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New diagnostic tool assesses confidence thresholding in pseudo-labeling

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed a new diagnostic tool to assess the reliability of confidence thresholding in pseudo-labeling pipelines for regression tasks. This method provides a way to predict the bias introduced by thresholding calibrated classifier scores, using the residual score variance on unlabelled data. The proposed $(V^{*}, \kappa)$ decision rule aims to help practitioners determine when confidence thresholding is a safe practice. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Provides a new operational tool for practitioners to improve the reliability of pseudo-labelled regression models.

RANK_REASON The cluster contains an academic paper detailing a new methodology and diagnostic tool for statistical analysis.

Read on arXiv stat.ML →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 · Marcell T. Kurbucz ·

    When to Trust Confidence Thresholding: Calibration Diagnostics for Pseudo-Labelled Regression

    arXiv:2605.12780v1 Announce Type: cross Abstract: Calibrated probability outputs of trained classifiers are increasingly used as inputs to downstream regression estimands such as effects, prevalences, or disparities for a latent group observed only on a small labelled subset. A s…

  2. arXiv stat.ML TIER_1 · Marcell T. Kurbucz ·

    When to Trust Confidence Thresholding: Calibration Diagnostics for Pseudo-Labelled Regression

    Calibrated probability outputs of trained classifiers are increasingly used as inputs to downstream regression estimands such as effects, prevalences, or disparities for a latent group observed only on a small labelled subset. A standard practice is to threshold the calibrated sc…

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