In this work, we provide a fundamental unified convergence theorem used for deriving expected and almost sure convergence results for a series of stochastic optimization methods.

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Conformal prediction is atechnique for constructing prediction intervals that attainvalidcoverage infinite samples, without making distributional assumptions. Despite this appeal, existing conformal methods can be unnecessarily conservativebecause theyform intervals ofconstant orweakly varying length across the input space.

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A key similarity between our works is that we both4 condition on intermediate renders, a feature also shared with other works in the computer vision literature such as5 Eslamietal. R3 points out that our value estimation loss is nonstandard.

Asymptotic constrained (1)has [6, 18, 21, 20]. R and closedsetC Rd introduced(1).