"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 / 4,253 papers shown

Title
A Blended Deep Learning Approach for Predicting User Intended Actions Fei Tan Zhi Wei Jun He Xiang Wu Bo Peng Haoran Liu Zhenyu Yan 19 16 0 11 Oct 2018
What made you do this? Understanding black-box decisions with sufficient input subsets Brandon Carter Jonas W. Mueller Siddhartha Jain David K Gifford FAtt 37 77 0 09 Oct 2018
Understanding the Origins of Bias in Word Embeddings Marc-Etienne Brunet Colleen Alkalay-Houlihan Ashton Anderson R. Zemel FaML 26 198 0 08 Oct 2018
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values Julius Adebayo Justin Gilmer Ian Goodfellow Been Kim FAtt AAML 19 128 0 08 Oct 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 64 1,931 0 08 Oct 2018
On the Art and Science of Machine Learning Explanations Patrick Hall FAtt XAI 28 30 0 05 Oct 2018
Projective Inference in High-dimensional Problems: Prediction and Feature Selection Juho Piironen Markus Paasiniemi Aki Vehtari 30 94 0 04 Oct 2018
Interpreting Layered Neural Networks via Hierarchical Modular Representation C. Watanabe 21 19 0 03 Oct 2018
Explainable Black-Box Attacks Against Model-based Authentication Washington Garcia Joseph I. Choi S. K. Adari S. Jha Kevin R. B. Butler 26 10 0 28 Sep 2018
A User-based Visual Analytics Workflow for Exploratory Model Analysis Dylan Cashman S. Humayoun Florian Heimerl Kendall Park Subhajit Das ... Abigail Mosca J. Stasko Alex Endert Michael Gleicher Remco Chang 21 41 0 27 Sep 2018
Actionable Recourse in Linear Classification Berk Ustun Alexander Spangher Yang Liu FaML 28 539 0 18 Sep 2018
Object-sensitive Deep Reinforcement Learning Yuezhang Li Katia P. Sycara R. Iyer 18 65 0 17 Sep 2018
Transparency and Explanation in Deep Reinforcement Learning Neural Networks R. Iyer Yuezhang Li Huao Li M. Lewis R. Sundar Katia P. Sycara 23 172 0 17 Sep 2018
Fair lending needs explainable models for responsible recommendation Jiahao Chen FaML SILM 8 27 0 12 Sep 2018
Assessing Composition in Sentence Vector Representations Allyson Ettinger Ahmed Elgohary C. Phillips Philip Resnik CoGe 20 78 0 11 Sep 2018
Automated Test Generation to Detect Individual Discrimination in AI Models Aniya Aggarwal P. Lohia Seema Nagar Kuntal Dey Diptikalyan Saha 15 40 0 10 Sep 2018
Interpreting Neural Networks With Nearest Neighbors Eric Wallace Shi Feng Jordan L. Boyd-Graber AAML FAtt MILM 15 53 0 08 Sep 2018
Faithful Multimodal Explanation for Visual Question Answering Jialin Wu Raymond J. Mooney 20 90 0 08 Sep 2018
DeepPINK: reproducible feature selection in deep neural networks Yang Young Lu Yingying Fan Jinchi Lv William Stafford Noble FAtt 27 124 0 04 Sep 2018
Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts Samuel Carton Qiaozhu Mei Paul Resnick FAtt AAML 21 34 0 01 Sep 2018
An Operation Sequence Model for Explainable Neural Machine Translation Felix Stahlberg Danielle Saunders Bill Byrne LRM MILM 40 29 0 29 Aug 2018
Targeted Nonlinear Adversarial Perturbations in Images and Videos R. Rey-de-Castro H. Rabitz AAML 16 10 0 27 Aug 2018
Unknown Examples & Machine Learning Model Generalization Yeounoh Chung P. Haas E. Upfal Tim Kraska OOD 21 32 0 24 Aug 2018
Shedding Light on Black Box Machine Learning Algorithms: Development of an Axiomatic Framework to Assess the Quality of Methods that Explain Individual Predictions Milo Honegger 17 35 0 15 Aug 2018
