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Aug 2026
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 coverage is finite-sample unattainable. Randomly localized conformal prediction (RLCP) mi...
Anton Conrad, R. Isaev, D. Belomestny et al.
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In this paper, we study nonasymptotic $L^p$ error bounds for interval length and conditional coverage in split conformalized quantile regression (CQR). Our bounds rely on local regularity conditions and accuracy guarantees for the estimated quantiles. We further instantiate our bounds for quantile regression with spars...
R. Isaev, Anton Conrad, D. Belomestny et al.
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A statistical framework for Inverse Entropy-regularized Reinforcement Learning is developed that resolves ambiguity by combining entropy regularization with a least-squares reconstruction of the reward from the soft Bellman residual, leading to a unique and well-defined so-called least-squares reward consistent with th...
D. Belomestny, Alexey Naumov, Sergey Samsonov
· arXiv.org · 0 citations
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