generalization-bounds

This paper introduces a theoretical framework for quantifying deployment risk when training and deployment distributions differ due to latent regime dynamics modeled as a Markov-switching process, providing exact decomposition and finite-sample bounds.

This paper develops a PAC-Bayesian framework for test-time adaptation that uses MMD-balls as credal sets, providing formal generalization bounds and separating epistemic from aleatoric uncertainty under distribution shift.

This paper proposes an information-theoretic framework for emergent communication in Agentic AI Networking (AgentNet), addressing physical constraints and providing generalization bounds. Experimental validation on hardware prototypes demonstrates improved generalization performance compared to state-of-the-art solutions.