As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly important. Such work offers the potential for significant impact not merely in domains of application but also in machine learning itself. In this paper, we describe the paradigm of application-driven research in machine learning, contrasting it with the more standard paradigm of methods-driven research. We illustrate the benefits of application-driven machine learning and how this approach can productively synergize with methods-driven work. Despite these benefits, we find that reviewing, hiring, and teaching practices in machine learning often hold back application-driven innovation. We outline how these processes may be improved.

中文翻译：

---

机器学习中应用驱动的创新

随着机器学习应用的激增，受特定现实挑战启发的创新算法变得越来越重要。此类工作不仅在应用领域而且在机器学习本身方面都具有产生重大影响的潜力。在本文中，我们描述了机器学习中应用驱动研究的范式，并将其与更标准的方法驱动研究范式进行了对比。我们说明了应用程序驱动的机器学习的好处，以及这种方法如何与方法驱动的工作有效地协同作用。尽管有这些好处，我们发现机器学习中的审查、招聘和教学实践往往会阻碍应用程序驱动的创新。我们概述了如何改进这些流程。