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Improving the evaluation of samplers on multi-modal targets

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arxiv 2504.08916 v1 pith:EU7Q2BTA submitted 2025-04-11 stat.ML cs.LG

Louis Grenioux , Maxence Noble , Marylou Gabrié This is my paper · ORCID
classification stat.MLcs.LG
keywords samplersevaluationmodemulti-modalityaddressingadvocatechallengeschallenging
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Addressing multi-modality constitutes one of the major challenges of sampling. In this reflection paper, we advocate for a more systematic evaluation of samplers towards two sources of difficulty that are mode separation and dimension. For this, we propose a synthetic experimental setting that we illustrate on a selection of samplers, focusing on the challenging criterion of recovery of the mode relative importance. These evaluations are crucial to diagnose the potential of samplers to handle multi-modality and therefore to drive progress in the field.

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