Explaining the Unique Nature of Individual Gait Patterns with Deep Learning Fabian Horst Sebastian Lapuschkin Wojciech Samek K. Müller W. Schöllhorn AI4CE 31 207 0 13 Aug 2018
Text Classification using Capsules Jaeyoung Kim Sion Jang Sungchul Choi Eunjeong Lucy Park 17 160 0 12 Aug 2018
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data Jianbo Chen Le Song Martin J. Wainwright Michael I. Jordan FAtt TDI 14 213 0 08 Aug 2018
Using Machine Learning Safely in Automotive Software: An Assessment and Adaption of Software Process Requirements in ISO 26262 Rick Salay Krzysztof Czarnecki 25 69 0 05 Aug 2018
Techniques for Interpretable Machine Learning Mengnan Du Ninghao Liu Xia Hu FaML 39 1,072 0 31 Jul 2018
Grounding Visual Explanations Lisa Anne Hendricks Ronghang Hu Trevor Darrell Zeynep Akata FAtt 17 225 0 25 Jul 2018
Contrastive Explanations for Reinforcement Learning in terms of Expected Consequences J. V. D. Waa J. Diggelen K. Bosch Mark Antonius Neerincx OffRL 31 106 0 23 Jul 2018
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time Feargus Pendlebury Fabio Pierazzi Roberto Jordaney Johannes Kinder Lorenzo Cavallaro 6 352 0 20 Jul 2018
Take a Look Around: Using Street View and Satellite Images to Estimate House Prices Stephen Law Brooks Paige Chris Russell 15 132 0 18 Jul 2018
RuleMatrix: Visualizing and Understanding Classifiers with Rules Yao Ming Huamin Qu E. Bertini FAtt 20 214 0 17 Jul 2018
Layer-wise Relevance Propagation for Explainable Recommendations Homanga Bharadhwaj FAtt 21 8 0 17 Jul 2018
Automated Data Slicing for Model Validation:A Big data - AI Integration Approach Yeounoh Chung Tim Kraska N. Polyzotis Ki Hyun Tae Steven Euijong Whang 19 129 0 16 Jul 2018
Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees Guiliang Liu Oliver Schulte Wang Zhu Qingcan Li AI4CE 17 134 0 16 Jul 2018
A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees Min Wu Matthew Wicker Wenjie Ruan Xiaowei Huang Marta Kwiatkowska AAML 19 111 0 10 Jul 2018
Model Agnostic Supervised Local Explanations Gregory Plumb Denali Molitor Ameet Talwalkar FAtt LRM MILM 14 196 0 09 Jul 2018
Women also Snowboard: Overcoming Bias in Captioning Models (Extended Abstract) Lisa Anne Hendricks Kaylee Burns Kate Saenko Trevor Darrell Anna Rohrbach 39 477 0 02 Jul 2018
Optimal Piecewise Local-Linear Approximations Kartik Ahuja W. Zame M. Schaar FAtt 27 1 0 27 Jun 2018
Open the Black Box Data-Driven Explanation of Black Box Decision Systems D. Pedreschi F. Giannotti Riccardo Guidotti A. Monreale Luca Pappalardo Salvatore Ruggieri Franco Turini 19 38 0 26 Jun 2018
xGEMs: Generating Examplars to Explain Black-Box Models Shalmali Joshi Oluwasanmi Koyejo Been Kim Joydeep Ghosh MLAU 25 40 0 22 Jun 2018
Learning Qualitatively Diverse and Interpretable Rules for Classification A. Ross Weiwei Pan Finale Doshi-Velez 16 13 0 22 Jun 2018
Interpretable Discovery in Large Image Data Sets K. Wagstaff Jake H. Lee 19 9 0 21 Jun 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 30 522 0 21 Jun 2018
Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach A. C. Gusmão Alvaro H. C. Correia Glauber De Bona Fabio Gagliardi Cozman 27 22 0 20 Jun 2018
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems Richard J. Tomsett Dave Braines Daniel Harborne Alun D. Preece Supriyo Chakraborty FaML 29 164 0 20 Jun 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 56 933 0 20 Jun 2018
DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks Mostafa Karimi Di Wu Zhangyang Wang Yang Shen 35 358 0 20 Jun 2018