11 May 2019
Interpret Federated Learning with Shapley Values Guan Wang FAtt FedML 71 92 0 11 May 2019
Machine learning-guided synthesis of advanced inorganic materials Bijun Tang Yuhao Lu Jiadong Zhou Han Wang Prafful Golani Manzhang Xu Quan Xu Cuntai Guan Zheng Liu AI4CE 31 99 0 10 May 2019
Visualizing Deep Networks by Optimizing with Integrated Gradients Zhongang Qi Saeed Khorram Fuxin Li FAtt 83 126 0 02 May 2019
Full-Gradient Representation for Neural Network Visualization Suraj Srinivas François Fleuret MILM FAtt 120 278 0 02 May 2019
Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance Giles Hooker L. Mentch Siyu Zhou 93 159 0 01 May 2019
To believe or not to believe: Validating explanation fidelity for dynamic malware analysis Li-Wei Chen Carter Yagemann Evan Downing AAML FAtt 56 3 0 30 Apr 2019
Evaluating Recurrent Neural Network Explanations L. Arras Ahmed Osman K. Müller Wojciech Samek XAI FAtt 117 88 0 26 Apr 2019
Explaining a prediction in some nonlinear models Cosimo Izzo FAtt 41 0 0 21 Apr 2019
Explainability in Human-Agent Systems A. Rosenfeld A. Richardson XAI 83 207 0 17 Apr 2019
Enhancing Time Series Momentum Strategies Using Deep Neural Networks Bryan Lim S. Zohren Stephen J. Roberts AIFin AI4TS 72 90 0 09 Apr 2019
Software and application patterns for explanation methods Maximilian Alber 80 11 0 09 Apr 2019
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations Christian A. Scholbeck Christoph Molnar C. Heumann B. Bischl Giuseppe Casalicchio 108 27 0 08 Apr 2019
Data Shapley: Equitable Valuation of Data for Machine Learning Amirata Ghorbani James Zou TDI FedML 142 796 0 05 Apr 2019
Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks Woo-Jeoung Nam Shir Gur Jaesik Choi Lior Wolf Seong-Whan Lee FAtt 83 99 0 01 Apr 2019
Interpreting Black Box Models via Hypothesis Testing Collin Burns Jesse Thomason Wesley Tansey FAtt 80 9 0 29 Mar 2019
Do Not Trust Additive Explanations Alicja Gosiewska P. Biecek 73 42 0 27 Mar 2019
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation Marco Ancona Cengiz Öztireli Markus Gross FAtt TDI 121 230 0 26 Mar 2019
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values K. Aas Martin Jullum Anders Løland FAtt TDI 90 635 0 25 Mar 2019
Activation Analysis of a Byte-Based Deep Neural Network for Malware Classification Scott E. Coull Christopher Gardner 64 51 0 12 Mar 2019
GNNExplainer: Generating Explanations for Graph Neural Networks Rex Ying Dylan Bourgeois Jiaxuan You Marinka Zitnik J. Leskovec LLMAG 163 1,339 0 10 Mar 2019
A Grid-based Method for Removing Overlaps of Dimensionality Reduction Scatterplot Layouts Gladys M. H. Hilasaca Wilson E. Marcílio-Jr D. M. Eler Rafael M. Martins F. Paulovich 54 9 0 08 Mar 2019
Explaining Anomalies Detected by Autoencoders Using SHAP Liat Antwarg Ronnie Mindlin Miller Bracha Shapira Lior Rokach FAtt TDI 77 86 0 06 Mar 2019
Towards Efficient Data Valuation Based on the Shapley Value R. Jia David Dao Wei Ping F. Hubis Nicholas Hynes Nezihe Merve Gürel Yue Liu Ce Zhang Basel Alomair C. Spanos TDI 118 426 0 27 Feb 2019
Forecasting intracranial hypertension using multi-scale waveform metrics Matthias Huser A. Kündig W. Karlen V. D. Luca Martin Jaggi 13 18 0 25 Feb 2019
Explaining a black-box using Deep Variational Information Bottleneck Approach Seo-Jin Bang P. Xie Heewook Lee Wei Wu Eric Xing XAI FAtt 77 77 0 19 Feb 2019
Regularizing Black-box Models for Improved Interpretability Gregory Plumb Maruan Al-Shedivat Ángel Alexander Cabrera Adam Perer Eric Xing Ameet Talwalkar AAML 125 80 0 18 Feb 2019
STRIP: A Defence Against Trojan Attacks on Deep Neural Networks Yansong Gao Chang Xu Derui Wang Shiping Chen Damith C. Ranasinghe Surya Nepal AAML 98 821 0 18 Feb 2019
LS-Tree: Model Interpretation When the Data Are Linguistic Jianbo Chen Michael I. Jordan 64 18 0 11 Feb 2019
Global Explanations of Neural Networks: Mapping the Landscape of Predictions Mark Ibrahim Melissa Louie C. Modarres John Paisley FAtt 97 119 0 06 Feb 2019
Fooling Neural Network Interpretations via Adversarial Model Manipulation Juyeon Heo Sunghwan Joo Taesup Moon AAML FAtt 129 206 0 06 Feb 2019
Fairwashing: the risk of rationalization Ulrich Aïvodji Hiromi Arai O. Fortineau Sébastien Gambs Satoshi Hara Alain Tapp FaML 70 147 0 28 Jan 2019
Testing Conditional Independence in Supervised Learning Algorithms David S. Watson Marvin N. Wright CML 98 53 0 28 Jan 2019
On the (In)fidelity and Sensitivity for Explanations Chih-Kuan Yeh Cheng-Yu Hsieh A. Suggala David I. Inouye Pradeep Ravikumar FAtt 110 456 0 27 Jan 2019
ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU William Caicedo-Torres Jairo Gutiérrez 41 82 0 24 Jan 2019
On Network Science and Mutual Information for Explaining Deep Neural Networks Brian Davis Umang Bhatt Kartikeya Bhardwaj R. Marculescu J. M. F. Moura FedML SSL FAtt 55 10 0 20 Jan 2019
Quantifying Interpretability and Trust in Machine Learning Systems Philipp Schmidt F. Biessmann 56 115 0 20 Jan 2019
Towards Aggregating Weighted Feature Attributions Umang Bhatt Pradeep Ravikumar José M. F. Moura FAtt TDI 34 13 0 20 Jan 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin Yu XAI HAI 211 1,459 0 14 Jan 2019
Interpretable CNNs for Object Classification Quanshi Zhang Xin Eric Wang Ying Nian Wu Huilin Zhou Song-Chun Zhu 61 54 0 08 Jan 2019
Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks Zenan Ling Haotian Ma Yu Yang Robert C. Qiu Song-Chun Zhu Quanshi Zhang MILM 36 3 0 08 Jan 2019