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Model Learning with Personalized Interpretability Estimation (ML-PIE)

13 April 2021

Papers citing "Model Learning with Personalized Interpretability Estimation (ML-PIE)"

23 / 23 papers shown

Title
Limits of trust in medical AI Joshua Hatherley 94 134 0 20 Mar 2025
Multi-Objective Learning to Predict Pareto Fronts Using Hypervolume Maximization T. Deist Monika Grewal F. Dankers Tanja Alderliesten Peter A. N. Bosman 70 19 0 08 Feb 2021
Mining Feature Relationships in Data Andrew Lensen 15 4 0 02 Feb 2021
GLocalX -- From Local to Global Explanations of Black Box AI Models Mattia Setzu Riccardo Guidotti A. Monreale Franco Turini D. Pedreschi F. Giannotti 92 120 0 19 Jan 2021
Evolutionary learning of interpretable decision trees Leonardo Lucio Custode Giovanni Iacca OffRL 62 41 0 14 Dec 2020
On Explaining Decision Trees Yacine Izza Alexey Ignatiev Sasha Rubin FAtt 79 88 0 21 Oct 2020
Learning a Formula of Interpretability to Learn Interpretable Formulas M. Virgolin A. D. Lorenzo Eric Medvet Francesca Randone 46 33 0 23 Apr 2020
Genetic Programming for Evolving a Front of Interpretable Models for Data Visualisation Andrew Lensen Bing Xue Mengjie Zhang 19 43 0 27 Jan 2020
On Explaining Machine Learning Models by Evolving Crucial and Compact Features M. Virgolin Tanja Alderliesten Peter A. N. Bosman 60 28 0 04 Jul 2019
Manipulating and Measuring Model Interpretability Forough Poursabzi-Sangdeh D. Goldstein Jake M. Hofman Jennifer Wortman Vaughan Hanna M. Wallach 99 701 0 21 Feb 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 148 3,979 0 06 Feb 2018
Morphology dictates a robot's ability to ground crowd-proposed language Zahra Mahoor Jack Felag Josh Bongard LM&Ro 35 7 0 16 Dec 2017
Interpretable Policies for Reinforcement Learning by Genetic Programming D. Hein Steffen Udluft Thomas Runkler OffRL 61 135 0 12 Dec 2017
Deep reinforcement learning from human preferences Paul Christiano Jan Leike Tom B. Brown Miljan Martic Shane Legg Dario Amodei 218 3,377 0 12 Jun 2017
Improved Training of Wasserstein GANs Ishaan Gulrajani Faruk Ahmed Martín Arjovsky Vincent Dumoulin Aaron Courville GAN 227 9,564 0 31 Mar 2017
Active Learning Using Uncertainty Information Yazhou Yang Marco Loog 40 73 0 27 Feb 2017
Interpretable Two-level Boolean Rule Learning for Classification Guolong Su Dennis L. Wei Kush R. Varshney Dmitry Malioutov 59 52 0 18 Jun 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 183 3,708 0 10 Jun 2016
Fast methods for training Gaussian processes on large data sets C. Moore A. J. Chua C. Berry J. Gair GP 49 41 0 05 Apr 2016
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model Benjamin Letham Cynthia Rudin Tyler H. McCormick D. Madigan FAtt 72 743 0 05 Nov 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 863 9,353 0 06 Jun 2015
Supersparse Linear Integer Models for Optimized Medical Scoring Systems Berk Ustun Cynthia Rudin 130 354 0 15 Feb 2015
Falling Rule Lists Fulton Wang Cynthia Rudin 66 258 0 21 Nov 2014