GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints

19 April 2022

Mathias Kraus

Papers citing "GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints"

21 / 21 papers shown

Title
Explainable AI for tailored electricity consumption feedback -- an experimental evaluation of visualizations Jacqueline Wastensteiner T. Weiß Felix Haag K. Hopf 47 11 0 24 Aug 2022
Critical Empirical Study on Black-box Explanations in AI Jean-Marie John-Mathews 34 6 0 29 Sep 2021
Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Work Through Explainable Artificial Intelligence Max Schemmer Niklas Kühl G. Satzger 56 14 0 28 Sep 2021
Predicting Census Survey Response Rates With Parsimonious Additive Models and Structured Interactions Shibal Ibrahim P. Radchenko E. Ben-David Rahul Mazumder 414 2 0 24 Aug 2021
Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification Agus Sudjianto William Knauth Rahul Singh Zebin Yang Aijun Zhang FAtt 61 46 0 08 Nov 2020
Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing Nijat Mehdiyev Peter Fettke 55 11 0 22 Sep 2020
A Technique for Determining Relevance Scores of Process Activities using Graph-based Neural Networks M. Stierle Sven Weinzierl Maximilian Harl Martin Matzner 30 17 0 07 Aug 2020
How Interpretable and Trustworthy are GAMs? C. Chang S. Tan Benjamin J. Lengerich Anna Goldenberg R. Caruana FAtt 109 79 0 11 Jun 2020
Neural Additive Models: Interpretable Machine Learning with Neural Nets Rishabh Agarwal Levi Melnick Nicholas Frosst Xuezhou Zhang Ben Lengerich R. Caruana Geoffrey E. Hinton 92 417 0 29 Apr 2020
GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions Zebin Yang Aijun Zhang Agus Sudjianto FAtt 150 128 0 16 Mar 2020
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 121 6,269 0 22 Oct 2019
InterpretML: A Unified Framework for Machine Learning Interpretability Harsha Nori Samuel Jenkins Paul Koch R. Caruana AI4CE 153 488 0 19 Sep 2019
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 568 14 0 23 Aug 2019
Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences Mathias Kraus Stefan Feuerriegel 52 110 0 11 Jul 2019
Enhancing Explainability of Neural Networks through Architecture Constraints Zebin Yang Aijun Zhang Agus Sudjianto AAML 40 87 0 12 Jan 2019
Techniques for Interpretable Machine Learning Mengnan Du Ninghao Liu Xia Hu FaML 84 1,092 0 31 Jul 2018
Deep learning in business analytics and operations research: Models, applications and managerial implications Mathias Kraus Stefan Feuerriegel A. Oztekin 62 291 0 28 Jun 2018
Explainable Neural Networks based on Additive Index Models J. Vaughan Agus Sudjianto Erind Brahimi Jie Chen V. Nair 57 106 0 05 Jun 2018
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 247 4,272 0 22 Jun 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,027 0 16 Feb 2016
A systematic comparison of supervised classifiers D. R. Amancio C. H. Comin Dalcimar Casanova G. Travieso Odemir M. Bruno F. Rodrigues L. D. F. Costa 66 206 0 17 Oct 2013