Explainability as statistical inference

6 December 2022

Hugo Senetaire

Damien Garreau

J. Frellsen

Pierre-Alexandre Mattei

FAtt

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Papers citing "Explainability as statistical inference"

38 / 38 papers shown

Title
A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them Lars Henry Berge Olsen I. Glad Martin Jullum K. Aas FAtt 65 12 0 16 May 2023
A Consistent and Efficient Evaluation Strategy for Attribution Methods Yao Rong Tobias Leemann V. Borisov Gjergji Kasneci Enkelejda Kasneci FAtt 63 96 0 01 Feb 2022
FastSHAP: Real-Time Shapley Value Estimation N. Jethani Mukund Sudarshan Ian Covert Su-In Lee Rajesh Ranganath TDI FAtt 96 131 0 15 Jul 2021
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning Yang Liu Sujay Khandagale Colin White Willie Neiswanger 98 66 0 23 Jun 2021
What's a good imputation to predict with missing values? Marine Le Morvan Julie Josse Erwan Scornet Gaël Varoquaux AI4TS 64 65 0 01 Jun 2021
Probabilistic Sufficient Explanations Eric Wang Pasha Khosravi Guy Van den Broeck XAI FAtt TPM 128 24 0 21 May 2021
Towards Rigorous Interpretations: a Formalisation of Feature Attribution Darius Afchar Romain Hennequin Vincent Guigue FAtt 64 20 0 26 Apr 2021
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations N. Jethani Mukund Sudarshan Yindalon Aphinyanagphongs Rajesh Ranganath FAtt 116 70 0 02 Mar 2021
Explaining by Removing: A Unified Framework for Model Explanation Ian Covert Scott M. Lundberg Su-In Lee FAtt 89 248 0 21 Nov 2020
Shapley explainability on the data manifold Christopher Frye Damien de Mijolla T. Begley Laurence Cowton Megan Stanley Ilya Feige FAtt TDI 35 99 0 01 Jun 2020
Explainable $k$ -Means and $k$ -Medians Clustering S. Dasgupta Nave Frost Michal Moshkovitz Cyrus Rashtchian 43 153 0 28 Feb 2020
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods Dylan Slack Sophie Hilgard Emily Jia Sameer Singh Himabindu Lakkaraju FAtt AAML MLAU 68 817 0 06 Nov 2019
LassoNet: A Neural Network with Feature Sparsity Ismael Lemhadri Feng Ruan L. Abraham Robert Tibshirani 80 129 0 29 Jul 2019
Monte Carlo Gradient Estimation in Machine Learning S. Mohamed Mihaela Rosca Michael Figurnov A. Mnih 67 411 0 25 Jun 2019
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values K. Aas Martin Jullum Anders Løland FAtt TDI 55 622 0 25 Mar 2019
Reparameterizable Subset Sampling via Continuous Relaxations Sang Michael Xie Stefano Ermon BDL 48 98 0 29 Jan 2019
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives George Tucker Dieterich Lawson S. Gu Chris J. Maddison BDL 52 110 0 09 Oct 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 127 1,966 0 08 Oct 2018
Importance Weighting and Variational Inference Justin Domke Daniel Sheldon 50 108 0 27 Aug 2018
Explaining Image Classifiers by Counterfactual Generation C. Chang Elliot Creager Anna Goldenberg David Duvenaud VLM 70 264 0 20 Jul 2018
A Benchmark for Interpretability Methods in Deep Neural Networks Sara Hooker D. Erhan Pieter-Jan Kindermans Been Kim FAtt UQCV 105 681 0 28 Jun 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 126 941 0 20 Jun 2018
Noise-adding Methods of Saliency Map as Series of Higher Order Partial Derivative Junghoon Seo J. Choe Jamyoung Koo Seunghyeon Jeon Beomsu Kim Taegyun Jeon FAtt ODL 44 29 0 08 Jun 2018
Variational Autoencoder with Arbitrary Conditioning Oleg Ivanov Michael Figurnov Dmitry Vetrov BDL DRL 55 147 0 06 Jun 2018
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation Jianbo Chen Le Song Martin J. Wainwright Michael I. Jordan MLT FAtt 131 574 0 21 Feb 2018
The (Un)reliability of saliency methods Pieter-Jan Kindermans Sara Hooker Julius Adebayo Maximilian Alber Kristof T. Schütt Sven Dähne D. Erhan Been Kim FAtt XAI 95 685 0 02 Nov 2017
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 276 9,760 0 25 Oct 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 280 8,878 0 25 Aug 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 21,864 0 22 May 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 74 1,519 0 11 Apr 2017
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications Tim Salimans A. Karpathy Xi Chen Diederik P. Kingma 105 942 0 19 Jan 2017
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables Chris J. Maddison A. Mnih Yee Whye Teh BDL 183 2,530 0 02 Nov 2016
Rationalizing Neural Predictions Tao Lei Regina Barzilay Tommi Jaakkola 110 812 0 13 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,954 0 16 Feb 2016
Importance Weighted Autoencoders Yuri Burda Roger C. Grosse Ruslan Salakhutdinov BDL 268 1,245 0 01 Sep 2015
U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger Philipp Fischer Thomas Brox SSeg 3DV 1.8K 77,133 0 18 May 2015
Deep Learning Face Attributes in the Wild Ziwei Liu Ping Luo Xiaogang Wang Xiaoou Tang CVBM 241 8,402 0 28 Nov 2014
Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation Yoshua Bengio Nicholas Léonard Aaron Courville 379 3,129 0 15 Aug 2013