The paper introduces COVER, a multi‑task learning framework that regularizes covariate overlap to mitigate the negative effects of sharing information across tasks with differing covariate distributions and response relationships. COVER blends a common component function, a shared neural representation, and low‑dimensional task‑specific coefficients, using taskwise second‑moment matrices to guide coefficient integration. The authors provide theoretical bias‑variance analysis, oracle inequalities, and neural‑network convergence rates, and demonstrate that COVER outperforms existing deep‑learning and statistical integration methods in simulations and a GTEx central‑nervous‑system study.
By Yang Sui, Qi Xu, Yang Bai, Annie Qu
arXiv:2607. 02681v1 Announce Type: cross Abstract: Integrating information across related tasks can improve estimation and prediction in transfer, multi-task, and federated learning, but contamination and heterogeneity make robust borrowing challenging.
By Ye Tian, Mengchu Li, Marco Avella Medina
arXiv:2607. 16554v1 Announce Type: cross Abstract: In multi-task learning (MTL) negative transfer is often considered as an optimization artifact, but it can also be viewed as a consequence of limited shared capacity and weak task redundancy.
By Asif Khan
The paper investigates multitask regression where different predictors may have varying degrees of coefficient sharing across tasks. It introduces a framework that quantifies sharing by the number of active predictors and the total number of task-specific coefficient deviations, and proposes a pairwise penalty estimator that achieves matching upper and lower bounds in terms of these quantities. The method also handles scenarios where a large subset of tasks shares an identical coefficient vector, with explicit sample‑size conditions ensuring exact pooling of those tasks while allowing others to differ.
By Xiaodong Li, Zhentao Li
arXiv:2512. 17678v2 Announce Type: replace-cross Abstract: Selecting compact and informative gene subsets from single-cell transcriptomic data is essential for biomarker discovery, improving interpretability, and cost-effective profiling.
By Daphn\'e Chopard, Jorge da Silva Gon\c{c}alves, Irene Cannistraci, Thomas M. Sutter, Julia E. Vogt
arXiv:2604. 07848v2 Announce Type: replace Abstract: Multi-task learning shows strikingly inconsistent results -- sometimes joint training helps substantially, sometimes it actively harms performance -- yet the field lacks a principled framework for predicting these outcomes.
By Jasper Zhang, Bryan Cheng