most citedImperceptible Adversarial Attacks on Tabular Data

50 citations · 70 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI20206 cited

Active Fairness Instead of Unawareness

Boris Ruf, Marcin Detyniecki

The possible risk that AI systems could promote discrimination by reproducing and enforcing unwanted bias in data has been broadly discussed in research and society. Many current l…

cs.LG20203 cited

Adversarial Learning for Counterfactual Fairness

Vincent Grari, Sylvain Lamprier, Marcin Detyniecki

In recent years, fairness has become an important topic in the machine learning research community. In particular, counterfactual fairness aims at building prediction models which…

eess.SP20203 cited

Vehicle Telematics Via Exteroceptive Sensors: A Survey

Fernando Molano Ortiz, Matteo Sammarco, Luís Henrique M. K. Costa +1

Whereas a very large number of sensors are available in the automotive field, currently just a few of them, mostly proprioceptive ones, are used in telematics, automotive insurance…

stat.ML201950 cited

Imperceptible Adversarial Attacks on Tabular Data

Vincent Ballet, Xavier Renard, Jonathan Aigrain +3

Security of machine learning models is a concern as they may face adversarial attacks for unwarranted advantageous decisions. While research on the topic has mainly been focusing o…

cs.LG20198 cited

Fairness-Aware Neural Réyni Minimization for Continuous Features

Vincent Grari, Boris Ruf, Sylvain Lamprier +1

The past few years have seen a dramatic rise of academic and societal interest in fair machine learning. While plenty of fair algorithms have been proposed recently to tackle this…