Interpretable machine learning: definitions, methods, and applications

14 January 2019

Papers citing "Interpretable machine learning: definitions, methods, and applications"

50 / 329 papers shown

Title
Model Interpretability through the Lens of Computational Complexity Pablo Barceló Mikaël Monet Jorge A. Pérez Bernardo Subercaseaux 124 94 0 23 Oct 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 20 397 0 19 Oct 2020
Understanding Information Processing in Human Brain by Interpreting Machine Learning Models Ilya Kuzovkin HAI 8 2 0 17 Oct 2020
Interpretable Neural Networks for Panel Data Analysis in Economics Yucheng Yang Zhong Zheng Weinan E 15 6 0 11 Oct 2020
Computational analysis of pathological image enables interpretable prediction for microsatellite instability Jin Zhu Wangwei Wu Yuting Zhang Shiyun Lin Yukang Jiang Rui-Feng Liu Xueqin Wang 15 9 0 07 Oct 2020
SOAR: Simultaneous Or of And Rules for Classification of Positive & Negative Classes Elena Khusainova Emily Dodwell Ritwik Mitra 19 2 0 25 Aug 2020
Stable discovery of interpretable subgroups via calibration in causal studies Raaz Dwivedi Yan Shuo Tan Briton Park Mian Wei Kevin Horgan D. Madigan Bin Yu CML 6 29 0 23 Aug 2020
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises S. Kevin Zhou H. Greenspan Christos Davatzikos James S. Duncan Bram van Ginneken A. Madabhushi Jerry L. Prince Daniel Rueckert Ronald M. Summers 55 624 0 02 Aug 2020
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies A. Markus J. Kors P. Rijnbeek 17 455 0 31 Jul 2020
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models Catarina Moreira Yu-Liang Chou M. Velmurugan Chun Ouyang Renuka Sindhgatta P. Bruza 36 57 0 21 Jul 2020
Drug discovery with explainable artificial intelligence José Jiménez-Luna F. Grisoni G. Schneider 30 625 0 01 Jul 2020
Counterfactual explanation of machine learning survival models M. Kovalev Lev V. Utkin CML OffRL 27 19 0 26 Jun 2020
Fast, Optimal, and Targeted Predictions using Parametrized Decision Analysis Daniel R. Kowal 6 0 0 23 Jun 2020
Deep Residual Mixture Models Perttu Hämäläinen Martin Trapp Tuure Saloheimo Arno Solin 28 8 0 22 Jun 2020
Efficient nonparametric statistical inference on population feature importance using Shapley values B. Williamson Jean Feng FAtt 13 70 0 16 Jun 2020
Explaining Predictions by Approximating the Local Decision Boundary G. Vlassopoulos T. Erven Henry Brighton Vlado Menkovski FAtt 22 8 0 14 Jun 2020
Interpreting CNN for Low Complexity Learned Sub-pixel Motion Compensation in Video Coding L. Murn Saverio G. Blasi Alan F. Smeaton Noel E. O'Connor M. Mrak 22 11 0 11 Jun 2020
Interpretable Classification of Bacterial Raman Spectra with Knockoff Wavelets Charmaine Chia Matteo Sesia Chi-Sing Ho S. Jeffrey J. Dionne Emmanuel J. Candès R. Howe 17 5 0 08 Jun 2020
A Semiparametric Approach to Interpretable Machine Learning Numair Sani Jaron J. R. Lee Razieh Nabi I. Shpitser 15 6 0 08 Jun 2020
ExKMC: Expanding Explainable $k$ -Means Clustering Nave Frost Michal Moshkovitz Cyrus Rashtchian 11 55 0 03 Jun 2020
Fractional ridge regression: a fast, interpretable reparameterization of ridge regression Ariel S. Rokem Kendrick Norris Kay 13 42 0 07 May 2020
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds M. Kovalev Lev V. Utkin AAML 21 31 0 05 May 2020
SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models Lev V. Utkin M. Kovalev E. Kasimov 8 10 0 05 May 2020
Post-hoc explanation of black-box classifiers using confident itemsets M. Moradi Matthias Samwald 57 97 0 05 May 2020
Construction and Elicitation of a Black Box Model in the Game of Bridge V. Ventos Daniel A. Braun Colin Deheeger Jean Pierre Desmoulins Jean Baptiste Fantun Swann Legras Alexis Rimbaud C. Rouveirol H. Soldano Solène Thépaut 4 0 0 04 May 2020
