An unexpected unity among methods for interpreting model predictions

22 November 2016

Papers citing "An unexpected unity among methods for interpreting model predictions"

22 / 22 papers shown

Title
Mapping Knowledge Representations to Concepts: A Review and New Perspectives Lars Holmberg P. Davidsson Per Linde 36 1 0 31 Dec 2022
Easy to Decide, Hard to Agree: Reducing Disagreements Between Saliency Methods Josip Jukić Martin Tutek Jan Snajder FAtt 33 0 0 15 Nov 2022
Machine Learning for a Sustainable Energy Future Zhenpeng Yao Yanwei Lum Andrew K. Johnston L. M. Mejia-Mendoza Xiaoxia Zhou Yonggang Wen Alán Aspuru-Guzik E. Sargent Z. Seh 32 211 0 19 Oct 2022
Detection of ADHD based on Eye Movements during Natural Viewing Shuwen Deng Paul Prasse D. R. Reich S. Dziemian Maja Stegenwallner-Schütz Daniel G. Krakowczyk Silvia Makowski N. Langer Tobias Scheffer Lena A. Jäger 36 9 0 04 Jul 2022
Decorrelated Variable Importance I. Verdinelli Larry A. Wasserman FAtt 17 18 0 21 Nov 2021
Explaining Deep Reinforcement Learning Agents In The Atari Domain through a Surrogate Model Alexander Sieusahai Matthew J. Guzdial 35 13 0 07 Oct 2021
Information-theoretic Evolution of Model Agnostic Global Explanations Sukriti Verma Nikaash Puri Piyush B. Gupta Balaji Krishnamurthy FAtt 29 0 0 14 May 2021
Why model why? Assessing the strengths and limitations of LIME Jurgen Dieber S. Kirrane FAtt 26 97 0 30 Nov 2020
Explainable Predictive Process Monitoring Musabir Musabayli F. Maggi Williams Rizzi Josep Carmona Chiara Di Francescomarino 19 60 0 04 Aug 2020
Making deep neural networks right for the right scientific reasons by interacting with their explanations P. Schramowski Wolfgang Stammer Stefano Teso Anna Brugger Xiaoting Shao Hans-Georg Luigs Anne-Katrin Mahlein Kristian Kersting 42 207 0 15 Jan 2020
Technical Report: Partial Dependence through Stratification T. Parr James D. Wilson 18 2 0 15 Jul 2019
Global Aggregations of Local Explanations for Black Box models I. V. D. Linden H. Haned Evangelos Kanoulas FAtt 27 63 0 05 Jul 2019
Training Machine Learning Models by Regularizing their Explanations A. Ross FaML 26 0 0 29 Sep 2018
Stakeholders in Explainable AI Alun D. Preece Daniel Harborne Dave Braines Richard J. Tomsett Supriyo Chakraborty 15 154 0 29 Sep 2018
Contrastive Explanations for Reinforcement Learning in terms of Expected Consequences J. V. D. Waa J. Diggelen K. Bosch Mark Antonius Neerincx OffRL 31 107 0 23 Jul 2018
Contrastive Explanations with Local Foil Trees J. V. D. Waa M. Robeer J. Diggelen Matthieu J. S. Brinkhuis Mark Antonius Neerincx FAtt 24 82 0 19 Jun 2018
"Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Users Stefano Teso Kristian Kersting FAtt HAI 25 12 0 22 May 2018
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability Mike Wu M. C. Hughes S. Parbhoo Maurizio Zazzi Volker Roth Finale Doshi-Velez AI4CE 28 281 0 16 Nov 2017
MAGIX: Model Agnostic Globally Interpretable Explanations Nikaash Puri Piyush B. Gupta Pratiksha Agarwal Sukriti Verma Balaji Krishnamurthy FAtt 32 41 0 22 Jun 2017
Interpreting Blackbox Models via Model Extraction Osbert Bastani Carolyn Kim Hamsa Bastani FAtt 35 170 0 23 May 2017
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations A. Ross M. C. Hughes Finale Doshi-Velez FAtt 54 583 0 10 Mar 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 45 5,865 0 04 Mar 2017