Techniques for Interpretable Machine Learning

31 July 2018

Ninghao Liu

Papers citing "Techniques for Interpretable Machine Learning"

47 / 97 papers shown

Title
Modeling Techniques for Machine Learning Fairness: A Survey Mingyang Wan Daochen Zha Ninghao Liu Na Zou SyDa FaML 30 36 0 04 Nov 2021
Tree-based local explanations of machine learning model predictions, AraucanaXAI Enea Parimbelli G. Nicora Szymon Wilk W. Michalowski Riccardo Bellazzi 16 25 0 15 Oct 2021
LEMON: Explainable Entity Matching Nils Barlaug FAtt AAML 20 9 0 01 Oct 2021
The Impact of Machine Learning on 2D/3D Registration for Image-guided Interventions: A Systematic Review and Perspective Mathias Unberath Cong Gao Yicheng Hu Max Judish Russell H. Taylor Mehran Armand Robert Grupp 26 66 0 04 Aug 2021
A Survey on Graph-Based Deep Learning for Computational Histopathology David Ahmedt-Aristizabal M. Armin Simon Denman Clinton Fookes L. Petersson GNN AI4CE 19 108 0 01 Jul 2021
Fairness via Representation Neutralization Mengnan Du Subhabrata Mukherjee Guanchu Wang Ruixiang Tang Ahmed Hassan Awadallah Xia Hu 25 78 0 23 Jun 2021
Multivariate Data Explanation by Jumping Emerging Patterns Visualization Mário Popolin Neto F. Paulovich 32 7 0 21 Jun 2021
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts Gesina Schwalbe Bettina Finzel XAI 29 184 0 15 May 2021
Metamorphic Detection of Repackaged Malware S. Singh Gail E. Kaiser 21 8 0 27 Apr 2021
Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Models Mengnan Du Varun Manjunatha R. Jain Ruchi Deshpande Franck Dernoncourt Jiuxiang Gu Tong Sun Xia Hu 57 105 0 11 Mar 2021
Deep Learning for Android Malware Defenses: a Systematic Literature Review Yue Liu C. Tantithamthavorn Li Li Yepang Liu AAML 30 77 0 09 Mar 2021
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs Harini Suresh Kathleen M. Lewis John Guttag Arvind Satyanarayan FAtt 40 25 0 17 Feb 2021
Towards Designing Computer Vision-based Explainable-AI Solution: A Use Case of Livestock Mart Industry Devam Dave Het Naik Smiti Singhal Rudresh Dwivedi Pankesh Patel 15 1 0 08 Feb 2021
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs Harini Suresh Steven R. Gomez K. Nam Arvind Satyanarayan 34 126 0 24 Jan 2021
XAI-P-T: A Brief Review of Explainable Artificial Intelligence from Practice to Theory Nazanin Fouladgar Kary Främling XAI 10 4 0 17 Dec 2020
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 24 18 0 10 Dec 2020
Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations Wolfgang Stammer P. Schramowski Kristian Kersting FAtt 14 107 0 25 Nov 2020
Interpreting convolutional networks trained on textual data Reza Marzban Christopher Crick FAtt 27 3 0 20 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
Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines A. Konstantinov Lev V. Utkin FedML AI4CE 8 138 0 14 Oct 2020
Cause vs. Effect in Context-Sensitive Prediction of Business Process Instances Jens Brunk M. Stierle Leon Papke K. Revoredo Martin Matzner J. Becker 14 22 0 15 Jul 2020
Usefulness of interpretability methods to explain deep learning based plant stress phenotyping Koushik Nagasubramanian Asheesh K. Singh Arti Singh S. Sarkar Baskar Ganapathysubramanian FAtt 19 16 0 11 Jul 2020
Counterfactual explanation of machine learning survival models M. Kovalev Lev V. Utkin CML OffRL 27 19 0 26 Jun 2020
An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks Ruixiang Tang Mengnan Du Ninghao Liu Fan Yang Xia Hu AAML 18 184 0 15 Jun 2020
Adversarial Infidelity Learning for Model Interpretation Jian Liang Bing Bai Yuren Cao Kun Bai Fei-Yue Wang AAML 44 18 0 09 Jun 2020
Location, location, location: Satellite image-based real-estate appraisal Jan-Peter Kucklick Oliver Müller 20 5 0 04 Jun 2020
A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers Kevin Fauvel Véronique Masson Elisa Fromont AI4TS 44 17 0 29 May 2020
iCapsNets: Towards Interpretable Capsule Networks for Text Classification Zhengyang Wang Xia Hu Shuiwang Ji 6 11 0 16 May 2020
Explainable Matrix -- Visualization for Global and Local Interpretability of Random Forest Classification Ensembles Mário Popolin Neto F. Paulovich FAtt 33 88 0 08 May 2020
XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification Kevin Fauvel Elisa Fromont Véronique Masson P. Faverdin Alexandre Termier AI4TS 33 41 0 07 May 2020
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition Mahsan Nourani Chiradeep Roy Tahrima Rahman Eric D. Ragan Nicholas Ruozzi Vibhav Gogate AAML 12 17 0 05 May 2020
Post-hoc explanation of black-box classifiers using confident itemsets M. Moradi Matthias Samwald 57 97 0 05 May 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
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation Raha Moraffah Mansooreh Karami Ruocheng Guo A. Raglin Huan Liu CML ELM XAI 27 213 0 09 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
Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy B. Shneiderman 16 673 0 10 Feb 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 38 300 0 08 Jan 2020
LionForests: Local Interpretation of Random Forests Ioannis Mollas Nick Bassiliades I. Vlahavas Grigorios Tsoumakas 11 12 0 20 Nov 2019
A Decision-Theoretic Approach for Model Interpretability in Bayesian Framework Homayun Afrabandpey Tomi Peltola Juho Piironen Aki Vehtari Samuel Kaski 17 3 0 21 Oct 2019
Techniques for Automated Machine Learning Yi-Wei Chen Qingquan Song Xia Hu 8 48 0 21 Jul 2019
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning Fan Yang Mengnan Du Xia Hu XAI ELM 27 66 0 16 Jul 2019
Deep Learning for Spatio-Temporal Data Mining: A Survey Senzhang Wang Jiannong Cao Philip S. Yu AI4TS 26 549 0 11 Jun 2019
Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding Ninghao Liu Qiaoyu Tan Yuening Li Hongxia Yang Jingren Zhou Xia Hu 22 87 0 25 May 2019
Fake News Early Detection: An Interdisciplinary Study Xinyi Zhou Atishay Jain V. Phoha R. Zafarani 14 206 0 26 Apr 2019
Deep Representation Learning for Social Network Analysis Qiaoyu Tan Ninghao Liu Xia Hu AI4TS GNN 24 99 0 18 Apr 2019
A Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees Klaus Broelemann Gjergji Kasneci 16 20 0 25 Sep 2018
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017