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"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 / 4,966 papers shown

Title
Learning Qualitatively Diverse and Interpretable Rules for Classification A. Ross Weiwei Pan Finale Doshi-Velez 57 13 0 22 Jun 2018
Interpretable Discovery in Large Image Data Sets K. Wagstaff Jake H. Lee 32 9 0 21 Jun 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 103 529 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 29 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 143 166 0 20 Jun 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 135 948 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 89 364 0 20 Jun 2018
Contrastive Explanations with Local Foil Trees J. V. D. Waa M. Robeer J. Diggelen Matthieu J. S. Brinkhuis Mark Antonius Neerincx FAtt 79 82 0 19 Jun 2018
RISE: Randomized Input Sampling for Explanation of Black-box Models Vitali Petsiuk Abir Das Kate Saenko FAtt 215 1,177 0 19 Jun 2018
Instance-Level Explanations for Fraud Detection: A Case Study Dennis Collaris L. M. Vink J. V. Wijk 79 31 0 19 Jun 2018
Deep Neural Decision Trees Yongxin Yang Irene Garcia Morillo Timothy M. Hospedales PINN 63 187 0 19 Jun 2018
Biased Embeddings from Wild Data: Measuring, Understanding and Removing Adam Sutton Thomas Lansdall-Welfare N. Cristianini 63 23 0 16 Jun 2018
Right for the Right Reason: Training Agnostic Networks Sen Jia Thomas Lansdall-Welfare N. Cristianini FaML 58 26 0 16 Jun 2018
Binary Classification in Unstructured Space With Hypergraph Case-Based Reasoning Alexandre Quemy 40 7 0 16 Jun 2018
Interactive Classification for Deep Learning Interpretation Ángel Alexander Cabrera Fred Hohman Jason Lin Duen Horng Chau VLM HAI 40 12 0 14 Jun 2018
Hierarchical interpretations for neural network predictions Chandan Singh W. James Murdoch Bin Yu 77 146 0 14 Jun 2018
Understanding Patch-Based Learning by Explaining Predictions Christopher J. Anders G. Montavon Wojciech Samek K. Müller UQCV FAtt 62 6 0 11 Jun 2018
A New Framework for Machine Intelligence: Concepts and Prototype Abel Torres Montoya 47 0 0 06 Jun 2018
Performance Metric Elicitation from Pairwise Classifier Comparisons Gaurush Hiranandani Shant Boodaghians R. Mehta Oluwasanmi Koyejo 64 14 0 05 Jun 2018
Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP) Linwei Hu Jie Chen V. Nair Agus Sudjianto FAtt 69 64 0 02 Jun 2018
A Review of Challenges and Opportunities in Machine Learning for Health Marzyeh Ghassemi Tristan Naumann Peter F. Schulam Andrew L. Beam Irene Y. Chen Rajesh Ranganath 92 270 0 01 Jun 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 121 1,868 0 31 May 2018
Teaching Meaningful Explanations Noel Codella Michael Hind Karthikeyan N. Ramamurthy Murray Campbell Amit Dhurandhar Kush R. Varshney Dennis L. Wei Aleksandra Mojsilović FAtt XAI 63 7 0 29 May 2018
Human-in-the-Loop Interpretability Prior Isaac Lage A. Ross Been Kim S. Gershman Finale Doshi-Velez 89 121 0 29 May 2018
Lightly-supervised Representation Learning with Global Interpretability M. A. Valenzuela-Escarcega Ajay Nagesh Mihai Surdeanu SSL 67 23 0 29 May 2018
Local Rule-Based Explanations of Black Box Decision Systems Riccardo Guidotti A. Monreale Salvatore Ruggieri D. Pedreschi Franco Turini F. Giannotti 144 439 0 28 May 2018
RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records Bum Chul Kwon Min-Je Choi J. Kim Edward Choi Young Bin Kim Soonwook Kwon Jimeng Sun Jaegul Choo 78 252 0 28 May 2018
Semantic Explanations of Predictions Freddy Lecue Jiewen Wu FAtt 26 11 0 27 May 2018
Personalized Influence Estimation Technique Kumarjit Pathak Jitin Kapila Aasheesh Barvey TDI 24 0 0 25 May 2018
Communication Algorithms via Deep Learning Hyeji Kim Yihan Jiang Ranvir Rana Sreeram Kannan Sewoong Oh Pramod Viswanath 58 220 0 23 May 2018
"Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Users Stefano Teso Kristian Kersting FAtt HAI 52 12 0 22 May 2018
Unsupervised Learning of Neural Networks to Explain Neural Networks Quanshi Zhang Yu Yang Yuchen Liu Ying Nian Wu Song-Chun Zhu FAtt SSL 61 27 0 18 May 2018
Defoiling Foiled Image Captions Pranava Madhyastha Josiah Wang Lucia Specia 59 9 0 16 May 2018
Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models Jacob R. Kauffmann K. Müller G. Montavon DRL 74 97 0 16 May 2018
SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines Roy Schwartz Sam Thomson Noah A. Smith 74 24 0 15 May 2018
Did the Model Understand the Question? Pramod Kaushik Mudrakarta Ankur Taly Mukund Sundararajan Kedar Dhamdhere ELM OOD FAtt 76 199 0 14 May 2018
Faithfully Explaining Rankings in a News Recommender System Maartje ter Hoeve Anne Schuth Daan Odijk Maarten de Rijke OffRL 35 24 0 14 May 2018
State Gradients for RNN Memory Analysis Lyan Verwimp Hugo Van hamme Vincent Renkens P. Wambacq 39 6 0 11 May 2018
Behavior Analysis of NLI Models: Uncovering the Influence of Three Factors on Robustness V. Carmona Jeff Mitchell Sebastian Riedel 83 44 0 11 May 2018
A Symbolic Approach to Explaining Bayesian Network Classifiers Andy Shih Arthur Choi Adnan Darwiche FAtt 86 243 0 09 May 2018
Explainable Recommendation: A Survey and New Perspectives Yongfeng Zhang Xu Chen XAI LRM 124 881 0 30 Apr 2018
Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models Hendrik Strobelt Sebastian Gehrmann M. Behrisch Adam Perer Hanspeter Pfister Alexander M. Rush VLM HAI 65 240 0 25 Apr 2018
A Nutritional Label for Rankings Ke Yang Julia Stoyanovich Abolfazl Asudeh Bill Howe H. V. Jagadish G. Miklau 71 108 0 21 Apr 2018
Pathologies of Neural Models Make Interpretations Difficult Shi Feng Eric Wallace Alvin Grissom II Mohit Iyyer Pedro Rodriguez Jordan L. Boyd-Graber AAML FAtt 97 322 0 20 Apr 2018
Toward Intelligent Autonomous Agents for Cyber Defense: Report of the 2017 Workshop by the North Atlantic Treaty Organization (NATO) Research Group IST-152-RTG Alexander Kott R. Thomas Martin Drašar Markus Kont A. Poylisher ... H. Harney Gregory Wehner A. Guarino Jana Komárková James Rowell 42 10 0 20 Apr 2018
Understanding Community Structure in Layered Neural Networks C. Watanabe Kaoru Hiramatsu K. Kashino 135 22 0 13 Apr 2018
Visual Analytics for Explainable Deep Learning Jaegul Choo Shixia Liu HAI XAI 75 237 0 07 Apr 2018
Explanations of model predictions with live and breakDown packages M. Staniak P. Biecek FAtt 61 118 0 05 Apr 2018
Enslaving the Algorithm: From a "Right to an Explanation" to a "Right to Better Decisions"? L. Edwards Michael Veale FaML AILaw 66 135 0 20 Mar 2018
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges Gabrielle Ras Marcel van Gerven W. Haselager XAI 113 220 0 20 Mar 2018