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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
SHAP values for Explaining CNN-based Text Classification Models Wei Zhao Tarun Joshi V. Nair Agus Sudjianto FAtt 47 37 0 26 Aug 2020
Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction Darius Afchar Romain Hennequin FAtt XAI 86 16 0 26 Aug 2020
Looking Deeper into Tabular LIME Damien Garreau U. V. Luxburg FAtt LMTD 176 30 0 25 Aug 2020
Counterfactual Explanations for Machine Learning on Multivariate Time Series Data E. Ates Burak Aksar V. Leung A. Coskun AI4TS 111 68 0 25 Aug 2020
DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular Data Zhaocheng Liu Qiang Liu Haoli Zhang Yuntian Chen 84 13 0 22 Aug 2020
A Unified Taylor Framework for Revisiting Attribution Methods Huiqi Deng Na Zou Mengnan Du Weifu Chen Guo-Can Feng Helen Zhou FAtt TDI 146 21 0 21 Aug 2020
Explainability in Deep Reinforcement Learning Alexandre Heuillet Fabien Couthouis Natalia Díaz Rodríguez XAI 243 284 0 15 Aug 2020
Survey of XAI in digital pathology Milda Pocevičiūtė Gabriel Eilertsen Claes Lundström 75 56 0 14 Aug 2020
Can We Trust Your Explanations? Sanity Checks for Interpreters in Android Malware Analysis Ming Fan Wenying Wei Xiaofei Xie Yang Liu X. Guan Ting Liu FAtt AAML 102 38 0 13 Aug 2020
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay Sasha Rubin Thomas Gerspacher Martin C. Cooper Alexey Ignatiev Nina Narodytska FAtt 72 63 0 13 Aug 2020
Predictive and Causal Implications of using Shapley Value for Model Interpretation Sisi Ma R. Tourani FAtt TDI 59 37 0 12 Aug 2020
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability Dylan Slack Sophie Hilgard Sameer Singh Himabindu Lakkaraju FAtt 140 163 0 11 Aug 2020
Trustworthy AI Inference Systems: An Industry Research View Rosario Cammarota M. Schunter Anand Rajan Fabian Boemer Ágnes Kiss ... Aydin Aysu Fateme S. Hosseini Chengmo Yang Eric Wallace Pam Norton 109 14 0 10 Aug 2020
An Interpretable Deep Learning System for Automatically Scoring Request for Proposals S. Maji A. Appe Raghav Bali Veera Raghavendra Chikka Arijit Ghosh Chowdhury Vamsi M Bhandaru 37 6 0 05 Aug 2020
Explainable Predictive Process Monitoring Musabir Musabayli F. Maggi Williams Rizzi Josep Carmona Chiara Di Francescomarino 75 61 0 04 Aug 2020
A Causal Lens for Peeking into Black Box Predictive Models: Predictive Model Interpretation via Causal Attribution A. Khademi Vasant Honavar CML 39 9 0 01 Aug 2020
An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagery Pavan Rajkumar Magesh Richard Delwin Myloth Rijo Jackson Tom FAtt 52 197 0 01 Aug 2020
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies A. Markus J. Kors P. Rijnbeek 91 472 0 31 Jul 2020
Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification Cristina Garbacea Mengtian Guo Samuel Carton Qiaozhu Mei 60 28 0 31 Jul 2020
Revisiting the Modifiable Areal Unit Problem in Deep Traffic Prediction with Visual Analytics Wei Zeng Chengqiao Lin Juncong Lin Jincheng Jiang Jiazhi Xia Cagatay Turkay Wei Chen 53 27 0 30 Jul 2020
Surrogate Locally-Interpretable Models with Supervised Machine Learning Algorithms Linwei Hu Jie Chen V. Nair Agus Sudjianto 49 15 0 28 Jul 2020
The Tractability of SHAP-Score-Based Explanations over Deterministic and Decomposable Boolean Circuits Marcelo Arenas Pablo Barceló Mikaël Monet FAtt 80 8 0 28 Jul 2020
Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines with Applications to Explaining High-Dimensional Data Christopher D. Blakely Ole-Christoffer Granmo 46 16 0 27 Jul 2020
Three-stage intelligent support of clinical decision making for higher trust, validity, and explainability Sergey Kovalchuk G. Kopanitsa Ilya V. Derevitskii Daria A. Savitskaya 24 19 0 25 Jul 2020
