.%20EP-GFlowNet%20is%20a%20provably%20correct%20divide-and-conquer%20method%20to%20sample%20from%20product%20distributions%20of%20the%20form%20%24R(cdot)%20propto%20R_1(cdot)%20...%20R_N(cdot)%24%20--%20e.g.%2C%20in%20parallel%20or%20federated%20Bayes%2C%20where%20each%20%24R_n%24%20is%20a%20local%20posterior%20defined%20on%20a%20data%20partition.%20First%2C%20in%20parallel%2C%20we%20train%20a%20local%20GFlowNet%20targeting%20each%20%24R_n%24%20and%20send%20the%20resulting%20models%20to%20the%20server.%20Then%2C%20the%20server%20learns%20a%20global%20GFlowNet%20by%20enforcing%20our%20newly%20proposed%20emph%7Baggregating%20balance%7D%20condition%2C%20requiring%20a%20single%20communication%20step.%20Importantly%2C%20EP-GFlowNets%20can%20also%20be%20applied%20to%20multi-objective%20optimization%20and%20model%20reuse.%20Our%20experiments%20illustrate%20the%20EP-GFlowNets's%20effectiveness%20on%20many%20tasks%2C%20including%20parallel%20Bayesian%20phylogenetics%2C%20multi-objective%20multiset%2C%20sequence%20generation%2C%20and%20federated%20Bayesian%20structure%20learning.)
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