Rationalizing Medical Relation Prediction from Corpus-level Statistics Zhen Wang Jennifer A Lee Simon M. Lin Huan Sun OOD 6 4 0 02 May 2020
Interpretable Random Forests via Rule Extraction Clément Bénard Gérard Biau Sébastien Da Veiga Erwan Scornet 12 58 0 29 Apr 2020
Unifying Neural Learning and Symbolic Reasoning for Spinal Medical Report Generation Zhongyi Han B. Wei Yilong Yin Shuo Li MedIm 30 40 0 28 Apr 2020
Dynamic Predictions of Postoperative Complications from Explainable, Uncertainty-Aware, and Multi-Task Deep Neural Networks B. Shickel Tyler J. Loftus M. Ruppert Gilbert R. Upchurch T. Ozrazgat-Baslanti Parisa Rashidi A. Bihorac UQCV AI4CE 13 13 0 27 Apr 2020
Adversarial Attacks and Defenses: An Interpretation Perspective Ninghao Liu Mengnan Du Ruocheng Guo Huan Liu Xia Hu AAML 26 8 0 23 Apr 2020
Investigating similarities and differences between South African and Sierra Leonean school outcomes using Machine Learning Henry Wandera Vukosi Marivate David M. Sengeh 6 1 0 22 Apr 2020
Human Evaluation of Interpretability: The Case of AI-Generated Music Knowledge Haizi Yu Heinrich Taube James A. Evans L. Varshney 11 5 0 15 Apr 2020
Structure-preserving neural networks Quercus Hernandez Alberto Badías D. González Francisco Chinesta Elías Cueto PINN 22 69 0 09 Apr 2020
Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness? Alon Jacovi Yoav Goldberg XAI 17 567 0 07 Apr 2020
A New Method to Compare the Interpretability of Rule-based Algorithms Vincent Margot G. Luta FAtt 12 17 0 03 Apr 2020
Born-Again Tree Ensembles Thibaut Vidal Toni Pacheco Maximilian Schiffer 62 53 0 24 Mar 2020
Interpretable machine learning models: a physics-based view Ion Matei Johan de Kleer C. Somarakis R. Rai John S. Baras PINN AI4CE 16 1 0 22 Mar 2020
SurvLIME: A method for explaining machine learning survival models M. Kovalev Lev V. Utkin E. Kasimov 108 89 0 18 Mar 2020
GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions Zebin Yang Aijun Zhang Agus Sudjianto FAtt 19 126 0 16 Mar 2020
Transformation Importance with Applications to Cosmology Chandan Singh Wooseok Ha F. Lanusse V. Boehm Jia-Wei Liu Bin Yu AI4CE 17 11 0 04 Mar 2020
Interpretability of machine learning based prediction models in healthcare Gregor Stiglic Primož Kocbek Nino Fijačko Marinka Zitnik K. Verbert Leona Cilar AI4CE 35 372 0 20 Feb 2020
Algorithmic Recourse: from Counterfactual Explanations to Interventions Amir-Hossein Karimi Bernhard Schölkopf Isabel Valera CML 24 337 0 14 Feb 2020
Self-explaining AI as an alternative to interpretable AI Daniel C. Elton 8 56 0 12 Feb 2020
Decisions, Counterfactual Explanations and Strategic Behavior Stratis Tsirtsis Manuel Gomez Rodriguez 27 58 0 11 Feb 2020
Making Logic Learnable With Neural Networks Tobias Brudermueller Dennis L. Shung A. Stanley Johannes Stegmaier Smita Krishnaswamy NAI 10 2 0 10 Feb 2020
Generating Interpretable Poverty Maps using Object Detection in Satellite Images Kumar Ayush Burak Uzkent Marshall Burke David B. Lobell Stefano Ermon 25 82 0 05 Feb 2020
Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review Khansa Rasheed A. Qayyum Junaid Qadir Shobi Sivathamboo P. Kwan L. Kuhlmann T. O'Brien Adeel Razi 37 220 0 04 Feb 2020
Interpretability of Blackbox Machine Learning Models through Dataview Extraction and Shadow Model creation Rupam Patir Shubham Singhal C. Anantaram Vikram Goyal 9 0 0 02 Feb 2020
Adequate and fair explanations Nicholas M. Asher Soumya Paul Chris Russell 35 9 0 21 Jan 2020
Multi-Objective Genetic Programming for Manifold Learning: Balancing Quality and Dimensionality Andrew Lensen Mengjie Zhang Bing Xue 6 15 0 05 Jan 2020