Application-Driven Innovation in Machine Learning
This addresses a systemic issue for the machine learning research community by advocating for a shift in practices to support innovation, though it is incremental as it builds on existing discussions without introducing new methods or data.
The paper tackles the challenge of fostering application-driven research in machine learning by contrasting it with methods-driven approaches and highlighting its benefits and synergies, resulting in an analysis that identifies barriers in reviewing, hiring, and teaching practices and proposes improvements.
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.