We invite submissions of 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
Machine learning for reduced-order modeling and physics-constrained systems
Physical learning in dynamical and control systems applications in robotics, autonomy, biology, energy systems, transportation systems, cognitive systems, neuroscience, etc.
The conference is open to any topic on the interface between machine learning, control, and optimization; its primary goal is to address scientific and application challenges in real-time processes modeled by dynamical or control systems.
Papers are to be submitted on OpenReview using the provided L4DC LaTeX template.
All papers must be written in English and be uploaded in PDF format.
Submissions are limited to 10 pages, including all figures, tables, conclusions, etc, with unlimited allowance for references. Acknowledgments do not count toward the page limit.
Authors may include supplementary material (long proofs, additional experimental results, etc), but reviews and acceptance decisions will be based only on the submitted paper (10 pages). The supplementary material must be submitted along with the main paper in the submission portal.
L4DC reviewing is single-blind.
All accepted papers will be presented as posters at the conference. A selected set of papers deemed particularly exceptional by the program committee will be presented as oral talks.
Please contact the conference organizers if you have any questions.
* Update: Deadline extended by 2 days. We will accommodate timely notification of the decision before the end of January.