May 15, 2024, 4:43 a.m. | Antoine Wehenkel, Juan L. Gamella, Ozan Sener, Jens Behrmann, Guillermo Sapiro, Marco Cuturi, J\"orn-Henrik Jacobsen

cs.LG updates on

arXiv:2405.08719v1 Announce Type: cross
Abstract: Driven by steady progress in generative modeling, simulation-based inference (SBI) has enabled inference over stochastic simulators. However, recent work has demonstrated that model misspecification can harm SBI's reliability. This work introduces robust posterior estimation (ROPE), a framework that overcomes model misspecification with a small real-world calibration set of ground truth parameter measurements. We formalize the misspecification gap as the solution of an optimal transport problem between learned representations of real-world and simulated observations. Assuming the …

abstract arxiv calibration cs.lg data data-driven framework generative generative modeling harm however inference modeling posterior progress reliability robust rope set simulation small stochastic through type work world

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