arXiv Machine Learning
Aug 18

Convolution Smoothed Quantile Regression for XGBoost

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.

By Mandy Yao (University of Toronto), Meredith Franklin (University of Toronto)
arXiv Machine Learning
Oct 22

Residual-Based Causal Discovery via Pretrained In-Context Quantile Regression

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.

By Daniel Klippert, Quang Nhat Huynh, Alexander Marx