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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 / 4,968 papers shown

Title
A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration M. Cavallo Çağatay Demiralp 55 55 0 28 Nov 2018
What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems O. Lahav Nicholas Mastronarde M. Schaar 64 30 0 27 Nov 2018
Abduction-Based Explanations for Machine Learning Models Alexey Ignatiev Nina Narodytska Sasha Rubin FAtt 65 226 0 26 Nov 2018
Predicting Language Recovery after Stroke with Convolutional Networks on Stitched MRI Yusuf H. Roohani Noor Sajid Pranava Madhyastha Cathy J. Price T. Hope 19 5 0 26 Nov 2018
Attention, Please! Adversarial Defense via Activation Rectification and Preservation Shangxi Wu Jitao Sang Kaiyuan Xu Jiaming Zhang Jian Yu AAML 52 7 0 24 Nov 2018
Interpretable Convolutional Filters with SincNet Mirco Ravanelli Yoshua Bengio 93 107 0 23 Nov 2018
Representer Point Selection for Explaining Deep Neural Networks Chih-Kuan Yeh Joon Sik Kim Ian En-Hsu Yen Pradeep Ravikumar TDI 103 254 0 23 Nov 2018
State of the Art in Fair ML: From Moral Philosophy and Legislation to Fair Classifiers Elias Baumann J. L. Rumberger FaML 38 4 0 20 Nov 2018
Explain to Fix: A Framework to Interpret and Correct DNN Object Detector Predictions Denis A. Gudovskiy Alec Hodgkinson Takuya Yamaguchi Yasunori Ishii Sotaro Tsukizawa FAtt 74 13 0 19 Nov 2018
On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection Vivian Lai Chenhao Tan 78 380 0 19 Nov 2018
Explicit Bias Discovery in Visual Question Answering Models Varun Manjunatha Nirat Saini L. Davis CML FAtt 67 93 0 19 Nov 2018
Understanding Learned Models by Identifying Important Features at the Right Resolution Kyubin Lee Akshay Sood M. Craven 44 8 0 18 Nov 2018
Interpretable Credit Application Predictions With Counterfactual Explanations Rory Mc Grath Luca Costabello Chan Le Van Paul Sweeney F. Kamiab Zhao Shen Freddy Lecue FAtt 81 109 0 13 Nov 2018
TED: Teaching AI to Explain its Decisions Michael Hind Dennis L. Wei Murray Campbell Noel Codella Amit Dhurandhar Aleksandra Mojsilović Karthikeyan N. Ramamurthy Kush R. Varshney 80 111 0 12 Nov 2018
Characterizing machine learning process: A maturity framework Rama Akkiraju Vibha Sinha Anbang Xu J. Mahmud Pritam Gundecha Zhe Liu Xiaotong Liu John Schumacher 74 60 0 12 Nov 2018
Correction of AI systems by linear discriminants: Probabilistic foundations A. N. Gorban A. Golubkov Bogdan Grechuk E. M. Mirkes I. Tyukin 32 63 0 11 Nov 2018
A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective Yuji Roh A. Mishra Steven Euijong Whang 84 685 0 08 Nov 2018
Contrastive Explanation: A Structural-Model Approach Tim Miller CML 75 167 0 07 Nov 2018
Explaining Deep Learning Models - A Bayesian Non-parametric Approach Wenbo Guo Sui Huang Yunzhe Tao Masashi Sugiyama Lin Lin BDL 48 47 0 07 Nov 2018
YASENN: Explaining Neural Networks via Partitioning Activation Sequences Yaroslav Zharov Denis Korzhenkov J. Lyu Alexander Tuzhilin FAtt AAML 23 6 0 07 Nov 2018
Deep Weighted Averaging Classifiers Dallas Card Michael J.Q. Zhang Hao Tang 94 41 0 06 Nov 2018
Progressive Disclosure: Designing for Effective Transparency Aaron Springer Ling Huang 65 16 0 06 Nov 2018
"I had a solid theory before but it's falling apart": Polarizing Effects of Algorithmic Transparency Aaron Springer S. Whittaker 32 6 0 06 Nov 2018
