

Wasserstein gradient flows and applications to sampling in machine learning - lecture 1
De Anna Korba


Wasserstein gradient flows and applications to sampling in machine learning - lecture 2
De Anna Korba
De Yoram Moses
Apparaît dans la collection : 2016 - T1 - WS1 - Distributed computation and communication theme
Decisions taken by an agent in a multi-agent system depend on the agent's local information. A new formulation of the connection between knowledge and action in multi-agent systems allows new insights into the design of such systems. This talk will discuss how a theory of what agents know about the world and about knowledge of others can help in the design and analysis of distributed computer systems. Based in part on joint work with Armando Castaneda and Yannai Gonczarowski.