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New paper explores randomized smoothing geometry on feasible sets

Researchers Alireza Furutanpey and Ruevo ny have published a paper on arXiv detailing the geometry of randomized smoothing on feasible sets. Their work introduces a method to certify label probabilities by filtering proposals, which can lead to certifying balls containing decision boundaries. The paper separates the problem into geometric and certification questions, exploring how conditioning preserves Gaussian comparisons and the implications for certification radii. AI

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New paper explores randomized smoothing geometry on feasible sets

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The cluster contains a research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Syed Izhan Khilji, Alireza Furutanpey, Schahram Dustdar ·

    The Geometry of Randomized Smoothing on Feasible Sets

    arXiv:2609.39497v1 Announce Type: cross Abstract: Randomized smoothing certifies the probability of a fixed output event as the center of Gaussian noise moves. Feasibility or confidence filtering reports label probabilities only among retained proposals, producing a ratio. Its nu…