Multi-task learning (MTL) is a powerful machine learning paradigm designed to
leverage shared knowledge across tasks to improve generalization and
performance. Previous works have proposed approaches to MTL that can be divided
into feature learning, focused on the identification of a common feature
representation, and task clustering, where similar tasks are grouped together.
In this paper, we propose an MTL approach at the intersection between task
clustering and feature transformation based on a two-phase iterative
aggregation of targets and features. First, we propose a bias-variance analysis
for regression models with additive Gaussian noise, where we provide a general
expression of the asymptotic bias and variance of a task, considering a linear
regression trained on aggregated input features and an aggregated target. Then,
we exploit this analysis to provide a two-phase MTL algorithm (NonLinCTFA).
Firstly, this method partitions the tasks into clusters and aggregates each
obtained group of targets with their mean. Then, for each aggregated task, it
aggregates subsets of features with their mean in a dimensionality reduction
fashion. In both phases, a key aspect is to preserve the interpretability of
the reduced targets and features through the aggregation with the mean, which
is further motivated by applications to Earth science. Finally, we validate the
algorithms on synthetic data, showing the effect of different parameters and
real-world datasets, exploring the validity of the proposed methodology on
classical datasets, recent baselines, and Earth science applications.

提出了一种多任务学习方法，该方法基于任务聚类和特征变换，通过目标和特征的两阶段迭代聚合来提高回归模型的泛化能力，并在合成数据和真实世界的数据集上进行了验证。

基于偏差 - 方差分析的可解释性目标特征聚合多任务学习

Interpetable Target-Feature Aggregation for Multi-Task Learning based on  Bias-Variance Analysis

In order to learn quickly with few samples, meta-learning utilizes prior
knowledge learned from previous tasks. However, a critical challenge in
meta-learning is task uncertainty and heterogeneity, which can not be handled
via globally sharing knowledge among tasks. In this paper, based on
gradient-based meta-learning, we propose a hierarchically structured
meta-learning (HSML) algorithm that explicitly tailors the transferable
knowledge to different clusters of tasks. Inspired by the way human beings
organize knowledge, we resort to a hierarchical task clustering structure to
cluster tasks. As a result, the proposed approach not only addresses the
challenge via the knowledge customization to different clusters of tasks, but
also preserves knowledge generalization among a cluster of similar tasks. To
tackle the changing of task relationship, in addition, we extend the
hierarchical structure to a continual learning environment. The experimental
results show that our approach can achieve state-of-the-art performance in both
toy-regression and few-shot image classification problems.

本文提出了一种基于梯度的分层结构的元学习方法，该方法通过任务聚类结构以及自适应知识传递来解决元学习中任务不确定性和异质性问题，并在玩具回归和少样本图像分类问题中取得了最优表现。

分层结构元学习

Hierarchically Structured Meta-learning

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its
aim is to leverage useful information contained in multiple related tasks to
help improve the generalization performance of all the tasks. In this paper, we
give a survey for MTL from the perspective of algorithmic modeling,
applications and theoretical analyses. For algorithmic modeling, we give a
definition of MTL and then classify different MTL algorithms into five
categories, including feature learning approach, low-rank approach, task
clustering approach, task relation learning approach and decomposition approach
as well as discussing the characteristics of each approach. In order to improve
the performance of learning tasks further, MTL can be combined with other
learning paradigms including semi-supervised learning, active learning,
unsupervised learning, reinforcement learning, multi-view learning and
graphical models. When the number of tasks is large or the data dimensionality
is high, we review online, parallel and distributed MTL models as well as
dimensionality reduction and feature hashing to reveal their computational and
storage advantages. Many real-world applications use MTL to boost their
performance and we review representative works in this paper. Finally, we
present theoretical analyses and discuss several future directions for MTL.

本文综述了多任务学习的算法建模、任务聚类及半监督学习、真实世界应用等方面，并探讨其理论分析和未来方向。