Application-Driven Innovation in Machine Learning

TL;DR

This paper advocates for greater emphasis on application-driven research in machine learning, highlighting its potential to foster impactful innovations and suggesting improvements in academic practices to support this paradigm.

Contribution

It introduces the paradigm of application-driven machine learning research, contrasting it with traditional methods-driven approaches, and discusses ways to enhance its integration and recognition.

Findings

Application-driven research can lead to significant real-world impact.

Current academic practices often undervalue application-driven innovation.

Proposed improvements in review, hiring, and teaching practices to support application-driven work.

Abstract

In this position paper, we argue that application-driven research has been systemically under-valued in the machine learning community. 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.