Call for Papers
The Learning for Dynamics and Control (L4DC) conference has recently emerged as the premier event for bringing together machine learning, dynamics, optimization and control with numerous applications. This fifth edition of the conference follows very successful conferences at MIT (2019), UC Berkeley (2020, Online), ETH (2021, Online) and Stanford (2021).
We invite submissions of short papers addressing topics including:
- Foundations of learning of dynamics models
- System identification
- Optimization for machine learning
- Data-driven optimization for dynamical systems
- Distributed learning over distributed systems
- Reinforcement learning for physical systems
- Safe reinforcement learning and safe adaptive control
- Statistical learning for dynamical and control systems
- Bridging model-based and learning-based dynamical and control systems
- Physics-constrained learning
- Physical learning in dynamical and control systems applications in robotics, autonomy, transportation systems, cognitive systems, neuroscience, etc.
We also strongly encourage submission of tools or datasets that can advance the field. The program consists of a few keynotes, contributed oral presentations, and poster presentations. All papers will be published on the Proceedings of Machine Learning Research (PMLR) series.
- Submissions are limited to 10 pages in PMLR format with unlimited allowance for references. Acknowledgements do not count towards the page limit. Please use this LaTeX style sheet. (Updated template without activated “draft” mode)
- If the submitted paper includes an appendix, it should also be within the 10-page limit.
- In their submission, authors may point to a technical report, e.g., on arXiv or on a personal webpage, for long proofs, extended results, etc.
- L4DC reviewing is single blind.
- We are currently accepting submissions via Openreview
- The submission deadline is November 30rd 2022, 11:59PM EST (Deadline has been extended).
- Please contact the conference organizers at firstname.lastname@example.org if you have any questions.