"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 / 4,267 papers shown

Title
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 22 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
Contrastive Explanations with Local Foil Trees J. V. D. Waa M. Robeer J. Diggelen Matthieu J. S. Brinkhuis Mark Antonius Neerincx FAtt 21 82 0 19 Jun 2018
RISE: Randomized Input Sampling for Explanation of Black-box Models Vitali Petsiuk Abir Das Kate Saenko FAtt 76 1,153 0 19 Jun 2018
Instance-Level Explanations for Fraud Detection: A Case Study Dennis Collaris L. M. Vink J. V. Wijk 31 31 0 19 Jun 2018
Biased Embeddings from Wild Data: Measuring, Understanding and Removing Adam Sutton Thomas Lansdall-Welfare N. Cristianini 26 23 0 16 Jun 2018
Right for the Right Reason: Training Agnostic Networks Sen Jia Thomas Lansdall-Welfare N. Cristianini FaML 17 26 0 16 Jun 2018
Hierarchical interpretations for neural network predictions Chandan Singh W. James Murdoch Bin Yu 31 145 0 14 Jun 2018
Understanding Patch-Based Learning by Explaining Predictions Christopher J. Anders G. Montavon Wojciech Samek K. Müller UQCV FAtt 33 6 0 11 Jun 2018
Assessing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour Emilia Gómez Carlos Castillo V. Charisi V. Dahl G. Deco ... Núria Sebastián Xavier Serra Joan Serrà Songül Tolan Karina Vold 11 11 0 07 Jun 2018
Performance Metric Elicitation from Pairwise Classifier Comparisons G. Hiranandani Shant Boodaghians R. Mehta Oluwasanmi Koyejo 19 14 0 05 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 40 1,842 0 31 May 2018
Human-in-the-Loop Interpretability Prior Isaac Lage A. Ross Been Kim S. Gershman Finale Doshi-Velez 32 120 0 29 May 2018
Lightly-supervised Representation Learning with Global Interpretability M. A. Valenzuela-Escarcega Ajay Nagesh Mihai Surdeanu SSL 17 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 31 435 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 36 251 0 28 May 2018
"Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Users Stefano Teso Kristian Kersting FAtt HAI 25 12 0 22 May 2018
Defoiling Foiled Image Captions Pranava Madhyastha Josiah Wang Lucia Specia 27 9 0 16 May 2018
Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models Jacob R. Kauffmann K. Müller G. Montavon DRL 42 96 0 16 May 2018
SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines Roy Schwartz Sam Thomson Noah A. Smith 30 24 0 15 May 2018
Did the Model Understand the Question? Pramod Kaushik Mudrakarta Ankur Taly Mukund Sundararajan Kedar Dhamdhere ELM OOD FAtt 27 196 0 14 May 2018
Behavior Analysis of NLI Models: Uncovering the Influence of Three Factors on Robustness V. Carmona Jeff Mitchell Sebastian Riedel 27 44 0 11 May 2018
A Symbolic Approach to Explaining Bayesian Network Classifiers Andy Shih Arthur Choi Adnan Darwiche FAtt 13 237 0 09 May 2018
Explainable Recommendation: A Survey and New Perspectives Yongfeng Zhang Xu Chen XAI LRM 52 866 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 31 239 0 25 Apr 2018
Understanding Community Structure in Layered Neural Networks C. Watanabe Kaoru Hiramatsu K. Kashino 13 22 0 13 Apr 2018
Visual Analytics for Explainable Deep Learning Jaegul Choo Shixia Liu HAI XAI 14 235 0 07 Apr 2018
Enslaving the Algorithm: From a "Right to an Explanation" to a "Right to Better Decisions"? L. Edwards Michael Veale FaML AILaw 20 134 0 20 Mar 2018
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges Gabrielle Ras Marcel van Gerven W. Haselager XAI 17 217 0 20 Mar 2018
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning Nicolas Papernot Patrick McDaniel OOD AAML 13 503 0 13 Mar 2018
Explaining Black-box Android Malware Detection Marco Melis Davide Maiorca Battista Biggio Giorgio Giacinto Fabio Roli AAML FAtt 9 43 0 09 Mar 2018
The Challenge of Crafting Intelligible Intelligence Daniel S. Weld Gagan Bansal 26 241 0 09 Mar 2018
Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification Chengliang Yang Anand Rangarajan Sanjay Ranka FAtt 21 140 0 07 Mar 2018
Explain Yourself: A Natural Language Interface for Scrutable Autonomous Robots Javier Chiyah-Garcia D. A. Robb Xingkun Liu A. Laskov P. Patrón H. Hastie 24 25 0 06 Mar 2018
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation Jianbo Chen Le Song Martin J. Wainwright Michael I. Jordan MLT FAtt 44 561 0 21 Feb 2018
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections Xin Zhang Armando Solar-Lezama Rishabh Singh FAtt 27 63 0 21 Feb 2018
Teaching Categories to Human Learners with Visual Explanations Oisin Mac Aodha Shihan Su Yuxin Chen Pietro Perona Yisong Yue 21 70 0 20 Feb 2018
Explainable Prediction of Medical Codes from Clinical Text J. Mullenbach Sarah Wiegreffe J. Duke Jimeng Sun Jacob Eisenstein FAtt 29 567 0 15 Feb 2018