Reframing fairness as distributional stability under protected-group shifts unifies classical fairness notions via Lipschitz constants and yields a second-order cone program with uniform test-time guarantees.
Diff-CA conditions diffusion models to decompose image representations into common and salient factors via weak supervision, achieving high-fidelity contrastive generation and editing with provable factorization identifiability.
Gossip algorithms enable decentralized ranking aggregation via local peer interactions, yielding global consensus without central authority despite corrupted nodes and distributed preferences.