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ICML 2020 : Beyond First Order Methods in Machine Learning

Vienna, Austria
Event Date: Jul 17, 2020 - Jul 18, 2020
Submission Deadline: Jun 12, 2020
Notification of Acceptance: Jun 19, 2020
Camera Ready Version Due : Jul 03, 2020

About

This workshop will attempt to shed light on this statement. Topics of interest include, but are not limited to, second-order methods, adaptive gradient descent methods, regularization techniques, as well as techniques based on higher-order derivatives. This workshop will bring machine learning and optimization researchers closer, in order to facilitate a discussion with regards to underlying questions such as the following:

  • Why are they not omnipresent?

  • Why are higher-order methods important in machine learning?

  • What advantages can they offer? What are their limitations and disadvantages?

  • How should (or could) they be implemented in practice?

Call For Paper

We welcome submissions to the workshop under the general theme of “Beyond First-Order Methods in ML systems”. Topics of interest include, but are not limited to,

  • Second-order methods

  • Quasi-Newton methods

  • Derivative-free methods

  • Distributed methods beyond first-order

  • Online methods beyond first-order

  • Applications of methods beyond first-order to diverse applications (e.g., training deep neural networks, natural language processing, dictionary learning, etc)

We encourage submissions that are theoretical, empirical or both.

Submissions should be up to 4 pages excluding references, acknowledgements, and supplementary material, and should follow ICML format. The CMT-based review process will be double-blind to avoid potential conflicts of interests; submit at (to be annnounced).

Accepted submissions will be presented as posters.

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