With co-organizers Albert Berahas, Amir Gholami, Michael Mahoney and Fred Roosta, our workshop on optimization methods for ML has been accepted at ICML 2020!
Please visit the official website for more information.
Description
Description: In the last few decades, much effort has been devoted to the development of first-order methods. These methods enjoy a low per-iteration cost and have optimal complexity, are easy to implement, and have proven to be effective for most machine learning applications. In contrast, higher-order methods, such as Newton, quasi-Newton and adaptive gradient descent methods, are extensively used in many scientific and engineering domains. At least in theory, these methods possess several nice features: they exploit local curvature information to mitigate the effects of ill-conditioning, they avoid or diminish the need for hyper-parameter tuning, and they have enough concurrency to take advantage of distributed computing environments. However, often higher-order methods are “undervalued.”
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, and what advantages can they offer?
- What are their limitations and disadvantages?
- How should (or could) they be implemented in practice?
Call for Papers
We welcome submissions to the workshop under the general theme of “Beyond First-Order Optimization Methods in Machine Learning”. 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.