Conformal Point and the Calibrated Conic

2026-01-16 · Richard Hartley arxiv

This gives some information about the conformal point and the calibrating conic, and their relationship one to the other. These concepts are useful for visualizing image geometry, and lead to intuitive ways to compute geometry, such as angles and directions in an image.

📄 PDF Abstract BibTeX arXiv:2601.11679

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Self-Calibrating Conformal Prediction

2024-02-11 · Lars van der Laan, Ahmed M. Alaa

In machine learning, model calibration and predictive inference are essential for producing reliable predictions and quantifying uncertainty to support decision-making. Recognizing the complementary roles of point and in…

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Generalized Conformal Predictive Systems Under Distributional Shifts

2026-06-09 · Jef Jonkers, Johanna Ziegel arxiv

Conformal predictive systems (CPS) output calibrated bands of CDFs under exchangeability. We extend generalized CPS to non-exchangeable settings by encoding distributional shifts through observation-specific permutation …

Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction

2025-02-08 · Lars van der Laan, Ahmed Alaa

Ensuring model calibration is critical for reliable predictions, yet popular distribution-free methods, such as histogram binning and isotonic regression, provide only asymptotic guarantees. We introduce a unified framew…

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Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction

2024-02-02 · Ziqi Ma, Kamyar Azizzadenesheli, Anima Anandkumar

Operator learning has been increasingly adopted in scientific and engineering applications, many of which require calibrated uncertainty quantification. Since the output of operator learning is a continuous function, qua…

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Adaptive Regime-Switching Forecasts with Distribution-Free Uncertainty: Deep Switching State-Space Models Meet Conformal Prediction

2025-12-02 · Echo Diyun LU, Charles Findling, Marianne Clausel, Alessandro Leite 외 arxiv

Regime transitions routinely break stationarity in time series, making calibrated uncertainty as important as point accuracy. We study distribution-free uncertainty for regime-switching forecasting by coupling Deep Switc…

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