[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117174-en":3,"doc-seo-117174-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117174,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Application-Driven Innovation in Machine Learning","Application-driven research has been systematically under-valued in the machine learning community despite the rapid proliferation of ML applications across healthcare, climate science, and heavy industry. This position paper presents the paradigm of application-driven ML research and contrasts it with methods-driven research, showing how application-grounded challenges can advance both real-world impact and core ML capabilities. It also identifies how reviewing, hiring, and teaching practices discourage application-driven innovation and proposes improvements to support this approach.","Position: Application-Driven Innovation in Machine Learning  \nDavid Rolnick 1 Alan Aspuru-Guzik 2 Sara Beery 3 Bistra Dilkina 4 Priya L. Donti 3 Marzyeh Ghassemi 3 Hannah Kerner 5 Claire Monteleoni 6 7 Esther Rolf 8 7 Milind Tambe 8 Adam White 9  \narXiv :2403 . 17381v2 [ cs .LG] 29 Dec 2025  \nAbstract  \nIn 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.  \n1. Introduction  \nMachine learning (ML) is increasingly being used across diverse fields and sectors, with significant impacts for society. ML is being used in healthcare to analyze genetic markers, process medical imagery, and digitize health records (Ghassemi et al., 2020) . ML is being used in climate science to speed up physical simulations, parse satellite data, and forecast extreme events (Monteleoni et al., 2013 ; Rolnick et al., 2022) . ML is being used in heavy industry to control  \n1McGill University and Mila – Quebec AI Institute, Montreal, Canada 2University of Toronto and Vector Institute, Toronto, Canada 3Massachusetts Institute of Technology, Cambridge, USA 4University of Southern California, Los Angeles, USA 5Arizona State University, Tempe, USA 6Inria Paris, Paris, France 7University of Colorado Boulder, Boulder, USA 8Harvard University, Cambridge, USA 9University of Alberta and Alberta Machine Intelligence Institute, Edmonton, Canada. Correspondence to: David Rolnick \u003C[drolnick@cs.mcgill.ca](drolnick@cs.mcgill.ca) >.  \nProceedings of the 41 st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024 . Copyright 2024 by the author(s) .  \ncomplex processes, optimize supply chains, and design new materials (Gopaluni et al., 2020) . These kinds of applications, and many others (Wang et al., 2023a), involve diverse ML tools being used in a multiplicity of ways.  \nThe widespread use of machine learning across society draws on decades of innovation in core ML algorithms, but also on another type of ML research: Application-driven innovation. ML algorithms designed in blue-sky, methodsfocused research continue to fall short when used directly for applications. Bridging the gap requires thoughtful consideration of the challenges of real-world tasks and the properties of real-world data, and the welcoming of use cases into the research process. This approach to ML research has much to contribute to broader innovation in ML methods, as well as downstream applications. However, application-driven innovation has characteristics that have led too often to it being under-valued within the ML community or perceived as out-of-scope (Rudin & Wagstaff, 2014) .  \nIn this paper, we frame the paradigm of application-driven ML (ADML) research, propose where it fits within the ML research landscape, and discuss why it is important not just merely to applications but to advancing ML methods. We reflect on common failures in understanding ADML work during reviewing, hiring, and teaching, and how such factors serve to strongly disincentivize application-oriented work within ML, in both academia and industry research. Finally, we suggest steps toward an ML research ecosystem in which application-driven approaches are","cbCaicHRVM8YYzOg","https://ap.wps.com/l/cbCaicHRVM8YYzOg","pdf",639180,1,12,"English","en",105,"# Introduction\n## Machine learning applications and the gap\n# Paradigms of Innovation in ML\n## Methods-Driven Research\n## Application-Driven Innovation","[{\"question\":\"What is application-driven innovation in machine learning, and how does it differ from methods-driven research?\",\"answer\":\"Application-driven innovation designs algorithms around challenges from real-world problems. Methods-driven research focuses on algorithms’ target properties evaluated on standardized benchmarks.\"},{\"question\":\"Why does the paper argue that application-driven research is under-valued?\",\"answer\":\"The paper finds reviewing, hiring, and teaching practices often fail to recognize application-driven work, creating strong disincentives for application-oriented research in academia and industry.\"},{\"question\":\"How can application-driven work benefit machine learning beyond downstream applications?\",\"answer\":\"The paper argues application-driven approaches can advance ML methods themselves by requiring thoughtful treatment of real-world task challenges and the properties of real-world data.\"}]","Application-Driven Innovation in Machine Learning | PDF",1785674220,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"application-driven-innovation-in-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/application-driven-innovation-in-machine-learning/117174/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is application-driven innovation in machine learning, and how does it differ from methods-driven research?","Question",{"text":75,"@type":76},"Application-driven innovation designs algorithms around challenges from real-world problems. Methods-driven research focuses on algorithms’ target properties evaluated on standardized benchmarks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why does the paper argue that application-driven research is under-valued?",{"text":80,"@type":76},"The paper finds reviewing, hiring, and teaching practices often fail to recognize application-driven work, creating strong disincentives for application-oriented research in academia and industry.",{"name":82,"@type":73,"acceptedAnswer":83},"How can application-driven work benefit machine learning beyond downstream applications?",{"text":84,"@type":76},"The paper argues application-driven approaches can advance ML methods themselves by requiring thoughtful treatment of real-world task challenges and the properties of real-world data.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]