Breiman's classic paper casts data analysis as a choice between two cultures:
data modelers and algorithmic modelers. Stated broadly, data modelers use
simple, interpretable models with well-understood theoretical properties to
analyze data. Algorithmic modelers prioritize predictive accuracy and use more
flexible function approximations to analyze data. This dichotomy overlooks a
third set of models $-$ mechanistic models derived from scientific theories
(e.g., ODE/SDE simulators). Mechanistic models encode application-specific
scientific knowledge about the data. And while these categories represent
extreme points in model space, modern computational and algorithmic tools
enable us to interpolate between these points, producing flexible,
interpretable, and scientifically-informed hybrids that can enjoy accurate and
robust predictions, and resolve issues with data analysis that Breiman
describes, such as the Rashomon effect and Occam's dilemma. Challenges still
remain in finding an appropriate point in model space, with many choices on how
to compose model components and the degree to which each component informs
inferences.

该论文提出数据分析涉及数据建模和算法建模两种文化之间的选择，而机械模型则提供了一种有效的解决方案，可以生产灵活、可解释、科学的混合模型，以获得准确和稳健的预测，并且可以解决数据分析中的一些挑战。

Breiman 的两种文化：无需选边站

Breiman's two cultures: You don't have to choose sides

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。