On April 29, 2022 Pierre-André Noël and David Vasquez from ServiceNow Research will be co-hosting a Workshop on Anchoring Machine Learning in Classical Algorithmic Theory as part of the International Conference on Learning Representations (ICLR).
The GoundedML workshop seeks to bring together researchers from both the algorithmic theory and Machine Learning (ML) communities to initiate a dialogue on how ideas from theoretical algorithm design can inspire and guide future research in ML.
Recent advances in ML have revolutionized our ability to solve complex problems in a myriad of application domains. Yet, just as empirical data plays a fundamental role in the development of such applications, the process of designing these methods has also remained empirical: we have learned which of the known methods tend to perform better for certain types of problems and have developed intuition guiding our discovery of new methods.
In contrast, classical algorithmic theory provides tools directly addressing the mathematical core of a problem, and clear theoretical justifications motivate powerful design techniques. At the heart of this process is the analysis of the correctness and time/space efficiency of an algorithm, providing actionable bounds and guarantees. Problems themselves may be characterized by bounding the performance of any algorithm, providing a meaningful reference point to which concrete algorithms may be compared. While ML models may appear to be an awkward fit for such techniques, some research in the area has succeeded in obtaining results with the “definitive” flavor associated to algorithms, complementary to empirical ones. Are such discoveries bound to be exceptions, or can they be part of a new algorithmic theory?
Interested in submitting a paper? We are looking for contributions addressing topics in four areas of study: algorithmic guarantees, characterization, design paradigms of machine learning algorithms, and datasets. Visit the workshop’s website for further detail and a complete list of topics. Please also make sure to review the submission guidelines before sending your proposal.
Papers will be peer-reviewed under a double-blind policy and the submission deadline is February 26, 2022, at 12:00 AM UTC. All accepted papers will be presented as posters and two will be selected as orals. Awards will be given to the best papers!
Got questions? Contact the organizers at WorkshopGroundedML@gmail.com.
General Chairs include Perouz Taslakian (Samsung Electronics), Pierre-André Noël (ServiceNow Research), David Vazquez (ServiceNow Research), Jian Tang (MILA), and Xavier Bresson (National University of Singapore).
We are excited to help bring this workshop together and hope to see you there!
Are you interested in doing fundamental or applied research at ServiceNow?
If you are interested in exploring full-time career opportunities with the ServiceNow Research team or wish to learn more about the part-time Visiting Researcher Program (for research internships), please take a moment to fill out this form so that hiring managers can learn more about your background and we can contact you about our current openings. Please note that ServiceNow Research internships through our Visiting Researcher Program start and run all year and are not limited to "seasonal" applications.
Follow @ServiceNowRSRCH on Twitter for our latest news, and updates from the community, and to get in touch.
© 2022 ServiceNow, Inc. All rights reserved. ServiceNow, the ServiceNow logo, Now, and other ServiceNow marks are trademarks and/or registered trademarks of ServiceNow, Inc. in the United States and/or other countries. Other company names, product names, and logos may be trademarks of the respective companies with which they are associated.