@StatMLPapers
arxiv.org Data-Efficient Kernel Methods for Learning Differential Equations... We introduce a novel kernel-based framework for learning differential equations and their solution maps. The framework is efficient in its data requirements in terms of the number of observed... 
arxiv.org CausalGrade: Evidence-Guided Causal Graph Scoring with Jev Evaluating the structural quality of candidate causal graphs when the true causal structure is unknown is an important problem in applying causal discovery results. This paper proposes... arxiv.org The Geometry of Multitask Learning: Sharp Rates for Shared... Learning a shared subspace in multitask learning is an important statistical problem, yet exact rates remain poorly understood in many of the formal settings considered in the literature. To... arxiv.org INLA for Adaptive UAV Sampling of Radio Environment Maps in IoT... We study adaptive UAV sampling for radio environment map (REM) construction from non-Gaussian IoT observations. Integrated nested Laplace approximation(INLA) updates a Matérn GMRF posterior,... arxiv.org Robust-by-Design Distributional Learning from Contaminated Samples Modern data analysis pipelines increasingly rely on large datasets assembled through retrieval, scraping, automated filtering, and post-hoc curation, where the observed sample is often a...