Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

20 March 2021

Zhi Chen

Papers citing "Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges"

32 / 82 papers shown

Title
Rectifying Mono-Label Boolean Classifiers S. Coste-Marquis Pierre Marquis 32 0 0 17 Jun 2022
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes Yishay Mansour Michal Moshkovitz Cynthia Rudin FAtt 29 3 0 09 Jun 2022
Assessing the trade-off between prediction accuracy and interpretability for topic modeling on energetic materials corpora Monica Puerto Mason Kellett Rodanthi Nikopoulou M. Fuge Ruth M. Doherty Peter W. Chung Zois Boukouvalas 23 1 0 01 Jun 2022
Exploiting Inductive Bias in Transformers for Unsupervised Disentanglement of Syntax and Semantics with VAEs G. Felhi Joseph Le Roux Djamé Seddah DRL 26 2 0 12 May 2022
Are Metrics Enough? Guidelines for Communicating and Visualizing Predictive Models to Subject Matter Experts Ashley Suh G. Appleby Erik W. Anderson Luca A. Finelli Remco Chang Dylan Cashman 27 8 0 11 May 2022
Boosting human decision-making with AI-generated decision aids Frederic Becker Julian Skirzyñski B. V. Opheusden Falk Lieder 30 13 0 05 Mar 2022
Sparse Bayesian Optimization Sulin Liu Qing Feng David Eriksson Benjamin Letham E. Bakshy 27 7 0 03 Mar 2022
Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review Kyle Hamilton Aparna Nayak Bojan Bozic Luca Longo NAI 29 57 0 24 Feb 2022
Towards Disentangling Information Paths with Coded ResNeXt Apostolos Avranas Marios Kountouris FAtt 17 1 0 10 Feb 2022
Learning Interpretable, High-Performing Policies for Autonomous Driving Rohan R. Paleja Yaru Niu Andrew Silva Chace Ritchie Sugju Choi Matthew C. Gombolay 19 16 0 04 Feb 2022
Hierarchical Shrinkage: improving the accuracy and interpretability of tree-based methods Abhineet Agarwal Yan Shuo Tan Omer Ronen Chandan Singh Bin-Xia Yu 65 27 0 02 Feb 2022
To what extent should we trust AI models when they extrapolate? Roozbeh Yousefzadeh Xuenan Cao 13 5 0 27 Jan 2022
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next S. Cuomo Vincenzo Schiano Di Cola F. Giampaolo G. Rozza Maizar Raissi F. Piccialli PINN 26 1,177 0 14 Jan 2022
A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study Yilin Ning Siqi Li M. Ong F. Xie Bibhas Chakraborty Daniel Ting Nan Liu FAtt 21 22 0 10 Jan 2022
Interpretable Low-Resource Legal Decision Making R. Bhambhoria Hui Liu Samuel Dahan Xiao-Dan Zhu ELM AILaw 14 9 0 01 Jan 2022
GAM Changer: Editing Generalized Additive Models with Interactive Visualization Zijie J. Wang Alex Kale Harsha Nori P. Stella M. Nunnally Duen Horng Chau Mihaela Vorvoreanu Jennifer Wortman Vaughan R. Caruana KELM 19 24 0 06 Dec 2021
HIVE: Evaluating the Human Interpretability of Visual Explanations Sunnie S. Y. Kim Nicole Meister V. V. Ramaswamy Ruth C. Fong Olga Russakovsky 66 114 0 06 Dec 2021
Rule Induction in Knowledge Graphs Using Linear Programming S. Dash Joao Goncalves 26 5 0 15 Oct 2021
Shapley variable importance clouds for interpretable machine learning Yilin Ning M. Ong Bibhas Chakraborty B. Goldstein Daniel Ting Roger Vaughan Nan Liu FAtt 21 69 0 06 Oct 2021
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies F. Xie Han Yuan Yilin Ning M. Ong Mengling Feng W. Hsu B. Chakraborty Nan Liu 21 83 0 21 Jul 2021
Characterizing the risk of fairwashing Ulrich Aivodji Hiromi Arai Sébastien Gambs Satoshi Hara 20 27 0 14 Jun 2021
Relational Reasoning Networks G. Marra Michelangelo Diligenti Francesco Giannini NAI 29 4 0 01 Jun 2021
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization Yingfan Wang Haiyang Huang Cynthia Rudin Yaron Shaposhnik 159 301 0 08 Dec 2020
Machine Learning Interpretability Meets TLS Fingerprinting Mahdi Jafari Siavoshani A. Khajehpour Amirmohammad Ziaei Bideh Amirali Gatmiri A. Taheri 13 2 0 12 Nov 2020
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models Christoph Molnar Gunnar Konig J. Herbinger Timo Freiesleben Susanne Dandl Christian A. Scholbeck Giuseppe Casalicchio Moritz Grosse-Wentrup B. Bischl FAtt AI4CE 8 135 0 08 Jul 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 38 405 0 29 Apr 2020
ViCE: Visual Counterfactual Explanations for Machine Learning Models Oscar Gomez Steffen Holter Jun Yuan E. Bertini AAML 55 93 0 05 Mar 2020
Deep Reinforcement Learning for Autonomous Driving: A Survey B. R. Kiran Ibrahim Sobh V. Talpaert Patrick Mannion A. A. Sallab S. Yogamani P. Pérez 165 1,630 0 02 Feb 2020
Learning Certifiably Optimal Rule Lists for Categorical Data E. Angelino Nicholas Larus-Stone Daniel Alabi Margo Seltzer Cynthia Rudin 46 195 0 06 Apr 2017
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 281 2,888 0 15 Sep 2016
Deep Reinforcement Learning for Dialogue Generation Jiwei Li Will Monroe Alan Ritter Michel Galley Jianfeng Gao Dan Jurafsky 214 1,327 0 05 Jun 2016
High-dimensional additive modeling L. Meier Sara van de Geer Peter Buhlmann 189 481 0 25 Jun 2008