Explaining Explanations in AI Brent Mittelstadt Chris Russell Sandra Wachter XAI 122 666 0 04 Nov 2018
What evidence does deep learning model use to classify Skin Lesions? Xiaoxiao Li Junyan Wu Eric Z. Chen Hongda Jiang 63 9 0 02 Nov 2018
Towards Explainable NLP: A Generative Explanation Framework for Text Classification Hui Liu Qingyu Yin William Yang Wang 118 148 0 01 Nov 2018
SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning Daoming Lyu Fangkai Yang Bo Liu Steven M. Gustafson OffRL 96 152 0 31 Oct 2018
Multimodal Machine Learning for Automated ICD Coding Keyang Xu Mike Lam Jingzhi Pang Xin Gao Charlotte Band ... A. Khanna J. Cywinski K. Maheshwari P. Xie Eric Xing 77 109 0 31 Oct 2018
Compositional Attention Networks for Interpretability in Natural Language Question Answering Selvakumar Murugan Suriyadeepan Ramamoorthy Vaidheeswaran Archana Malaikannan Sankarasubbu 50 3 0 30 Oct 2018
Do Explanations make VQA Models more Predictable to a Human? Arjun Chandrasekaran Viraj Prabhu Deshraj Yadav Prithvijit Chattopadhyay Devi Parikh FAtt 139 97 0 29 Oct 2018
Learning and Interpreting Multi-Multi-Instance Learning Networks Alessandro Tibo M. Jaeger P. Frasconi AI4CE 141 23 0 26 Oct 2018
Interpreting Black Box Predictions using Fisher Kernels Rajiv Khanna Been Kim Joydeep Ghosh Oluwasanmi Koyejo FAtt 88 104 0 23 Oct 2018
What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play Shi Feng Jordan L. Boyd-Graber HAI 82 130 0 23 Oct 2018
On The Stability of Interpretable Models Riccardo Guidotti Salvatore Ruggieri FAtt 64 10 0 22 Oct 2018
Finding Average Regret Ratio Minimizing Set in Database Sepanta Zeighami Raymond Chi-Wing Wong 29 16 0 18 Oct 2018
Explaining Machine Learning Models using Entropic Variable Projection François Bachoc Fabrice Gamboa Max Halford Jean-Michel Loubes Laurent Risser FAtt 63 5 0 18 Oct 2018
Explaining Black Boxes on Sequential Data using Weighted Automata Stéphane Ayache Rémi Eyraud Noé Goudian 69 44 0 12 Oct 2018
Feature Selection using Stochastic Gates Yutaro Yamada Ofir Lindenbaum S. Negahban Y. Kluger 176 42 0 09 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 90 78 0 09 Oct 2018
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values Julius Adebayo Justin Gilmer Ian Goodfellow Been Kim FAtt AAML 76 129 0 08 Oct 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 179 1,973 0 08 Oct 2018
On the Art and Science of Machine Learning Explanations Patrick Hall FAtt XAI 92 30 0 05 Oct 2018
Local Interpretable Model-agnostic Explanations of Bayesian Predictive Models via Kullback-Leibler Projections Tomi Peltola FAtt BDL 70 40 0 05 Oct 2018
Projective Inference in High-dimensional Problems: Prediction and Feature Selection Juho Piironen Markus Paasiniemi Aki Vehtari 74 96 0 04 Oct 2018
Interpreting Layered Neural Networks via Hierarchical Modular Representation C. Watanabe 84 19 0 03 Oct 2018
Stakeholders in Explainable AI Alun D. Preece Daniel Harborne Dave Braines Richard J. Tomsett Supriyo Chakraborty 55 157 0 29 Sep 2018
Explainable Black-Box Attacks Against Model-based Authentication Washington Garcia Joseph I. Choi S. K. Adari S. Jha Kevin R. B. Butler 92 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 65 41 0 27 Sep 2018
Understanding Convolutional Neural Networks for Text Classification Alon Jacovi Oren Sar Shalom Yoav Goldberg FAtt 75 221 0 21 Sep 2018
Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure Besmira Nushi Ece Kamar Eric Horvitz 58 142 0 19 Sep 2018