"Why Should I Trust You?": Explaining the Predictions of Any Classifier

16 February 2016

Papers citing ""Why Should I Trust You?": Explaining the Predictions of Any Classifier"

50 / 4,309 papers shown

Title
Self-Explaining Structures Improve NLP Models Zijun Sun Chun Fan Qinghong Han Xiaofei Sun Yuxian Meng Fei Wu Jiwei Li MILM XAI LRM FAtt 48 38 0 03 Dec 2020
Explainable AI for Software Engineering Chakkrit Tantithamthavorn Jirayus Jiarpakdee J. Grundy 29 58 0 03 Dec 2020
Evaluating Explanations: How much do explanations from the teacher aid students? Danish Pruthi Rachit Bansal Bhuwan Dhingra Livio Baldini Soares Michael Collins Zachary Chase Lipton Graham Neubig William W. Cohen FAtt XAI 22 108 0 01 Dec 2020
Every Model Learned by Gradient Descent Is Approximately a Kernel Machine Pedro M. Domingos MLT 29 71 0 30 Nov 2020
Why model why? Assessing the strengths and limitations of LIME Jurgen Dieber S. Kirrane FAtt 26 97 0 30 Nov 2020
TimeSHAP: Explaining Recurrent Models through Sequence Perturbations João Bento Pedro Saleiro André F. Cruz Mário A. T. Figueiredo P. Bizarro FAtt AI4TS 24 88 0 30 Nov 2020
ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity Discovery Dawid Rymarczyk Lukasz Struski Jacek Tabor Bartosz Zieliñski 21 111 0 29 Nov 2020
Reflective-Net: Learning from Explanations Johannes Schneider Michalis Vlachos FAtt OffRL LRM 57 18 0 27 Nov 2020
Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation G. Agarwal L. Hay I. Iashvili Benjamin Mannix C. McLean Margaret E. Morris S. Rappoccio U. Schubert 46 18 0 26 Nov 2020
Understand Watchdogs: Discover How Game Bot Get Discovered Eunji Park Kyoung Ho Park H. Kim 9 4 0 26 Nov 2020
Towards Interpretable Multilingual Detection of Hate Speech against Immigrants and Women in Twitter at SemEval-2019 Task 5 A. Ishmam 21 1 0 26 Nov 2020
Probing Model Signal-Awareness via Prediction-Preserving Input Minimization Sahil Suneja Yunhui Zheng Yufan Zhuang Jim Laredo Alessandro Morari AAML 32 33 0 25 Nov 2020
Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations Wolfgang Stammer P. Schramowski Kristian Kersting FAtt 14 107 0 25 Nov 2020
Explaining by Removing: A Unified Framework for Model Explanation Ian Covert Scott M. Lundberg Su-In Lee FAtt 53 243 0 21 Nov 2020
Gradient Starvation: A Learning Proclivity in Neural Networks Mohammad Pezeshki Sekouba Kaba Yoshua Bengio Aaron Courville Doina Precup Guillaume Lajoie MLT 54 259 0 18 Nov 2020
Declarative Approaches to Counterfactual Explanations for Classification Leopoldo Bertossi 44 17 0 15 Nov 2020
Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty Soufiane Belharbi Jérôme Rony Jose Dolz Ismail Ben Ayed Luke McCaffrey Eric Granger 24 52 0 14 Nov 2020
Robust and Stable Black Box Explanations Himabindu Lakkaraju Nino Arsov Osbert Bastani AAML FAtt 24 84 0 12 Nov 2020
What Did You Think Would Happen? Explaining Agent Behaviour Through Intended Outcomes Herman Yau Chris Russell Simon Hadfield FAtt LRM 28 36 0 10 Nov 2020
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End R. Mothilal Divyat Mahajan Chenhao Tan Amit Sharma FAtt CML 32 100 0 10 Nov 2020
Parameterized Explainer for Graph Neural Network Dongsheng Luo Wei Cheng Dongkuan Xu Wenchao Yu Bo Zong Haifeng Chen Xiang Zhang 53 542 0 09 Nov 2020
Explaining Deep Graph Networks with Molecular Counterfactuals Danilo Numeroso D. Bacciu 21 10 0 09 Nov 2020
Adversarial Black-Box Attacks On Text Classifiers Using Multi-Objective Genetic Optimization Guided By Deep Networks Alex Mathai Shreya Khare Srikanth G. Tamilselvam Senthil Mani AAML 36 6 0 08 Nov 2020
Explainable Automated Fact-Checking: A Survey Neema Kotonya Francesca Toni 8 113 0 07 Nov 2020
