Description: ICML 2023 Workshop on Duality Principles for Modern ML
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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
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.
Emtiyaz Khan's Homepage
Bayes Duality
J.J. (Jia-Jie) Zhu – Dynamics - Opt - ML
Dharmesh Tailor
Thomas Möllenhoff | About
Thomas Möllenhoff | About
Ronny Bergmann | Moin.
Peter Nickl