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ORCIDIngvar M. Ziemann
Person information
- affiliation: KTH Royal Institute of Technology, Stockholm, Sweden
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Books and Theses
- 2022
[b1]Ingvar Max Ziemann:
Statistical Learning, Dynamics and Control: Fast Rates and Fundamental Limits for Square Loss. Royal Institute of Technology, Stockholm, Sweden, 2022
Journal Articles
- 2024
- [j3]Ingvar Max Ziemann
:
A Note on the Smallest Eigenvalue of the Empirical Covariance of Causal Gaussian Processes. IEEE Trans. Autom. Control. 69(2): 1372-1376 (2024) - 2021
- [j2]Ingvar M. Ziemann, Henrik Sandberg:
On a Phase Transition of Regret in Linear Quadratic Control: The Memoryless Case. IEEE Control. Syst. Lett. 5(2): 695-700 (2021) - 2020
- [j1]Ingvar M. Ziemann, Henrik Sandberg:
Regret Lower Bounds for Unbiased Adaptive Control of Linear Quadratic Regulators. IEEE Control. Syst. Lett. 4(3): 785-790 (2020)
Conference and Workshop Papers
- 2024
- [c13]Thomas T. C. K. Zhang, Bruce D. Lee, Ingvar M. Ziemann, George J. Pappas, Nikolai Matni:
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples. ICML 2024 - [c12]Ingvar M. Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni:
Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss. ICML 2024 - 2023
- [c11]Bruce D. