I am a postdoc at the University of Pennsylvania, hosted by Nikolai Matni and George J. Pappas and generously funded by a Swedish Research Council Grant. Previously, I obtained a PhD under the supervision of Henrik Sandberg at KTH. You can find an interview with me about my PhD Work here.
Research Interests
- Machine Learning, Controls
Selected Papers:
For a full list, please refer to my Google Scholar
Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss, Ingvar Ziemann, Stephen Tu, George J. Pappas and Nikolai Matni, ICML24. Spotlight. Slides, Poster
The Noise Level in Dependent Linear Regression, Ingvar Ziemann, Stephen Tu, George J. Pappas and Nikolai Matni, NeurIPS’23.
Learning with little mixing, Ingvar Ziemann and Stephen Tu, NeurIPS’22. Slides
How are policy gradient methods affected by the limits of control?, Ingvar Ziemann, Anastasios Tsiamis, Henrik Sandberg and Nikolai Matni, IEEE CDC’22. Best Student Paper Award. Slides
Regret Lower Bounds for Learning Linear Quadratic Gaussian Systems, Ingvar Ziemann and Henrik Sandberg, to appear, IEEE Transactions on Automatic Control. Slides
Expository Writing
Lecture Notes for ESE6180: Learning, Dynamics and Control, Ingvar Ziemann, ⚠️ WIP FALL2024: if you find errors please do not hesitate to send me an email
A tutorial on the non-asymptotic theory of system identification, Ingvar Ziemann, Bruce D. Lee, Anastasios Tsiamis, Jassir Yedra, Nikolai Matni and George J. Pappas, IEEE CDC’23
- I organized a connecting tutorial session in Singapore at CDC’23. You may find the slides here.
Statistical Learning Theory for Control, Anastasios Tsiamis, Ingvar Ziemann, Nikolai Matni and George J. Pappas, IEEE Control Systems Magazine 2023.
