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.

Robust learning is a critical field that seeks to develop efficient algorithms that can recover an underlying model despite possibly malicious corruptions in the data. In recent decades, being able to deal with corrupted measurements has become of crucial importance.