AI RESEARCH
Conservative neural posterior estimation via distributionally robust training
arXiv CS.LG
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ArXi:2605.28516v1 Announce Type: cross Simulation-based inference with neural posterior estimation (NPE) often yields overconfident and unreliable posteriors under limited simulation budgets. To address this, we propose DRO-NPE, a distributionally robust approach that replaces the standard NPE objective with a worst-case loss over a Wasserstein ambiguity set. We