GAN framework to generate synthetic glucose traces.

Therefore, we perform the learning and communication over the same set of parameters.

We view outcome homogenization as an important class ofsystemicharms that arise when we study socialsystems, i.e.harmsthatrequire observing howindividuals aretreated bymanydecision-makers.2 In 2,we conceptually motivate outcome homogenization in the context of algorithmic hiring.

We propose Elastic Reset, a new algorithm that achieves higher reward with less drift without explicitly modifying the training objective.