To address this gap, we study deterministic/stochastic versions of SAM with practical configurations (i.e., constant

Knowledge distillation is a popular approach for enhancing the performance of "student" models, with lower representational capacity, by taking advantage of

However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability.

Recent advances in machine learning and artificial intelligence have relied on fitting highly overparam-eterized models, notably deep neural networks, to observed data; e.g.

While OT commonly seeks at computing the transport plan that minimizes the cost of moving between two distributions, it can naturally be extended to the multi-marginal setting (mOT) when considering several distributions.