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Conformal Prediction via Transported Beta Laws

stat.ML · 2026-05-18 · unverdicted · novelty 7.0

The paper derives that calibration-conditional coverage follows a Beta(k, n+1-k) law under continuous i.i.d. exchangeability and quantifies non-i.i.d. departures via Wasserstein distances on transported beta laws, yielding explicit bounds in scale-shift, clustered, and mixing regimes.

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  • Conformal Prediction via Transported Beta Laws stat.ML · 2026-05-18 · unverdicted · none · ref 7

    The paper derives that calibration-conditional coverage follows a Beta(k, n+1-k) law under continuous i.i.d. exchangeability and quantifies non-i.i.d. departures via Wasserstein distances on transported beta laws, yielding explicit bounds in scale-shift, clustered, and mixing regimes.