Score-Based Explanations in Data Management and Machine Learning Leopoldo Bertossi FAtt XAI 61 4 0 24 Jul 2020
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance Mattia Carletti M. Terzi Gian Antonio Susto 54 42 0 21 Jul 2020
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models Catarina Moreira Yu-Liang Chou M. Velmurugan Chun Ouyang Renuka Sindhgatta P. Bruza 114 58 0 21 Jul 2020
Melody: Generating and Visualizing Machine Learning Model Summary to Understand Data and Classifiers Together G. Chan E. Bertini L. G. Nonato Brian Barr Claudio T. Silva 40 17 0 21 Jul 2020
DeepNNK: Explaining deep models and their generalization using polytope interpolation Sarath Shekkizhar Antonio Ortega 33 6 0 20 Jul 2020
Machine Learning approach for Credit Scoring A. R. Provenzano D. Trifirò A. Datteo L. Giada N. Jean A. Riciputi Giacomo Le Pera M. Spadaccino L. Massaron C. Nordio 39 21 0 20 Jul 2020
Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context Ehsan Toreini Mhairi Aitken Kovila P. L. Coopamootoo Karen Elliott Vladimiro González-Zelaya P. Missier Magdalene Ng Aad van Moorsel 74 18 0 17 Jul 2020
Deep Learning in Protein Structural Modeling and Design Wenhao Gao S. Mahajan Jeremias Sulam Jeffrey J. Gray 89 161 0 16 Jul 2020
Relative Feature Importance Gunnar Konig Christoph Molnar B. Bischl Moritz Grosse-Wentrup 76 49 0 16 Jul 2020
timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers Felix Mujkanovic Vanja Doskoc Martin Schirneck Patrick Schäfer Tobias Friedrich FAtt AI4TS 58 23 0 15 Jul 2020
On quantitative aspects of model interpretability An-phi Nguyen María Rodríguez Martínez 73 115 0 15 Jul 2020
VAE-LIME: Deep Generative Model Based Approach for Local Data-Driven Model Interpretability Applied to the Ironmaking Industry C. Schockaert Vadim Macher A. Schmitz 48 18 0 15 Jul 2020
Explaining Deep Neural Networks using Unsupervised Clustering Yu-Han Liu Sercan O. Arik SSL AI4CE 91 11 0 15 Jul 2020
Concept Learners for Few-Shot Learning Kaidi Cao Maria Brbic J. Leskovec VLM OffRL 92 4 0 14 Jul 2020
A simple defense against adversarial attacks on heatmap explanations Laura Rieger Lars Kai Hansen FAtt AAML 100 37 0 13 Jul 2020
Monitoring and explainability of models in production Janis Klaise A. V. Looveren Clive Cox G. Vacanti Alexandru Coca 116 49 0 13 Jul 2020
Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies D. Fryer Inga Strümke Hien Nguyen TDI FAtt 77 7 0 12 Jul 2020
Machine Learning Explainability for External Stakeholders Umang Bhatt Mckane Andrus Adrian Weller Alice Xiang FaML SILM 63 58 0 10 Jul 2020
Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case S. Wollenstein-Betech Christian Muise Christos G. Cassandras I. Paschalidis Y. Khazaeni 35 5 0 09 Jul 2020
Accuracy Prediction with Non-neural Model for Neural Architecture Search Renqian Luo Xu Tan Rui Wang Tao Qin Enhong Chen Tie-Yan Liu 69 14 0 09 Jul 2020
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models Christoph Molnar Gunnar Konig J. Herbinger Timo Freiesleben Susanne Dandl Christian A. Scholbeck Giuseppe Casalicchio Moritz Grosse-Wentrup B. Bischl FAtt AI4CE 85 140 0 08 Jul 2020
Model Distillation for Revenue Optimization: Interpretable Personalized Pricing Max Biggs Wei-Ju Sun M. Ettl 75 35 0 03 Jul 2020
Gaussian Process Regression with Local Explanation Yuya Yoshikawa Tomoharu Iwata FAtt 64 21 0 03 Jul 2020
Efficient computation and analysis of distributional Shapley values Yongchan Kwon Manuel A. Rivas James Zou FAtt TDI 150 62 0 02 Jul 2020
Explaining predictive models with mixed features using Shapley values and conditional inference trees Annabelle Redelmeier Martin Jullum K. Aas FAtt TDI 82 19 0 02 Jul 2020
Drug discovery with explainable artificial intelligence José Jiménez-Luna F. Grisoni G. Schneider 195 645 0 01 Jul 2020