"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 / 4,307 papers shown

Title
Counterfactual States for Atari Agents via Generative Deep Learning Matthew Lyle Olson Lawrence Neal Fuxin Li Weng-Keen Wong CML 21 29 0 27 Sep 2019
Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging Luke Oakden-Rayner Jared A. Dunnmon G. Carneiro Christopher Ré OOD 38 373 0 27 Sep 2019
Interpreting Undesirable Pixels for Image Classification on Black-Box Models Sin-Han Kang Hong G Jung Seong-Whan Lee FAtt 19 3 0 27 Sep 2019
Towards Explainable Artificial Intelligence Wojciech Samek K. Müller XAI 32 437 0 26 Sep 2019
Interpretable Models for Understanding Immersive Simulations Nicholas Hoernle Yaákov Gal Barbara J. Grosz Leilah Lyons Ada Ren Andee Rubin 24 4 0 24 Sep 2019
Deep Convolutions for In-Depth Automated Rock Typing E. E. Baraboshkin L. Ismailova D. Orlov E. Zhukovskaya G. Kalmykov O. V. Khotylev E. Baraboshkin D. Koroteev 33 84 0 23 Sep 2019
FACE: Feasible and Actionable Counterfactual Explanations Rafael Poyiadzi Kacper Sokol Raúl Santos-Rodríguez T. D. Bie Peter A. Flach 11 365 0 20 Sep 2019
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models Eric Wallace Jens Tuyls Junlin Wang Sanjay Subramanian Matt Gardner Sameer Singh MILM 28 137 0 19 Sep 2019
Representation Learning for Electronic Health Records W. Weng Peter Szolovits 36 19 0 19 Sep 2019
Slices of Attention in Asynchronous Video Job Interviews Léo Hemamou G. Felhi Jean-Claude Martin Chloé Clavel 13 20 0 19 Sep 2019
Large-scale representation learning from visually grounded untranscribed speech Gabriel Ilharco Yuan Zhang Jason Baldridge SSL 27 60 0 19 Sep 2019
Semantically Interpretable Activation Maps: what-where-how explanations within CNNs Diego Marcos Sylvain Lobry D. Tuia FAtt MILM 25 26 0 18 Sep 2019
X-ToM: Explaining with Theory-of-Mind for Gaining Justified Human Trust Arjun Reddy Akula Changsong Liu Sari Saba-Sadiya Hongjing Lu S. Todorovic J. Chai Song-Chun Zhu 24 18 0 15 Sep 2019
Towards Safe Machine Learning for CPS: Infer Uncertainty from Training Data Xiaozhe Gu Arvind Easwaran 13 29 0 11 Sep 2019
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey Jérôme Rony Soufiane Belharbi Jose Dolz Ismail Ben Ayed Luke McCaffrey Eric Granger 34 70 0 08 Sep 2019
DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning Theo Jaunet Romain Vuillemot Christian Wolf HAI 18 36 0 06 Sep 2019
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data Dylan Slack Sorelle A. Friedler Emile Givental FaML 32 54 0 24 Aug 2019
TabNet: Attentive Interpretable Tabular Learning Sercan Ö. Arik Tomas Pfister LMTD 55 1,293 0 20 Aug 2019
Visualizing Image Content to Explain Novel Image Discovery Jake H. Lee K. Wagstaff 27 3 0 14 Aug 2019
Regional Tree Regularization for Interpretability in Black Box Models Mike Wu S. Parbhoo M. C. Hughes R. Kindle Leo Anthony Celi Maurizio Zazzi Volker Roth Finale Doshi-Velez 15 37 0 13 Aug 2019
LoRMIkA: Local rule-based model interpretability with k-optimal associations Dilini Sewwandi Rajapaksha Christoph Bergmeir Wray Buntine 35 31 0 11 Aug 2019
Advocacy Learning: Learning through Competition and Class-Conditional Representations Ian Fox Jenna Wiens SSL 25 2 0 07 Aug 2019
NeuroMask: Explaining Predictions of Deep Neural Networks through Mask Learning M. Alzantot Amy Widdicombe S. Julier Mani B. Srivastava AAML FAtt 26 3 0 05 Aug 2019
Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models Daniel Omeiza Skyler Speakman C. Cintas Komminist Weldemariam FAtt 22 216 0 03 Aug 2019
