Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

5 November 2015

Papers citing "Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model"

50 / 244 papers shown

Title
"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations Himabindu Lakkaraju Osbert Bastani 90 258 0 15 Nov 2019
Coverage-based Outlier Explanation Yue Wu Leman Akoglu Ian Davidson 16 1 0 06 Nov 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 182 6,380 0 22 Oct 2019
A Decision-Theoretic Approach for Model Interpretability in Bayesian Framework Homayun Afrabandpey Tomi Peltola Juho Piironen Aki Vehtari Samuel Kaski 77 3 0 21 Oct 2019
Finding Interpretable Concept Spaces in Node Embeddings using Knowledge Bases Maximilian Idahl Megha Khosla Avishek Anand 33 10 0 11 Oct 2019
Modeling Conceptual Understanding in Image Reference Games Rodolfo Corona Stephan Alaniz Zeynep Akata 97 27 0 10 Oct 2019
MonoNet: Towards Interpretable Models by Learning Monotonic Features An-phi Nguyen María Rodríguez Martínez FAtt 60 13 0 30 Sep 2019
Model-Agnostic Linear Competitors -- When Interpretable Models Compete and Collaborate with Black-Box Models Hassan Rafique Tong Wang Qihang Lin 49 4 0 23 Sep 2019
SIRUS: Stable and Interpretable RUle Set for Classification Clément Bénard Gérard Biau Sébastien Da Veiga Erwan Scornet 56 9 0 19 Aug 2019
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI Erico Tjoa Cuntai Guan XAI 164 1,464 0 17 Jul 2019
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning Fan Yang Mengnan Du Helen Zhou XAI ELM 67 67 0 16 Jul 2019
A study on the Interpretability of Neural Retrieval Models using DeepSHAP Zeon Trevor Fernando Jaspreet Singh Avishek Anand FAtt AAML 65 68 0 15 Jul 2019
Optimal Explanations of Linear Models Dimitris Bertsimas A. Delarue Patrick Jaillet Sébastien Martin FAtt 38 2 0 08 Jul 2019
The Price of Interpretability Dimitris Bertsimas A. Delarue Patrick Jaillet Sébastien Martin 63 34 0 08 Jul 2019
Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations Rohan R. Paleja Andrew Silva Letian Chen Matthew C. Gombolay 98 33 0 14 Jun 2019
A hybrid machine learning framework for analyzing human decision making through learning preferences Mengzhuo Guo Qingpeng Zhang Xiuwu Liao Youhua Chen Daniel Dajun Zeng 93 8 0 04 Jun 2019
Ex-Twit: Explainable Twitter Mining on Health Data Tunazzina Islam FAtt 21 5 0 24 May 2019
Interpretability with Accurate Small Models Abhishek Ghose Balaraman Ravindran 110 1 0 04 May 2019
Interpretable multiclass classification by MDL-based rule lists Hugo Manuel Proença M. Leeuwen 59 48 0 01 May 2019
Optimal Sparse Decision Trees Xiyang Hu Cynthia Rudin Margo Seltzer 143 175 0 29 Apr 2019
Explainability in Human-Agent Systems A. Rosenfeld A. Richardson XAI 83 207 0 17 Apr 2019
A Categorisation of Post-hoc Explanations for Predictive Models John Mitros Brian Mac Namee XAI CML 28 1 0 04 Apr 2019
VINE: Visualizing Statistical Interactions in Black Box Models M. Britton FAtt 63 22 0 01 Apr 2019
Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making S. Aghaei Javad Azizi P. Vayanos FaML 87 180 0 25 Mar 2019
Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement Learning I. D. Rodriguez Taylor W. Killian Ivan Dario Jimenez Rodriguez Sung-Hyun Son Matthew C. Gombolay OffRL 85 12 0 22 Mar 2019
Neural Network Attributions: A Causal Perspective Aditya Chattopadhyay Piyushi Manupriya Anirban Sarkar V. Balasubramanian CML 96 146 0 06 Feb 2019
Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI Shane T. Mueller R. Hoffman W. Clancey Abigail Emrey Gary Klein XAI 76 285 0 05 Feb 2019
An Evaluation of the Human-Interpretability of Explanation Isaac Lage Emily Chen Jeffrey He Menaka Narayanan Been Kim Sam Gershman Finale Doshi-Velez FAtt XAI 135 159 0 31 Jan 2019
Fairwashing: the risk of rationalization Ulrich Aïvodji Hiromi Arai O. Fortineau Sébastien Gambs Satoshi Hara Alain Tapp FaML 70 147 0 28 Jan 2019
Quantifying Interpretability and Trust in Machine Learning Systems Philipp Schmidt F. Biessmann 56 115 0 20 Jan 2019
Explaining Explanations to Society Leilani H. Gilpin Cecilia Testart Nathaniel Fruchter Julius Adebayo XAI 118 35 0 19 Jan 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin Yu XAI HAI 211 1,459 0 14 Jan 2019
Improving the Interpretability of Deep Neural Networks with Knowledge Distillation Xuan Liu Xiaoguang Wang Stan Matwin HAI 73 101 0 28 Dec 2018
Interpretable Optimal Stopping D. Ciocan V. Mišić 68 44 0 18 Dec 2018
MLIC: A MaxSAT-Based framework for learning interpretable classification rules Dmitry Malioutov Kuldeep S. Meel 80 44 0 05 Dec 2018
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems Sina Mohseni Niloofar Zarei Eric D. Ragan 122 102 0 28 Nov 2018
A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration M. Cavallo Çağatay Demiralp 57 55 0 28 Nov 2018
How to improve the interpretability of kernel learning Jinwei Zhao Qizhou Wang Yufei Wang Yu Liu Zhenghao Shi Xinhong Hei FAtt 42 0 0 21 Nov 2018
An Overview of Computational Approaches for Interpretation Analysis Philipp Blandfort Jörn Hees D. Patton 49 2 0 09 Nov 2018
MCA-based Rule Mining Enables Interpretable Inference in Clinical Psychiatry Qingzhu Gao H. González P. Ahammad 21 3 0 26 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
Axiomatic Interpretability for Multiclass Additive Models Xuezhou Zhang S. Tan Paul Koch Yin Lou Urszula Chajewska R. Caruana FAtt AI4CE 55 3 0 22 Oct 2018
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals Andrew Cotter Heinrich Jiang S. Wang Taman Narayan Maya R. Gupta Seungil You Karthik Sridharan 90 158 0 11 Sep 2018
Grounding Visual Explanations Lisa Anne Hendricks Ronghang Hu Trevor Darrell Zeynep Akata FAtt 59 230 0 25 Jul 2018
Knowledge-based Transfer Learning Explanation Jiaoyan Chen Freddy Lecue Jeff Z. Pan Ian Horrocks Huajun Chen 58 42 0 22 Jul 2018
RuleMatrix: Visualizing and Understanding Classifiers with Rules Yao Ming Huamin Qu E. Bertini FAtt 77 215 0 17 Jul 2018
Rule Induction Partitioning Estimator Vincent Margot Jean-Patrick Baudry Frédéric Guilloux Olivier Wintenberger 52 4 0 12 Jul 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 92 272 0 01 Jun 2018
Boolean Decision Rules via Column Generation S. Dash Oktay Gunluk Dennis L. Wei 77 175 0 24 May 2018
A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models Tomáš Kliegr Š. Bahník Johannes Furnkranz 106 105 0 09 Apr 2018