6th Annual Learning for Dynamics & Control Conference
University of Oxford
Date & Time
Monday 15 Jul 2024 - Wednesday 17 Jul 2024
Over the next decade, the biggest generator of data is expected to be devices that sense and control the physical world.
The explosion of real-time data that is emerging from the physical world requires a rapprochement of areas such as machine learning, control theory, and optimization. While control theory has been firmly rooted in the tradition of model-based design, the availability and scale of data (both temporal and spatial) will require rethinking the foundations of our discipline. From a machine learning perspective, one of the main challenges going forward is to go beyond pattern recognition and address problems in data-driven control and optimization of dynamical processes. Our overall goal is to create a new community of people who think rigorously across the disciplines, ask new questions, and develop the foundations of this new scientific area. We are happy to welcome you to the University of Oxford for the 6th annual L4DC.
L4DC 2024 will begin accepting submissions via EasyChair on 1 October 2023.
- Paper submission deadline: 1 December 2023
- Author notification: April 2024
- Final paper upload deadline: May 2024
- Conference: 15-17 July 2024
Call for papers
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, biology, energy systems, transportation systems, cognitive systems, neuroscience, etc.
While the conference is open to any topic on the interface between machine learning, control, optimization and related areas, its primary goal is to address scientific and application challenges in real-time physical processes modeled by dynamical or control systems.
Kostas Margellos (Oxford)
Antonis Papachristodoulou (Oxford)
Alessandro Abate (Oxford)
Program Chair: Mark Cannon (Oxford)
Tutorials Chair: Simone Garatti (Polimi)
Publicity Chair: Ian Manchester (Sydney)
Awards Chair: Maryam Kamgarpour (EPFL)
Website Chair: Jack Umenberger (Oxford)
Ali Jadbabaei, MIT
John Lygeros, ETH Zurich
George Pappas, UPenn
Pablo Parrilo, MIT
Ben Recht, UC Berkeley
Claire Tomlin, UC Berkeley
Melanie Zeilinger, ETH Zurich