Seminar Details
2025-08-26 (11:00) : Robustness for Tabular Machine Learning: A Back-and-Forth Journey between Research and Industry
At Shannon room
Organized by Computer Science and Engineering
Speaker :
Maxime Cordy (SnT Université du Luxembourg)
Abstract :
Adversarial attacks are widely recognized as a critical security threat for machine learning, but most were originally designed in the context of image recognition, where arbitrary pixel-level perturbations are applied. Such attacks often fail to capture the realities of domains governed by strict constraints on valid inputs. This is particularly the case in tabular machine learning, where only feasible feature-level perturbations can occur. This talk explores the gap between classical adversarial attack formulations and real-world applicability. I will review our recent research on constrained feature-space attacks, which aim to generate realistic adversarial examples under domain-specific restrictions. Drawing on our own experience in applying these methods to industrial use cases, I will highlight the challenges of evaluating robustness in practice and discuss opportunities for new research in this area.