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      "statement": "# RAPS: regularized adaptive prediction sets for image classifiers\n\nPenalizes cumulative ranked class probabilities beyond a chosen rank and calibrates the resulting nested randomized sets on held-out data to control marginal label coverage.\n\n## Scientific scope\n\nInherits conformal validity and APS cumulative scores; new rank-tail regularization is distinct. Finite-sample theorem assumes i.i.d. calibration/test triples and nested sets, with positivity for near-tight upper coverage. Marginal coverage is not per-input conditional coverage or guaranteed minimal set size.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nAnastasios N. Angelopoulos; Stephen Bates; Jitendra Malik; Michael I. Jordan\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Uncertainty Sets for Image Classifiers using Conformal Prediction](https://arxiv.org/pdf/2009.14193v1)\n",
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