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New Frontiers in Learning, Control, and Dynamical Systems
Valentin De Bortoli · Charlotte Bunne · Guan-Horng Liu · Tianrong Chen · Maxim Raginsky · Pratik Chaudhari · Melanie Zeilinger · Animashree Anandkumar

Fri Jul 28 12:00 PM -- 07:00 PM (PDT) @ Ballroom B
Event URL: https://frontiers4lcd.github.io »

Recent advances in algorithmic design and principled, theory-driven deep learning architectures have sparked a growing interest in control and dynamical system theory. Complementary, machine learning plays an important role in enhancing existing control theory algorithms in terms of performance and scalability. The boundaries between both disciplines are blurring even further with the rise of modern reinforcement learning, a field at the crossroad of data-driven control theory and machine learning. This workshop aims to unravel the mutual relationship between learning, control, and dynamical systems and to shed light on recent parallel developments in different communities. Strengthening the connection between learning and control will open new possibilities for interdisciplinary research areas.

Author Information

Valentin De Bortoli (CNRS, ENS Ulm (projet NORIA))
Charlotte Bunne (ETH Zurich)
Guan-Horng Liu (Georgia Institute of Technology)
Tianrong Chen (Georgia Institute of Technology)
Maxim Raginsky
Pratik Chaudhari (UPenn, AWS)
Melanie Zeilinger (ETH Zurich)
Animashree Anandkumar (Caltech and NVIDIA)

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