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New algorithm enables selective conformal inference with adaptive capabilities

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an extension to the Gibbs and Candès (2021) algorithm called OnlineSCI, which allows for selective conformal inference in supervised online settings. This new method enables users to choose specific times for inference, expanding its applicability to tasks like building prediction intervals for extreme outcomes, classification with abstention, and online testing. OnlineSCI rigorously controls error rates, both overall and conditional on selection, and importantly, supports adaptive updates to the point-prediction algorithm, potentially converging to optimal solutions with explicit convergence rates. AI

IMPACT Introduces a novel method for uncertainty quantification in online learning settings, potentially improving reliability in adaptive prediction tasks.

RANK_REASON The cluster contains an academic paper detailing a new algorithm for statistical inference. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New algorithm enables selective conformal inference with adaptive capabilities

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The cluster contains an academic paper detailing a new algorithm for statistical inference. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Pierre Humbert, Ulysse Gazin, Ruth Heller, Etienne Roquain ·

    Online selective conformal inference: adaptive scores, convergence rates and optimality

    arXiv:2508.10336v3 Announce Type: replace-cross Abstract: In a supervised online setting, quantifying uncertainty has been proposed in the seminal work of Gibbs and Cand\`es (2021). For any given point-prediction algorithm, their method (ACI) produces a conformal prediction set w…

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