Approximate full-conformal multi-task regression with reproducing kernels

arXiv:2607.00645 · math.ST, stat.ML · Submitted 2026-07-01 · Read on arXiv

Davidson Lova Razafindrakoto, Alain Celisse, Jérôme Lacaille

SAMM · SAMM

math.ST, stat.ML

Submitted: 2026-07-01

Code: https://github.com/Davidson-Lova/approximate_

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Regularization — 30× in this paper · explained in 3 episodes
• Conformal Prediction — 26× in this paper · explained in 5 episodes
• Empirical Coverage — 13× in this paper · explained in 2 episodes
• Algorithmic Stability — 11× in this paper · explained in 1 episode
• Hilbert Space — 11× in this paper · explained in 2 episodes
• Reproducing Kernel Hilbert Space — 6× in this paper
• Adaptation — 3× in this paper · explained in 1 episode
• Reproducing Kernel Hilbert Space (RKHS) — 3× in this paper · explained in 3 episodes
• Score Function — 3× in this paper · explained in 4 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Initial Data — 2× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Ambient Dimension — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Copula — 1× in this paper · explained in 2 episodes
• Influence Functions — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes

Sources

• Minimum Volume Conformal Sets for Multivariate Regression
• Statistical Properties of the log-cosh Loss Function Used in Machine Learning

Related papers

• Conformal Prediction for Dyadic Regression Under Complex Missingness
• Bentkus-type asymptotic e-values
• High-Dimensional Asymptotics of Differentially Private PCA
• KL Convergence Guarantees for Score diffusion models under minimal data assumptions
• Geometric bias in eigenspace perturbation under random heterogeneous noise
• On the Asymptotic Inadmissibility of Double Machine Learning Estimators Under Structure-Agnostic Models