Learning Models and Constraints with Limited Data
Virtual Informal Systems Seminar (VISS)
Centre for Intelligent Machines (CIM) and Groupe d'Etudes et de Recherche en Analyse des Decisions (GERAD)
Meeting ID: 910 7928 6959 Ìý Ìý Ìý Ìý
Passcode: VISS Ìý
Speaker: Necmiye Ozay, Associate Professor, Electrical Engineering and Computer Science, University of Michigan
Abstract: System identification has a long history with several well-established methods, in particular for learning linear dynamical systems from input/output data. While the asymptotic properties of these methods are well understood as the number of data points goes to infinity or the noise level tends to zero, how well their estimates in finite data regime evolve is relatively less studied. This talk will mainly focus on our analysis of the robustness of the classical Ho-Kalman algorithm and how it translates to non-asymptotic estimation error bounds as a function of the number of data samples. In the second part of the talk, I will describe a practical problem where a robot needs to learn safe behaviors from a limited number of demonstrations. We recast this problem as an inverse constraint learning problem, similar to inverse optimal control. Our experiments with several robotics problems show (local) optimality can be a very strong prior in learning from demonstrations. I will conclude the talk with some open problems and directions for future research.
Biography: Necmiye Ozay received her B.S. degree from Bogazici University, Istanbul in 2004, her M.S. degree from the Pennsylvania State University, University Park in 2006 and her Ph.D. degree from Northeastern University, Boston in 2010, all in electrical engineering. She was a postdoctoral scholar at the California Institute of Technology, Pasadena between 2010 and 2013. She joined the University of Michigan, Ann Arbor in 2013, where she is currently an associate professor of Electrical Engineering and Computer Science. She is also a member of the Michigan Robotics Institute. Dr. Ozay's research interests include hybrid dynamical systems, control, optimization and formal methods with applications in cyber-physical systems, system identification, verification and validation, autonomy and dynamic data analysis. Her papers received several awards. She received the 1938E Award and a Henry Russel Award from the University of Michigan for her contributions to teaching and research, and five young investigator awards, including NSF CAREER. She is also a recipient of the 2021 Antonio Ruberti Young Researcher Prize from the IEEE Control Systems Society for her fundamental contributions to the control and identification of hybrid and cyber-physical systems.
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