Many recent AI architectures are inspired by zero-sum games, however, the behavior of their dynamics is still not well understood.

However, none of these solutions eliminates the linear dependence ongraph size.

However, existing ways of implementing the ME-HP for such data are either inflexible, as the exogenous (background) rate functions are typically constant and the endogenous (excitation) rate functions are specified parametrically, or inefficient, as inference usually relies on Markov chain Monte Carlo methods with high computational costs.

Toachievethis, webuildonInfluenceFunction(IF)andproposesource-awareIF 2,whichleverages the generation process of the probabilistic labels to decompose the end model's training objective and then calculate the influence associated with each (data, source, class)tuple.

In this work, we propose a new approach to R-D estimation from the perspective of optimal transport.