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Non-Asymptotic Analysis of Efficiency in Conformalized Regression
Yunzhen Yao, Lie He, Michael Gastpar
https://arxiv.org/abs/2510.07093 https://arxiv.org/p…

Non-Asymptotic Analysis of Efficiency in Conformalized Regression
Conformal prediction provides prediction sets with coverage guarantees. The informativeness of conformal prediction depends on its efficiency, typically quantified by the expected size of the prediction set. Prior work on the efficiency of conformalized regression commonly treats the miscoverage level $α$ as a fixed constant. In this work, we establish non-asymptotic bounds on the deviation of the prediction set length from the oracle interval length for conformalized quantile and median regre…

Replaced article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- Multiscale Quantile Regression with Local Error Control
Zhi Liu, Housen Li
ht…

An efficient algorithm for kernel quantile regression
Shengxiang Deng, Xudong Li, Yangjing Zhang
https://arxiv.org/abs/2510.07929 https://arxiv.org/pdf/251…

An efficient algorithm for kernel quantile regression
Kernel Quantile Regression (KQR) extends classical quantile regression to nonlinear settings using kernel methods, offering powerful tools for modeling conditional distributions. However, its application to large-scale datasets is severely limited by the computational burden of the large, dense kernel matrix and the need to efficiently solve large, often ill-conditioned linear systems. Existing state-of-the-art solvers usually struggle with scalability. In this paper, we propose a novel and hig…

On function-on-function linear quantile regression
Muge Mutis, Ufuk Beyaztas, Filiz Karaman, Han Lin Shang
https://arxiv.org/abs/2510.10792 https://arxiv.o…

On function-on-function linear quantile regression
We present two innovative functional partial quantile regression algorithms designed to accurately and efficiently estimate the regression coefficient function within the function-on-function linear quantile regression model. Our algorithms utilize functional partial quantile regression decomposition to effectively project the infinite-dimensional response and predictor variables onto a finite-dimensional space. Within this framework, the partial quantile regression components are approximated …

Adapting Noise to Data: Generative Flows from 1D Processes
Jannis Chemseddine, Gregor Kornhardt, Richard Duong, Gabriele Steidl
https://arxiv.org/abs/2510.12636 https://

Adapting Noise to Data: Generative Flows from 1D Processes
We introduce a general framework for constructing generative models using one-dimensional noising processes. Beyond diffusion processes, we outline examples that demonstrate the flexibility of our approach. Motivated by this, we propose a novel framework in which the 1D processes themselves are learnable, achieved by parameterizing the noise distribution through quantile functions that adapt to the data. Our construction integrates seamlessly with standard objectives, including Flow Matching an…