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Adaptive Conformal Inference: online miscoverage feedback under shift

Contribution grade

AI uncertainty, reliability and selective prediction · Human-designed research method or resource

Reference score155,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Updates a single miscoverage parameter from observed errors to adapt prediction sets online, with long-run realized coverage-frequency analysis and finite-horizon discrepancy bounds.

Scientific scope

Updates a single miscoverage parameter from observed errors to adapt prediction sets online, with long-run realized coverage-frequency analysis and finite-horizon discrepancy bounds. Long-run empirical frequency differs from per-time marginal or conditional coverage, and can require empty/full sets under endpoint conventions. Stronger local tracking needs further distributional assumptions; step size trades responsiveness for volatility. V1 notation conventions were not independently implemented or certified.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Long-run empirical frequency differs from per-time marginal or conditional coverage, and can require empty/full sets under endpoint conventions. Stronger local tracking needs further distributional assumptions; step size trades responsiveness for volatility. V1 notation conventions were not independently implemented or certified.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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