DIGing--SGLD: Decentralized and Scalable Langevin Sampling over Time--Varying Networks
The episode discusses 'DIGing-SGLD,' a method for decentralized and scalable Langevin sampling over time-varying networks. The hosts explain how this algorithm allows multiple agents to collaboratively approximate a global target distribution without needing central coordination, overcoming limitations in dynamic communication environments.