RAT reformulates regularized natural policy gradients as vanilla gradients with a transformed advantage, computed efficiently via randomized block Kaczmarz iterations on on-policy data.
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Clipped least-squares importance fitting enables weighted conformal prediction to achieve dataset-conditional coverage guarantees under unbounded covariate shifts by bounding undercoverage and estimating a corrective inflation factor from data.
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Randomized Advantage Transformation (RAT): Computing Natural Policy Gradients via Direct Backpropagation
RAT reformulates regularized natural policy gradients as vanilla gradients with a transformed advantage, computed efficiently via randomized block Kaczmarz iterations on on-policy data.
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Weight Clipping for Robust Conformal Inference under Unbounded Covariate Shifts
Clipped least-squares importance fitting enables weighted conformal prediction to achieve dataset-conditional coverage guarantees under unbounded covariate shifts by bounding undercoverage and estimating a corrective inflation factor from data.