Duality Principles for Modern Machine Learning

The ICML Duality Principles workshop brings together researchers working on various duality concepts from many different fields to discuss new applications for modern machine learning, especially focusing on topics such as model understanding, explanation, and adaptation in deep learning and reinforcement learning.

Duality is a pervasive and important principle in mathematics. Not only has it fascinated researchers in many different fields but it has also been used extensively in optimization, statistics, and machine-learning, giving rise to powerful tools such as

• Fenchel duality in convex optimization,
• Representer theorems in kernel methods and Bayesian nonparametrics,
• Dually-flat spaces in information geometry.

Duality played an important role in the past, but lately we do not see much work on duality principles, especially in deep learning. For example, Lagrange duality can be useful for model explanation because it allows us to measure sensitivity of certain perturbations, but this is not yet fully exploited. This slowdown is perhaps due to a growing focus on nonconvex and nonlinear problems where duality does not seem to be directly applicable.

With this workshop, we aim to revive the interest of the ML community in duality principles.

See our Call for Papers here.

This workshop is partially supported by the Bayes-Duality Project, JST CREST Grant Number JPMJCR2112: “A new Bayes-duality principle for adaptive, robust, and lifelong learning of AI systems”.