This paper examines an online multi-task learning (OMTL) method, which
processes data sequentially to predict labels across related tasks. The
framework learns task weights and their relatedness concurrently. Unlike
previous models that assumed static task relatedness, our approach treats tasks
as initially independent, updating their relatedness iteratively using newly
calculated weight vectors. We introduced three rules to update the task
relatedness matrix: OMTLCOV, OMTLLOG, and OMTLVON, and compared them against a
conventional method (CMTL) that uses a fixed relatedness value. Performance
evaluations on three datasets a spam dataset and two EEG datasets from
construction workers under varying conditions demonstrated that our OMTL
methods outperform CMTL, improving accuracy by 1\% to 3\% on EEG data, and
maintaining low error rates around 12\% on the spam dataset.

本研究探讨了一种在线多任务学习（OMTL）方法，该方法按顺序处理数据以预测相关任务的标签，并同时学习任务权重及其相互关系。通过引入三种规则来更新任务相互关系矩阵：OMTLCOV、OMTLLOG 和 OMTLVON，与使用固定相关性值的传统方法（CMTL）相比较，我们的方法在三个数据集上进行的性能评估表明，OMTL 方法在 EEG 数据上将准确率提高了 1％至 3％，并在垃圾邮件数据集上保持了大约 12％的低错误率。

多任务及其关系的在线学习：针对垃圾邮件数据和建筑领域脑电信号的测试

Online Learning of Multiple Tasks and Their Relationships : Testing on  Spam Email Data and EEG Signals Recorded in Construction Fields

This paper introduces two novel approaches for Online Multi-Task Learning
(MTL) Regression Problems. We employ a high performance graph-based MTL
formulation and develop its recursive versions based on the Weighted Recursive
Least Squares (WRLS) and the Online Sparse Least Squares Support Vector
Regression (OSLSSVR). Adopting task-stacking transformations, we demonstrate
the existence of a single matrix incorporating the relationship of multiple
tasks and providing structural information to be embodied by the MT-WRLS method
in its initialization procedure and by the MT-OSLSSVR in its multi-task kernel
function. Contrasting the existing literature, which is mostly based on Online
Gradient Descent (OGD) or cubic inexact approaches, we achieve exact and
approximate recursions with quadratic per-instance cost on the dimension of the
input space (MT-WRLS) or on the size of the dictionary of instances
(MT-OSLSSVR). We compare our online MTL methods to other contenders in a
real-world wind speed forecasting case study, evidencing the significant gain
in performance of both proposed approaches.

该论文介绍了两种新颖的在线多任务学习回归问题的方法，分别基于高性能的基于图的多任务学习和基于加权递归最小二乘法（WRLS）和在线稀疏最小二乘支持向量回归（OSLSSVR）的递归版本。通过任务堆叠变换，我们证明存在一个单一矩阵，其中包含多个任务的关系，并提供结构信息供 MT-WRLS 方法在其初始化过程中和 MT-OSLSSVR 的多任务核函数中体现。与现有的以在线梯度下降（OGD）或不精确立方方法为主的文献形成对比，我们实现了在输入空间维度（MT-WRLS）或实例字典大小（MT-OSLSSVR）上具有二次每实例成本的精确和近似递归。我们通过一个真实的风速预测案例研究将我们的在线多任务学习方法与其他竞争者进行比较，证明了两种提出的方法在性能上的显著收益。

在线多任务学习的递归最小二乘和递归核方法

Online Multi-Task Learning with Recursive Least Squares and Recursive  Kernel Methods

We study the problem of online multi-task learning where the tasks are
performed within similar but not necessarily identical multi-armed bandit
environments. In particular, we study how a learner can improve its overall
performance across multiple related tasks through robust transfer of knowledge.
While an upper confidence bound (UCB)-based algorithm has recently been shown
to achieve nearly-optimal performance guarantees in a setting where all tasks
are solved concurrently, it remains unclear whether Thompson sampling (TS)
algorithms, which have superior empirical performance in general, share similar
theoretical properties. In this work, we present a TS-type algorithm for a more
general online multi-task learning protocol, which extends the concurrent
setting. We provide its frequentist analysis and prove that it is also
nearly-optimal using a novel concentration inequality for multi-task data
aggregation at random stopping times. Finally, we evaluate the algorithm on
synthetic data and show that the TS-type algorithm enjoys superior empirical
performance in comparison with the UCB-based algorithm and a baseline algorithm
that performs TS for each individual task without transfer.

本研究针对具有相似但不完全相同的多臂赌博机环境中的在线多任务学习问题，研究了如何通过知识的健壮传递从而提高学习器在多个相关任务上的整体性能。我们提出了一种 TS 类型算法，对其进行了经验分析，并证明了它是几乎最优的。最后，我们将算法在合成数据上进行了评估，证明了 TS 类型算法在与基准算法和 UCB 算法的比较中具有卓越的经验性能。