Debarghya Ghoshdastidar

Challenging the conventional wisdom that strong certified robustness requires heavy partitioning, this work shows how white-box knowledge of base classifiers in partition-aggregation ensembles can yield significantly tighter robustness guarantees against label-flipping attacks with fewer partitions.

Graph transformers avoid oversmoothing in deep layers by structurally preserving community information, a theoretical advantage over GCNs revealed through Gaussian process limits.