Position: Application-Driven Innovation in Machine Learning

David Rolnick, Alan Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi, Hannah Kerner, Claire Monteleoni, Esther Rolf, Milind Tambe, Adam White
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:42707-42718, 2024.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-rolnick24a, title = {Position: Application-Driven Innovation in Machine Learning}, author = {Rolnick, David and Aspuru-Guzik, Alan and Beery, Sara and Dilkina, Bistra and Donti, Priya L. and Ghassemi, Marzyeh and Kerner, Hannah and Monteleoni, Claire and Rolf, Esther and Tambe, Milind and White, Adam}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {42707--42718}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/rolnick24a/rolnick24a.pdf}, url = {https://proceedings.mlr.press/v235/rolnick24a.html}, 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.} }
Endnote
%0 Conference Paper %T Position: Application-Driven Innovation in Machine Learning %A David Rolnick %A Alan Aspuru-Guzik %A Sara Beery %A Bistra Dilkina %A Priya L. Donti %A Marzyeh Ghassemi %A Hannah Kerner %A Claire Monteleoni %A Esther Rolf %A Milind Tambe %A Adam White %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-rolnick24a %I PMLR %P 42707--42718 %U https://proceedings.mlr.press/v235/rolnick24a.html %V 235 %X 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.
APA
Rolnick, D., Aspuru-Guzik, A., Beery, S., Dilkina, B., Donti, P.L., Ghassemi, M., Kerner, H., Monteleoni, C., Rolf, E., Tambe, M. & White, A.. (2024). Position: Application-Driven Innovation in Machine Learning. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:42707-42718 Available from https://proceedings.mlr.press/v235/rolnick24a.html.

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