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Complexity of Non-Log-Concave Sampling in Fisher Information

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arXiv:2605. 15859v1 Announce Type: cross Abstract: We study the query complexity of obtaining a relative Fisher information guarantee for sampling from a log-smooth non-log-concave distribution; this is a sampling analog of finding an approximate stationary point in optimization.

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Authors: Sinho Chewi, Andre Wibisono
Released: 2026-05-18 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Complexity of Non-Log-Concave Sampling in Fisher Information overlaps with experiment "Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe". Matching terms: implement. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.15859

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