Timezone: »

 
Poster
Sequential Cooperative Bayesian Inference
Junqi Wang · Pei Wang · Patrick Shafto

Wed Jul 15 05:00 AM -- 05:45 AM & Wed Jul 15 04:00 PM -- 04:45 PM (PDT) @
in Poster Session 21 »

Cooperation is often implicitly assumed when learning from other agents. Cooperation implies that the agent selecting the data, and the agent learning from the data, have the same goal, that the learner infer the intended hypothesis. Recent models in human and machine learning have demonstrated the possibility of cooperation. We seek foundational theoretical results for cooperative inference by Bayesian agents through sequential data. We develop novel approaches analyzing consistency, rate of convergence and stability of Sequential Cooperative Bayesian Inference (SCBI). Our analysis of the effectiveness, sample efficiency and robustness show that cooperation is not only possible but theoretically well-founded. We discuss implications for human-human and human-machine cooperation.

Keywords: Bayesian Methods · Multiagent Learning · Probabilistic Inference - Models and Probabilistic Programming

Author Information

Junqi Wang (Rutgers University-Newark)
Pei Wang (Rutgers University-Newark)
Patrick Shafto (Rutgers University-Newark)

More from the Same Authors