Machine Learning at the Network Edge: A Survey M. G. Sarwar Murshed Chris Murphy Daqing Hou Nazar Khan Ganesh Ananthanarayanan Faraz Hussain 38 378 0 31 Jul 2019
LassoNet: A Neural Network with Feature Sparsity Ismael Lemhadri Feng Ruan L. Abraham Robert Tibshirani 41 122 0 29 Jul 2019
Visual Interaction with Deep Learning Models through Collaborative Semantic Inference Sebastian Gehrmann Hendrik Strobelt Robert Krüger Hanspeter Pfister Alexander M. Rush HAI 21 57 0 24 Jul 2019
Interpretable and Steerable Sequence Learning via Prototypes Yao Ming Panpan Xu Huamin Qu Liu Ren AI4TS 12 138 0 23 Jul 2019
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning Fan Yang Mengnan Du Xia Hu XAI ELM 32 67 0 16 Jul 2019
Technical Report: Partial Dependence through Stratification T. Parr James D. Wilson 11 2 0 15 Jul 2019
A study on the Interpretability of Neural Retrieval Models using DeepSHAP Zeon Trevor Fernando Jaspreet Singh Avishek Anand FAtt AAML 24 68 0 15 Jul 2019
Metamorphic Testing of a Deep Learning based Forecaster Anurag Dwarakanath Manish Ahuja Sanjay Podder Silja Vinu Arijit Naskar M. Koushik AI4TS 16 9 0 13 Jul 2019
A Systematic Mapping Study on Testing of Machine Learning Programs S. Sherin Muhammad Uzair Khan Muhammad Zohaib Z. Iqbal 30 13 0 11 Jul 2019
Aerial Animal Biometrics: Individual Friesian Cattle Recovery and Visual Identification via an Autonomous UAV with Onboard Deep Inference William Andrew C. Greatwood T. Burghardt 22 52 0 11 Jul 2019
The What-If Tool: Interactive Probing of Machine Learning Models James Wexler Mahima Pushkarna Tolga Bolukbasi Martin Wattenberg F. Viégas Jimbo Wilson VLM 57 484 0 09 Jul 2019
On the Semantic Interpretability of Artificial Intelligence Models V. S. Silva André Freitas Siegfried Handschuh AI4CE 25 8 0 09 Jul 2019
The Price of Interpretability Dimitris Bertsimas A. Delarue Patrick Jaillet Sébastien Martin 23 33 0 08 Jul 2019
A Human-Grounded Evaluation of SHAP for Alert Processing Hilde J. P. Weerts Werner van Ipenburg Mykola Pechenizkiy FAtt 11 70 0 07 Jul 2019
Generative Counterfactual Introspection for Explainable Deep Learning Shusen Liu B. Kailkhura Donald Loveland Yong Han 25 90 0 06 Jul 2019
Global Aggregations of Local Explanations for Black Box models I. V. D. Linden H. Haned Evangelos Kanoulas FAtt 27 63 0 05 Jul 2019
Automating Distributed Tiered Storage Management in Cluster Computing H. Herodotou E. Kakoulli 21 24 0 04 Jul 2019
Interpretable Counterfactual Explanations Guided by Prototypes A. V. Looveren Janis Klaise FAtt 29 380 0 03 Jul 2019
A Debiased MDI Feature Importance Measure for Random Forests Xiao Li Yu Wang Sumanta Basu Karl Kumbier Bin Yu 27 83 0 26 Jun 2019
DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems Muhammad Rehman Zafar N. Khan FAtt 14 153 0 24 Jun 2019
Generating Counterfactual and Contrastive Explanations using SHAP Shubham Rathi 24 56 0 21 Jun 2019
Machine Learning Testing: Survey, Landscapes and Horizons Jie M. Zhang Mark Harman Lei Ma Yang Liu VLM AILaw 39 741 0 19 Jun 2019
Incorporating Priors with Feature Attribution on Text Classification Frederick Liu Besim Avci FAtt FaML 36 120 0 19 Jun 2019
MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning Marko Vasic Andrija Petrović Kaiyuan Wang Mladen Nikolic Rishabh Singh S. Khurshid OffRL MoE 20 23 0 16 Jun 2019
Yoga-Veganism: Correlation Mining of Twitter Health Data Tunazzina Islam 8 22 0 15 Jun 2019
ML-LOO: Detecting Adversarial Examples with Feature Attribution Puyudi Yang Jianbo Chen Cho-Jui Hsieh Jane-ling Wang Michael I. Jordan AAML 22 101 0 08 Jun 2019