Embarrassingly Parallel GFlowNets
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A4 Artikkeli konferenssijulkaisussa
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Date
2024
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Mcode
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en
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Proceedings of Machine Learning Research ; Volume 235
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(⋅)∝R1(⋅)...RN(⋅) — e.g., in parallel or federated Bayes, where each Rn is a local posterior defined on a data partition. First, in parallel, we train a local GFlowNet targeting each Rn and send the resulting models to the server. Then, the server learns a global GFlowNet by enforcing our newly proposed 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 effectiveness of EP-GFlowNets on multiple tasks, including parallel Bayesian phylogenetics, multi-objective multiset and sequence generation, and federated Bayesian structure learning.Description
| openaire: EC/H2020/951847/EU//ELISE
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da Silva, T, Carvalho, L M, Souza, A, Kaski, S & Mesquita, D 2024, Embarrassingly Parallel GFlowNets . in R Salakhutdinov, Z Kolter, K Heller, A Weller, N Oliver, J Scarlett & F Berkenkamp (eds), Proceedings of the 41st International Conference on Machine Learning . Proceedings of Machine Learning Research, vol. 235, JMLR, pp. 45406-45431, International Conference on Machine Learning, Vienna, Austria, 21/07/2024 . < https://arxiv.org/abs/2406.03288 >
