Reliable ML from Unreliable Data

Distributions shift, chatbots get jail‑broken, users game algorithms — how do we build reliable machine learning when data are missing, corrupted, or strategically manipulated?

This workshop bridges theory and practice to tackle these challenges, bringing together researchers working on distribution shift, adversarial robustness, and strategic behaviour to chart principled yet deployable solutions for Reliable ML from Unreliable Data.