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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
Marginal Contribution Feature Importance -- an Axiomatic Approach for The Natural Case Amnon Catav Boyang Fu J. Ernst S. Sankararaman Ran Gilad-Bachrach FAtt 41 3 0 15 Oct 2020
Altruist: Argumentative Explanations through Local Interpretations of Predictive Models Ioannis Mollas Nick Bassiliades Grigorios Tsoumakas 69 13 0 15 Oct 2020
Explainability for fair machine learning T. Begley Tobias Schwedes Christopher Frye Ilya Feige FaML FedML 101 47 0 14 Oct 2020
Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines A. Konstantinov Lev V. Utkin FedML AI4CE 60 153 0 14 Oct 2020
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering Hendrik Schuff Heike Adel Ngoc Thang Vu ELM 65 18 0 13 Oct 2020
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks Minh Nhat Vu My T. Thai BDL 91 339 0 12 Oct 2020
Gradient-based Analysis of NLP Models is Manipulable Junlin Wang Jens Tuyls Eric Wallace Sameer Singh AAML FAtt 78 60 0 12 Oct 2020
Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series Vincent Jacob Fei Song Arnaud Stiegler Bijan Rad Y. Diao Nesime Tatbul AI4TS 100 68 0 10 Oct 2020
FIND: Human-in-the-Loop Debugging Deep Text Classifiers Piyawat Lertvittayakumjorn Lucia Specia Francesca Toni 62 54 0 10 Oct 2020
Evaluating and Characterizing Human Rationales Samuel Carton Anirudh Rathore Chenhao Tan 74 49 0 09 Oct 2020
Learning the Pareto Front with Hypernetworks Aviv Navon Aviv Shamsian Gal Chechik Ethan Fetaya 123 146 0 08 Oct 2020
Astraea: Grammar-based Fairness Testing E. Soremekun Sakshi Udeshi Sudipta Chattopadhyay 154 31 0 06 Oct 2020
Visualizing Color-wise Saliency of Black-Box Image Classification Models Yuhki Hatakeyama Hiroki Sakuma Yoshinori Konishi Kohei Suenaga FAtt 64 3 0 06 Oct 2020
Fairness in Machine Learning: A Survey Simon Caton C. Haas FaML 116 656 0 04 Oct 2020
Explaining Deep Neural Networks Oana-Maria Camburu XAI FAtt 110 26 0 04 Oct 2020
Predicting traffic overflows on private peering Elad Rapaport Ingmar Poese Polina Zilberman Oliver Holschke Rami Puzis 15 1 0 03 Oct 2020
Stuttering Speech Disfluency Prediction using Explainable Attribution Vectors of Facial Muscle Movements Arun Das J. Mock Henry Chacón Farzan Irani E. Golob Peyman Najafirad 95 9 0 02 Oct 2020
Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers Hanjie Chen Yangfeng Ji AAML VLM 109 66 0 01 Oct 2020
Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task Han-Ching Wu Wenjie Ruan Jiangtao Wang Dingchang Zheng Bei Liu ... Xiangfei Chai Jian Chen Kunwei Li Shaolin Li A. Helal 87 27 0 30 Sep 2020
Explainable Deep Reinforcement Learning for UAV Autonomous Navigation Lei He Nabil Aouf Bifeng Song 70 11 0 30 Sep 2020
Trustworthy Convolutional Neural Networks: A Gradient Penalized-based Approach Nicholas F Halliwell Freddy Lecue FAtt 118 9 0 29 Sep 2020
A Human-in-the-Loop Approach based on Explainability to Improve NTL Detection Bernat Coma-Puig J. Carmona 43 1 0 28 Sep 2020
Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring Michael Bücker G. Szepannek Alicja Gosiewska P. Biecek FaML 63 115 0 28 Sep 2020
Generating End-to-End Adversarial Examples for Malware Classifiers Using Explainability Ishai Rosenberg Shai Meir J. Berrebi I. Gordon Guillaume Sicard Eli David AAML SILM 31 28 0 28 Sep 2020
Instance-based Counterfactual Explanations for Time Series Classification Eoin Delaney Derek Greene Mark T. Keane CML AI4TS 63 91 0 28 Sep 2020
A Diagnostic Study of Explainability Techniques for Text Classification Pepa Atanasova J. Simonsen Christina Lioma Isabelle Augenstein XAI FAtt 104 226 0 25 Sep 2020
Landscape of R packages for eXplainable Artificial Intelligence Szymon Maksymiuk Alicja Gosiewska P. Biecek XAI 83 22 0 24 Sep 2020
