arXiv:2608. 15290v1 Announce Type: cross Abstract: The increasing availability of large and complex datasets across many scientific disciplines has led to widespread adoption of machine learning (ML) for prediction.
arXiv:2606. 00265v1 Announce Type: cross Abstract: We study quantile regression in an extrapolation regime where the covariate takes unusually large values.
arXiv:2608.21652v1 Announce Type: cross Abstract: Neural-network surrogate models are increasingly used to accelerate scientific simulations, but their deployment in extrapolative and autoregressive...
Residual-Based Causal Discovery via Pretrained In-Context Quantile Regression proposes Loscar, an extension of the RESIT framework to location-scale noise models (LSNMs). Loscar estimates conditional mean and scale using quantile regression, allowing pretrained tabular foundation models to serve as plug‑in estimators. Experiments on bivariate and multivariate causal‑discovery benchmarks show that in‑context quantile regression outperforms a feasible generalized least squares (FGLS) variant, especially when causes are multivariate.
arXiv:2607. 04431v2 Announce Type: replace-cross Abstract: Quantile regression provides a powerful tool for summarizing the conditional distribution of a real-valued random variable (r.
arXiv:2607. 04431v1 Announce Type: cross Abstract: Quantile regression provides a powerful tool for summarizing the conditional distribution of a real valued random variable (r.
The paper introduces GBFRVFL, a fuzzy granular-ball random vector functional link network designed to improve robustness in noisy, imbalanced, or uncertain data settings. It employs granular-ball computing to group raw samples into adaptive balls and proposes two membership assignment schemes: F-GBRVFL, which uses fuzzy membership to gauge ball reliability, and SDAP-GBRVFL, which introduces a statistical density‑adaptive Pythagorean membership that adjusts based on class variance, local sparsity, and ball compactness. Experiments on 37 UCI and KEEL datasets show that these models outperform baseline methods in both clean and noisy conditions, achieving higher accuracy and stability.
arXiv:2606. 28652v1 Announce Type: cross Abstract: Online high-dimensional regression requires algorithms that can update sequentially while preserving structural sparsity.
arXiv:2606. 25188v1 Announce Type: new Abstract: Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning.
arXiv:2411.02771v3 Announce Type: replace-cross Abstract: Doubly robust estimators are widely used for estimating average treatment effects and other linear summaries of regression functions. While c...
arXiv:2511. 18945v4 Announce Type: replace Abstract: We propose a fully data-driven approach to designing mutual information (MI) estimators.
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.