Multi-task learning (MTL) is a learning paradigm that enables the simultaneous training of multiple communicating algorithms. Although MTL has been successfully applied to ether regression or classification tasks alone, incorporating mixed types of tasks into a unified MTL framework remains challenging, primarily due to variations in the magnitudes of losses associated with different tasks. This challenge, particularly evident in MTL applications with joint feature selection, often results in biased selections. To overcome this obstacle, we propose a provable loss weighting scheme that analytically determines the optimal weights for balancing regression and classification tasks. This scheme significantly mitigates the otherwise biased feature selection. Building upon this scheme, we introduce MTLComb, an MTL algorithm and software package encompassing optimization procedures, training protocols, and hyperparameter estimation procedures. MTLComb is designed for learning shared predictors among tasks of mixed types. To showcase the efficacy of MTLComb, we conduct tests on both simulated data and biomedical studies pertaining to sepsis and schizophrenia.

翻译：多任务学习（MTL）是一种能够同时训练多个通信算法的学习范式。尽管MTL已成功应用于单独的回归或分类任务，但将混合类型的任务纳入统一MTL框架仍具挑战性，主要原因是不同任务相关损失量级的差异。这一挑战在涉及联合特征选择的MTL应用中尤为突出，常导致有偏的特征选择。为克服这一障碍，我们提出了一种可证明的损失加权方案，该方案通过分析确定回归与分类任务的最优权重，从而显著缓解原本存在的特征选择偏差。基于此方案，我们开发了MTLComb——包含优化流程、训练协议及超参数估计程序的MTL算法与软件包。MTLComb专为混合类型任务间的共享预测器学习而设计。为验证MTLComb的有效性，我们在模拟数据及涉及脓毒症与精神分裂症的生物医学研究中进行了测试。