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
arXiv:2603. 05060v2 Announce Type: replace Abstract: Multi--task learning seeks to improve the generalization error by leveraging the common information shared by multiple related tasks.
By Ayed M. Alrashdi, Oussama Dhifallah, Houssem Sifaou
The paper introduces a theoretically grounded multi‑task learning framework, AMTIDIN, for joint interference detection, modulation identification, and interference identification. It derives an upper bound linking MTL performance to task similarity measured by Wasserstein distance and adaptive coefficients, and employs adversarial training to reduce distributional gaps across tasks. Experiments show AMTIDIN outperforms single‑task models and other MTL baselines, especially when training data is limited, signals are short, and SNRs are low.
By H. Xu, L. Hu, B. He, S. Wang
SIMS: Scale-Invariant Merit-Function-Based Scalarization for Multi-Task Learning proposes a new scalarization method for multi-task learning that is invariant to the relative scales of task losses. By using a logarithmic transformation, SIMS converts the multi-objective problem into a single objective that preserves weak Pareto optimality and allows a smooth surrogate with controllable approximation error. Experiments on standard multi-task benchmarks show that SIMS consistently outperforms existing scalarization methods and achieves state‑of‑the‑art performance.
By Zebin Chen, Fei Xing, Yang Chen, Hua Liu, Andy HF Chow, Yuhua Qian, Yu Zhang
The paper extends the Cooperative Multi-Task Semantic Communication (CMT‑SemCom) framework to jointly perform heterogeneous classification and regression tasks on the Cityscapes dataset, using an InfoMax principle to handle mixed discrete and continuous semantic variables. It compares the new framework against independent single‑task training, conventional task‑agnostic digital transmission, and single‑encoder multi‑decoder SemCom, and studies how the capacity of the common unit affects joint task performance. Extensive evaluations show that CMT‑SemCom outperforms all benchmarks.
By Ahmad Halimi Razlighi, Mohammad Siddiqur Rahman, Maximilian H. V. Tillmann, Edgar Beck, Armin Dekorsy
arXiv:2609.08615v1 Announce Type: new
Abstract: Dimensional attention in learning is often implemented as a globally shared attention vector, where each stimulus dimension corresponds to a single sca...
By Lenard Dome
arXiv:2607. 16233v1 Announce Type: cross Abstract: Integrating heterogeneous genomic and clinical modalities for joint cancer subtype classification and survival prediction remains a key challenge in precision oncology.
By Suxing Liu Byungwon Min