Designing Inherently Interpretable Machine Learning Models

2 November 2021

Papers citing "Designing Inherently Interpretable Machine Learning Models"

21 / 21 papers shown

Title
Explainable post-training bias mitigation with distribution-based fairness metrics Ryan Franks A. Miroshnikov 37 0 0 01 Apr 2025
Inherently Interpretable Tree Ensemble Learning Zebin Yang Agus Sudjianto Xiaoming Li Aijun Zhang AI4CE 28 0 0 24 Oct 2024
Less Discriminatory Alternative and Interpretable XGBoost Framework for Binary Classification Andrew Pangia Agus Sudjianto Aijun Zhang Taufiquar Khan FaML 33 1 0 24 Oct 2024
Space-scale Exploration of the Poor Reliability of Deep Learning Models: the Case of the Remote Sensing of Rooftop Photovoltaic Systems Gabriel Kasmi L. Dubus Yves-Marie Saint Drenan Philippe Blanc 40 0 0 31 Jul 2024
CHILLI: A data context-aware perturbation method for XAI Saif Anwar Nathan Griffiths A. Bhalerao T. Popham 44 0 0 10 Jul 2024
Are Logistic Models Really Interpretable? Danial Dervovic Freddy Lecue Nicolas Marchesotti Daniele Magazzeni 35 0 0 19 Jun 2024
Explainable Interface for Human-Autonomy Teaming: A Survey Xiangqi Kong Yang Xing Antonios Tsourdos Ziyue Wang Weisi Guo Adolfo Perrusquía Andreas Wikander 43 3 0 04 May 2024
Opening the Black Box: Towards inherently interpretable energy data imputation models using building physics insight Antonio Liguori Matias Quintana Chun Fu Clayton Miller J. Frisch C. Treeck AI4CE 10 5 0 28 Nov 2023
A Comprehensive Review on Financial Explainable AI Wei Jie Yeo Wihan van der Heever Rui Mao Min Zhang Ranjan Satapathy G. Mengaldo XAI AI4TS 32 15 0 21 Sep 2023
Interpreting and generalizing deep learning in physics-based problems with functional linear models Amirhossein Arzani Lingxiao Yuan P. Newell Bei Wang AI4CE 31 7 0 10 Jul 2023
Sound Explanation for Trustworthy Machine Learning Kai Jia Pasapol Saowakon L. Appelbaum Martin Rinard XAI FAtt FaML 24 2 0 08 Jun 2023
Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons Linwei Hu V. Nair Agus Sudjianto Aijun Zhang Jie Chen 30 8 0 25 May 2023
PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics Agus Sudjianto Aijun Zhang Zebin Yang Yuhao Su Ningzhou Zeng 29 6 0 07 May 2023
Semantics, Ontology and Explanation G. Guizzardi Nicola Guarino 10 7 0 21 Apr 2023
Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions Hongjing Xia Joshua C. Chang S. Nowak Sonya Mahajan R. Mahajan Ted L. Chang Carson C. Chow 38 1 0 19 Apr 2023
A Comparison of Modeling Preprocessing Techniques Tosan Johnson A. J. Liu S. Raza Aaron McGuire 9 1 0 23 Feb 2023
On marginal feature attributions of tree-based models Khashayar Filom A. Miroshnikov Konstandinos Kotsiopoulos Arjun Ravi Kannan FAtt 22 3 0 16 Feb 2023
Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life Kazuma Kobayashi S. B. Alam 19 49 0 17 Jan 2023
Autoencoded sparse Bayesian in-IRT factorization, calibration, and amortized inference for the Work Disability Functional Assessment Battery Joshua C. Chang Carson C. Chow Julia Porcino 39 1 0 20 Oct 2022
Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring Dangxing Chen Weicheng Ye FaML 32 13 0 21 Sep 2022
Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death Joshua C. Chang Ted L. Chang Carson C. Chow R. Mahajan Sonya Mahajan Joe Maisog Shashaank Vattikuti Hongjing Xia FAtt OOD 37 0 0 28 Aug 2022