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arXiv:2608. 06206v1 Announce Type: cross Abstract: 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.
arXiv:2608. 08204v1 Announce Type: cross Abstract: This work proposes deep nonparametric Instrumental variable quantile regression (IVQR), a two-stage estimator that combines conditional diffusion modeling with a kernel-smoothed conditional moment formulation.
arXiv:2607. 26577v1 Announce Type: new Abstract: Adaptive conformal inference (ACI) of Gibbs and Cand{\`e}s and its variants are the standard approach to online conformal prediction under distribution shift, but they suffer from three fundamental limitations.
arXiv:2505.13299v2 Announce Type: replace-cross Abstract: This paper considers the estimation of quantiles via a smoothed version of the stochastic gradient descent (SGD) algorithm. By smoothing the...
arXiv:2606. 28652v1 Announce Type: cross Abstract: Online high-dimensional regression requires algorithms that can update sequentially while preserving structural sparsity.
Occupancy-based Quantile Risk Control (OQRC) is a new method that extends conformal risk control to quantile-based risk measures while avoiding excessive conservatism and providing rigorous finite-sample guarantees. It works by partitioning the loss space using ordered calibration losses, estimating the distribution of test losses in each bin, and bounding the risk by the maximum loss in each bin. The authors prove that OQRC achieves tight risk control bounds with a finite-sample guarantee that converges at a rate of π(n−½), and experiments show it can reduce the risk gap by up to 78.64% on common benchmarks.