2020/02/13 by Junqi Wang, Pei Wang, Wang, Junqi +3 · 5 citations
Computer Science · Mathematics · #Artificial intelligence #Bayesian Modeling and Causal Inference #Bayesian inference #Bayesian probability #Computer science #Consistency (knowledge bases) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine learning #Multiagent Systems (cs.MA) #Robustness (evolution) #cs.LG #cs.MA #stat.ML
paper · pdf · doi:10.48550/arxiv.2002.05706
published in arXiv (Cornell University) (Cornell University) · 25 pages, 22 figures, accepted by ICML 2020
openalex publication_date 2020/02/13 · arxiv created 2020/07/01 · arxiv updated 2020/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 in specific instances but theoretically well-founded in general. We discuss implications for human-human and human-machine cooperation.