arXiv AI By Hoang Phuc Hau Luu, Marcelo Hartmann, Zhongjian Wang

Posterior sampling by source-space MCMC via prior-based few-step transport maps

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arXiv Machine Learning
Sep 3

Source Distribution Estimation by Posterior Averaging

The paper introduces a new approach to source distribution estimation (SDE) in simulation-based science, addressing limitations of existing methods that rely on a fixed surrogate likelihood. By employing an expectation‑maximization framework, the authors iteratively train an amortized posterior on fresh simulations (E‑step) and refit the source distribution to the posterior’s average (M‑step). Two parameterizations are explored: separate source and posterior flows, and a single shared conditional flow, with experiments on three benchmark tasks showing improved performance over fixed surrogate and iterated baseline methods, notably achieving higher data‑space C2ST scores on the Lotka–Volterra benchmark.

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Information-Geometric Forward Policy Training in GFlowNets

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arXiv AI
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Efficient Weighted Sampling via Score-based Generative Models

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