Information-Theoretic Visual Explanation for Black-Box Classifiers Jihun Yi Eunji Kim Siwon Kim Sungroh Yoon FAtt 88 6 0 23 Sep 2020
Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans Rohun A. Kshirsagar Li-yen Hsu Vatshank Chaturvedi Charles H. Greenberg Matthew McClelland ... Ankit Chheda Meredith V. Trotter Shonket Ray Arnold Lee Miguel Alvarado 32 12 0 23 Sep 2020
Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing Nijat Mehdiyev Peter Fettke 78 11 0 22 Sep 2020
A Generic Methodology for the Statistically Uniform & Comparable Evaluation of Automated Trading Platform Components A. Sokolovsky Luca Arnaboldi 38 3 0 21 Sep 2020
Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray images Lucas O. Teixeira R. M. Pereira Diego Bertolini Luiz Eduardo Soares de Oliveira L. Nanni George D. C. Cavalcanti Yandre M. G. Costa 97 114 0 21 Sep 2020
Evaluation of Local Explanation Methods for Multivariate Time Series Forecasting Ozan Ozyegen Igor Ilic Mucahit Cevik FAtt AI4TS 43 2 0 18 Sep 2020
Principles and Practice of Explainable Machine Learning Vaishak Belle I. Papantonis FaML 86 454 0 18 Sep 2020
On the Tractability of SHAP Explanations Guy Van den Broeck A. Lykov Maximilian Schleich Dan Suciu FAtt TDI 119 295 0 18 Sep 2020
Captum: A unified and generic model interpretability library for PyTorch Narine Kokhlikyan Vivek Miglani Miguel Martin Edward Wang B. Alsallakh ... Alexander Melnikov Natalia Kliushkina Carlos Araya Siqi Yan Orion Reblitz-Richardson FAtt 204 856 0 16 Sep 2020
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses Kaivalya Rawal Himabindu Lakkaraju 108 11 0 15 Sep 2020
An explainable XGBoost-based approach towards assessing the risk of cardiovascular disease in patients with Type 2 Diabetes Mellitus M. Athanasiou Konstantina Sfrintzeri K. Zarkogianni Anastasia C. Thanopoulou K. Nikita 22 51 0 14 Sep 2020
MeLIME: Meaningful Local Explanation for Machine Learning Models T. Botari Frederik Hvilshoj Rafael Izbicki A. Carvalho AAML FAtt 75 16 0 12 Sep 2020
Interpretable Machine Learning Approaches to Prediction of Chronic Homelessness Blake VanBerlo Matthew A. S. Ross Jonathan Rivard Ryan Booker 44 26 0 12 Sep 2020
Machine Learning for Temporal Data in Finance: Challenges and Opportunities J. Wittenbach Learning McLean Virginia Brian AI4TS 28 1 0 11 Sep 2020
TREX: Tree-Ensemble Representer-Point Explanations Jonathan Brophy Daniel Lowd TDI 77 7 0 11 Sep 2020
Towards a More Reliable Interpretation of Machine Learning Outputs for Safety-Critical Systems using Feature Importance Fusion D. Rengasamy Benjamin Rothwell Grazziela Figueredo FaML FAtt 40 35 0 11 Sep 2020
Towards Interpretable Multi-Task Learning Using Bilevel Programming Francesco Alesiani Shujian Yu Ammar Shaker Wenzhe Yin 31 4 0 11 Sep 2020
CounteRGAN: Generating Realistic Counterfactuals with Residual Generative Adversarial Nets Daniel Nemirovsky Nicolas Thiebaut Ye Xu Abhishek Gupta CML GAN 78 35 0 11 Sep 2020
Understanding the Role of Individual Units in a Deep Neural Network David Bau Jun-Yan Zhu Hendrik Strobelt Àgata Lapedriza Bolei Zhou Antonio Torralba GAN 84 456 0 10 Sep 2020
Enhancing the Interpretability of Deep Models in Heathcare Through Attention: Application to Glucose Forecasting for Diabetic People Maxime De Bois M. El-Yacoubi M. Ammi 18 5 0 08 Sep 2020
SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations Sebastian Mežnar Nada Lavrac Blaž Škrlj 94 5 0 08 Sep 2020
How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks Thomas Fel David Vigouroux Rémi Cadène Thomas Serre XAI FAtt 75 31 0 07 Sep 2020
Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic Dataset Erico Tjoa Cuntai Guan XAI FAtt 103 27 0 07 Sep 2020