Lazy generative modeling

Thomas M. Menino Convention & Exhibition Center 

In any Generative Model, the generated samples can be distributed according to a different distribution than the \emph{data distribution}, due to inevitable learning errors. Moreover, this discrepancy, and metrics for evaluating the generated samples, are hard to characterize in high dimensions. In this work, we explore a method to approximate the stochastic Koopman operator of the OU process and use this approximation as a lazy way to generate new samples. Although such an approximation does not produce the target probability distribution, it is amenable to adapt to learn certain features, e.g., sampling from the same support, of the target. This is joint work with Georg Gottwald (U Sydney).