Embarrassingly Parallel GFlowNets

• URL: http://arxiv.org/abs/2406.03288v1
• Date: Wed, 5 Jun 2024 13:59:05 GMT
• Title: Embarrassingly Parallel GFlowNets
• Authors: Tiago da Silva, Luiz Max Carvalho, Amauri Souza, Samuel Kaski, Diego Mesquita,
• Abstract summary: GFlowNets are a promising alternative to MCMC sampling for discrete compositional random variables. EP-GFlowNet is a provably correct divide-and-conquer method to sample from product distributions of the form $R(cdot) propto R_1(cdot)... R_N(cdot)$ -- e.g., in parallel or federated Bayes.
• Score: 17.751086304612667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: GFlowNets are a promising alternative to MCMC sampling for discrete compositional random variables. Training GFlowNets requires repeated evaluations of the unnormalized target distribution or reward function. However, for large-scale posterior sampling, this may be prohibitive since it incurs traversing the data several times. Moreover, if the data are distributed across clients, employing standard GFlowNets leads to intensive client-server communication. To alleviate both these issues, we propose embarrassingly parallel GFlowNet (EP-GFlowNet). EP-GFlowNet is a provably correct divide-and-conquer method to sample from product distributions of the form $R(\cdot) \propto R_1(\cdot) ... R_N(\cdot)$ -- e.g., in parallel or federated Bayes, where each $R_n$ is a local posterior defined on a data partition. First, in parallel, we train a local GFlowNet targeting each $R_n$ and send the resulting models to the server. Then, the server learns a global GFlowNet by enforcing our newly proposed \emph{aggregating balance} condition, requiring a single communication step. Importantly, EP-GFlowNets can also be applied to multi-objective optimization and model reuse. Our experiments illustrate the EP-GFlowNets's effectiveness on many tasks, including parallel Bayesian phylogenetics, multi-objective multiset, sequence generation, and federated Bayesian structure learning.

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