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