v1v2 (latest)

Testing robustness of predictions of trained classifiers against naturally occurring perturbations

21 April 2022

Papers citing "Testing robustness of predictions of trained classifiers against naturally occurring perturbations"

33 / 33 papers shown

Title
Generalized Out-of-Distribution Detection: A Survey Jingkang Yang Kaiyang Zhou Yixuan Li Ziwei Liu 295 940 0 21 Oct 2021
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms Martin Pawelczyk Sascha Bielawski J. V. D. Heuvel Tobias Richter Gjergji Kasneci CML 72 105 0 02 Aug 2021
An Information-theoretic Approach to Distribution Shifts Marco Federici Ryota Tomioka Patrick Forré OOD 72 20 0 07 Jun 2021
WILDS: A Benchmark of in-the-Wild Distribution Shifts Pang Wei Koh Shiori Sagawa Henrik Marklund Sang Michael Xie Marvin Zhang ... A. Kundaje Emma Pierson Sergey Levine Chelsea Finn Percy Liang OOD 230 1,445 0 14 Dec 2020
Captum: A unified and generic model interpretability library for PyTorch Narine Kokhlikyan Vivek Miglani Miguel Martin Edward Wang B. Alsallakh ... Alexander Melnikov Natalia Kliushkina Carlos Araya Siqi Yan Orion Reblitz-Richardson FAtt 144 846 0 16 Sep 2020
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples Timo Freiesleben GAN 98 64 0 11 Sep 2020
In Search of Lost Domain Generalization Ishaan Gulrajani David Lopez-Paz OOD 94 1,157 0 02 Jul 2020
Measuring Robustness to Natural Distribution Shifts in Image Classification Rohan Taori Achal Dave Vaishaal Shankar Nicholas Carlini Benjamin Recht Ludwig Schmidt OOD 123 549 0 01 Jul 2020
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization Dan Hendrycks Steven Basart Norman Mu Saurav Kadavath Frank Wang ... Samyak Parajuli Mike Guo Basel Alomair Jacob Steinhardt Justin Gilmer OOD 357 1,757 0 29 Jun 2020
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty Dan Hendrycks Norman Mu E. D. Cubuk Barret Zoph Justin Gilmer Balaji Lakshminarayanan OOD UQCV 128 1,308 0 05 Dec 2019
Learning Model-Agnostic Counterfactual Explanations for Tabular Data Martin Pawelczyk Johannes Haug Klaus Broelemann Gjergji Kasneci OOD CML 66 204 0 21 Oct 2019
Accurate, reliable and fast robustness evaluation Wieland Brendel Jonas Rauber Matthias Kümmerer Ivan Ustyuzhaninov Matthias Bethge AAML OOD 66 113 0 01 Jul 2019
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 93 1,845 0 06 May 2019
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations Dan Hendrycks Thomas G. Dietterich OOD VLM 191 3,455 0 28 Mar 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 166 2,051 0 08 Feb 2019
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms Yoshua Bengio T. Deleu Nasim Rahaman Nan Rosemary Ke Sébastien Lachapelle O. Bilaniuk Anirudh Goyal C. Pal CML OOD 117 334 0 30 Jan 2019
Explaining Explanations in AI Brent Mittelstadt Chris Russell Sandra Wachter XAI 105 667 0 04 Nov 2018
Adversarial Robustness Toolbox v1.0.0 Maria-Irina Nicolae M. Sinn Minh-Ngoc Tran Beat Buesser Ambrish Rawat ... Nathalie Baracaldo Bryant Chen Heiko Ludwig Ian Molloy Ben Edwards AAML VLM 77 460 0 03 Jul 2018
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach Tsui-Wei Weng Huan Zhang Pin-Yu Chen Jinfeng Yi D. Su Yupeng Gao Cho-Jui Hsieh Luca Daniel AAML 83 468 0 31 Jan 2018
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning Battista Biggio Fabio Roli AAML 130 1,409 0 08 Dec 2017
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 129 2,366 0 01 Nov 2017
Feature-Guided Black-Box Safety Testing of Deep Neural Networks Matthew Wicker Xiaowei Huang Marta Kwiatkowska AAML 46 235 0 21 Oct 2017
Security Evaluation of Pattern Classifiers under Attack Battista Biggio Giorgio Fumera Fabio Roli AAML 70 444 0 02 Sep 2017
Evasion Attacks against Machine Learning at Test Time Battista Biggio Igino Corona Davide Maiorca B. Nelson Nedim Srndic Pavel Laskov Giorgio Giacinto Fabio Roli AAML 163 2,160 0 21 Aug 2017
Robust Physical-World Attacks on Deep Learning Models Kevin Eykholt Ivan Evtimov Earlence Fernandes Yue Liu Amir Rahmati Chaowei Xiao Atul Prakash Tadayoshi Kohno Basel Alomair AAML 83 595 0 27 Jul 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 321 1,875 0 03 Feb 2017
Safety Verification of Deep Neural Networks Xiaowei Huang Marta Kwiatkowska Sen Wang Min Wu AAML 238 944 0 21 Oct 2016
A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples T. Tanay Lewis D. Griffin AAML 93 272 0 27 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 547 5,910 0 08 Jul 2016
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 154 4,905 0 14 Nov 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 282 19,129 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 284 14,968 1 21 Dec 2013
Domain Generalization via Invariant Feature Representation Krikamol Muandet David Balduzzi Bernhard Schölkopf OOD 142 1,188 0 10 Jan 2013