Scholar
Ingvar Ziemann
Google Scholar ID: _RBAS2IAAAAJ
Unknown affiliation
Citations & Impact
Citations
433
H-index
10
i10-index
11
Publications
20
Co-authors
10
list available
Publications
Resume (English only)
Academic Achievements
- Selected Papers:
- - ICML24: Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss
- - NeurIPS’23: The Noise Level in Dependent Linear Regression
- - NeurIPS’22: Learning with little mixing
- - IEEE CDC’22: How are policy gradient methods affected by the limits of control? (Best Student Paper Award)
- - To appear in IEEE Transactions on Automatic Control: Regret Lower Bounds for Learning Linear Quadratic Gaussian Systems
- Other Academic Contributions:
- - Lecture Notes for ESE6180: Learning, Dynamics and Control
- - IEEE CDC’23: A tutorial on the non-asymptotic theory of system identification
- - IEEE Control Systems Magazine 2023: Statistical Learning Theory for Control
Research Experience
- Postdoctoral Researcher: University of Pennsylvania, collaborating with Nikolai Matni and George J. Pappas.
Education
- PhD: Supervised by Henrik Sandberg at KTH Royal Institute of Technology.
Background
- Research Interests: Machine Learning, Controls. Currently a postdoc at the University of Pennsylvania, hosted by Nikolai Matni and George J. Pappas, and funded by a Swedish Research Council Grant.
Miscellany
- Personal Website: https://skylerhallinan.com/
- Email: ingvarz[at]seas[dot]upenn[dot]edu
