Optimal Transport PAC-Bayes Bounds
This work develops new generalization bounds by replacing classical Kullback-Leibler divergence with general optimal transport distances. This allows the resulting PAC-Bayesian bounds to incorporate the geometry of the hypothesis space, overcoming limitations where KL divergence fails due to support restrictions or large shifts between prior and posterior distributions.