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We propose a sequence of unnormalized distributions over Rᵈ with asymptotic convergence to the Hausdorff measure of the level set, therefore resulting in uniform samples. Beyond our formulation’s asymptotic convergence, we demonstrate its practicality by using MCMC to sample a distribution in the sequence for some analytical functions. Finally, we test our sampling method on representative applications related to machine learning, including extracting geometry from neural implicit representations and multi-objective optimization."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Uniform Sampling over Level Sets"]}]}],"canonical_facts":{"dc:contributor.advisor":["Solomon, Justin"],"dc:contributor.department":["Massachusetts Institute of Technology. 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