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New research advances conformal prediction for better ML uncertainty quantification · 4 sources tracked

By PulseAugur Editorial · [6 sources] ·

Researchers are exploring advanced conformal prediction techniques to improve uncertainty quantification in machine learning. One paper introduces Online Conformal Prediction Beyond Feedback (OCPQ), which can output predictions or query labels without direct feedback, achieving strong regret and coverage guarantees. Another study formalizes the "residual-information gap" to explain why marginal coverage in conformal prediction doesn't always equate to forecast quality. Additionally, new methods are being developed for localized conformal prediction, offering finite-sample guarantees for conditional validity and efficiency, and a specific approach called LoBoost is presented for fast, model-native local conformal prediction tailored for gradient-boosted trees. AI

IMPACT Advances in conformal prediction offer more reliable uncertainty quantification for ML models, crucial for safety-critical applications and improving forecast quality.

RANK_REASON Multiple arXiv papers presenting new theoretical and algorithmic contributions to conformal prediction methods.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 6 sources. How we write summaries →

New research advances conformal prediction for better ML uncertainty quantification · 4 sources tracked

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COVERAGE [6]

  1. arXiv cs.AI TIER_1 English(EN) · Sangjin Jin, Kangmin Kim, Junhyeong Lee, Yongjae Lee ·

    Retrieval-Corrected Conformal Prediction for Time Series

    arXiv:2608.10553v1 Announce Type: cross Abstract: Conformal prediction (CP) provides distribution-free prediction intervals for fixed forecasters, but its standard calibration procedure is often inefficient for time series data, where forecast errors are temporally dependent and …

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Retrieval-Corrected Conformal Prediction for Time Series

    Conformal prediction (CP) provides distribution-free prediction intervals for fixed forecasters, but its standard calibration procedure is often inefficient for time series data, where forecast errors are temporally dependent and change across time and operating conditions. Recen…

  3. arXiv cs.LG TIER_1 English(EN) · Joar Skalse, Edoardo Pona, Osvaldo Simeone, Nicola Paoletti ·

    Online Conformal Prediction Beyond Feedback

    arXiv:2608.07139v1 Announce Type: new Abstract: Uncertainty quantification is essential when deploying machine learning models in safety-critical applications. Online conformal prediction (OCP) provides theoretically principled uncertainty quantification for arbitrary black-box c…

  4. arXiv stat.ML TIER_1 English(EN) · Peter Cotton ·

    Marginally Useful: Formalizing the Information Gap in Conformal Prediction

    arXiv:2608.07479v1 Announce Type: cross Abstract: Conformal prediction gives finite-sample, distribution-free marginal coverage for a set. The guarantee is real, and it is often misread as evidence of forecast quality. We separate the two with one decomposition, which we call the…

  5. arXiv stat.ML TIER_1 English(EN) · Anton Conrad, Rustam Isaev, Denis Belomestny, Eric Moulines, Sergey Samsonov ·

    Beyond Marginal Validity: Finite-Sample Guarantees for Localized Conformal Prediction

    arXiv:2608.06206v1 Announce Type: new Abstract: Conformal prediction endows arbitrary black-box predictors with finite-sample, distribution-free marginal coverage, yet marginal validity can hide severe covariate-specific miscalibration, while exact distribution-free conditional c…

  6. arXiv stat.ML TIER_1 English(EN) · Vagner Santos, Victor Coscrato, Luben Cabezas, Rafael Izbicki, Thiago Ramos ·

    LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees

    arXiv:2602.22432v2 Announce Type: replace Abstract: Gradient-boosted decision trees are among the strongest off-the-shelf predictors for tabular regression, but point predictions alone do not quantify uncertainty. Conformal prediction provides distribution-free marginal coverage,…

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