Kok-Seng Wong

Enforcing global invariance in domain generalization can lead to the loss of critical predictive factors, but subset-shared invariance allows for more nuanced representation learning that boosts performance.

ChemBack reveals that even with rigorous admission protocols, chemically valid backdoors can still pose significant risks to molecular graph neural networks.