Feature Removal Is a Unifying Principle for Model Explanation Methods Ian Covert Scott M. Lundberg Su-In Lee FAtt 33 33 0 06 Nov 2020
Learning Causal Semantic Representation for Out-of-Distribution Prediction Chang-Shu Liu Xinwei Sun Jindong Wang Haoyue Tang Tao Li Tao Qin Wei Chen Tie-Yan Liu CML OODD OOD 35 104 0 03 Nov 2020
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models Tom Heskes E. Sijben I. G. Bucur Tom Claassen FAtt TDI 25 151 0 03 Nov 2020
Quadratic Metric Elicitation for Fairness and Beyond Gaurush Hiranandani Jatin Mathur Harikrishna Narasimhan Oluwasanmi Koyejo 27 5 0 03 Nov 2020
MAIRE -- A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers Rajat Sharma N. Reddy V. Kamakshi N. C. Krishnan Shweta Jain FAtt 29 7 0 03 Nov 2020
Weakly- and Semi-supervised Evidence Extraction Danish Pruthi Bhuwan Dhingra Graham Neubig Zachary Chase Lipton 9 23 0 03 Nov 2020
Towards Ethics by Design in Online Abusive Content Detection S. Kiritchenko I. Nejadgholi 34 13 0 28 Oct 2020
Selective Classification Can Magnify Disparities Across Groups Erik Jones Shiori Sagawa Pang Wei Koh Ananya Kumar Percy Liang 39 46 0 27 Oct 2020
A robust low data solution: dimension prediction of semiconductor nanorods Xiaoli Liu Yang Xu Jiali Li Xuanwei Ong S. A. Ibrahim Tonio Buonassisi Xiaonan Wang 14 11 0 27 Oct 2020
Interpretation of NLP models through input marginalization Siwon Kim Jihun Yi Eunji Kim Sungroh Yoon MILM FAtt 30 58 0 27 Oct 2020
GPUTreeShap: Massively Parallel Exact Calculation of SHAP Scores for Tree Ensembles Rory Mitchell E. Frank G. Holmes 14 55 0 27 Oct 2020
Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability Gintare Karolina Dziugaite Shai Ben-David Daniel M. Roy FaML 17 39 0 26 Oct 2020
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization Judy Borowski Roland S. Zimmermann Judith Schepers Robert Geirhos Thomas S. A. Wallis Matthias Bethge Wieland Brendel FAtt 47 7 0 23 Oct 2020
A Multilinear Sampling Algorithm to Estimate Shapley Values Ramin Okhrati Aldo Lipani TDI FAtt 87 38 0 22 Oct 2020
Meta-trained agents implement Bayes-optimal agents Vladimir Mikulik Grégoire Delétang Tom McGrath Tim Genewein Miljan Martic Shane Legg Pedro A. Ortega OOD FedML 40 41 0 21 Oct 2020
Axiom Learning and Belief Tracing for Transparent Decision Making in Robotics Tiago Mota Mohan Sridharan 21 5 0 20 Oct 2020
Interpreting convolutional networks trained on textual data Reza Marzban Christopher Crick FAtt 27 3 0 20 Oct 2020
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review Sahil Verma Varich Boonsanong Minh Hoang Keegan E. Hines John P. Dickerson Chirag Shah CML 28 164 0 20 Oct 2020
A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images Pablo Messina Pablo Pino Denis Parra Alvaro Soto Cecilia Besa S. Uribe Marcelo andía C. Tejos Claudia Prieto Daniel Capurro MedIm 36 62 0 20 Oct 2020
Explainable Automated Fact-Checking for Public Health Claims Neema Kotonya Francesca Toni 218 251 0 19 Oct 2020
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 41 48 0 19 Oct 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 28 396 0 19 Oct 2020
Feature Importance Ranking for Deep Learning Maksymilian Wojtas Ke Chen 147 115 0 18 Oct 2020
Altruist: Argumentative Explanations through Local Interpretations of Predictive Models Ioannis Mollas Nick Bassiliades Grigorios Tsoumakas 21 13 0 15 Oct 2020
Hierarchical Text Interaction for Rating Prediction Jiahui Wen Jingwei Ma Hongkui Tu Wei Yin Jian Fang 27 6 0 15 Oct 2020
Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines A. Konstantinov Lev V. Utkin FedML AI4CE 12 139 0 14 Oct 2020