McKean-Vlasov stochastic differential equations (MV-SDEs) provide a mathematical description of the behavior of an infinite number of interacting particles by imposing a dependence on the particle density.We study the influence of explicitly including distributional information in the parameterization of the SDE.We propose a series of semi-parametric methods for representing MV-SDEs, and corresponding estimators for inferring parameters from data based on the properties of the MV-SDE.We analyze the characteristics of the different architectures and estimators, and consider their applicability in relevant machine learning problems.We empirically compare the performance of the different architectures and estimators on real and synthetic datasets for time series and probabilistic modeling.The results suggest that explicitly including distributional dependence in the parameterization of the SDE is effective in modeling temporal data with interaction under an exchangeability assumption while maintaining strong performance for standard It\^o-SDEs due to the richer class of probability flows associated with MV-SDEs.