A survey of algorithmic recourse: definitions, formulations, solutions,
and prospects

A survey of algorithmic recourse: definitions, formulations, solutions, and prospects

8 October 2020

Amir-Hossein Karimi

Gilles Barthe

Bernhard Schölkopf

Papers citing "A survey of algorithmic recourse: definitions, formulations, solutions, and prospects"

10 / 110 papers shown

Title
Control of Memory, Active Perception, and Action in Minecraft Michael T. Lash Valliappa Chockalingam Ashwin Balakrishnan Honglak Lee 26 27 0 30 May 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 519 16,765 0 16 Feb 2016
Practical Black-Box Attacks against Machine Learning Nicolas Papernot Patrick McDaniel Ian Goodfellow S. Jha Z. Berkay Celik A. Swami MLAU AAML 44 3,656 0 08 Feb 2016
The Limitations of Deep Learning in Adversarial Settings Nicolas Papernot Patrick McDaniel S. Jha Matt Fredrikson Z. Berkay Celik A. Swami AAML 60 3,947 0 24 Nov 2015
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 90 4,878 0 14 Nov 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 158 18,922 0 20 Dec 2014
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images Anh Totti Nguyen J. Yosinski Jeff Clune AAML 122 3,261 0 05 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 159 14,831 1 21 Dec 2013
Causal Discovery from Changes Jin Tian Judea Pearl CML 74 165 0 10 Jan 2013
Causality and Statistical Learning Andrew Gelman CML AI4CE 87 70 0 12 Mar 2010