New Frontiers in
Learning, Control, and Dynamical Systems

Workshop at the International Conference on Machine Learning (ICML) 2023

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.

We invite researcher in machine learning, control, and dynamical systems to submit their latest works to our workshop.
Topics include but are not limited to (see Call for Papers for more details):

  • Optimal Transport
  • Stochastic Processes
  • Stochastic Optimal Control
  • Dynamical Probabilistic Inference, e.g., MCMC, Variational Inference
  • Diffusion Models
  • Neural ODEs, SDEs, or PDEs
  • Reinforcement Learning
01 Apr, 2023 We are accepting submissions! See Call for Paper for further information.
16 Mar, 2023 Our workshop proposal was accepted to ICML 2023. Call for submissions and contributions will follow soon. Stay tuned!

Confirmed Speakers

Brandon Amos

Meta AI

Claire Tomlin

UC Berkeley

Marco Cuturi


Rianne van den Berg

Microsoft Research

Jiequn Han

Flatiron Institute

Giorgia Ramponi

ETH Zurich


Valentin De Bortoli

ENS Paris

Charlotte Bunne

ETH Zurich

Guan-Horng Liu

Georgia Tech

Tianrong Chen

Georgia Tech

Anima Anandkumar

Caltech & NVIDIA

Maxim Raginsky


Melanie Zeilinger

ETH Zurich

Pratik Chaudhari



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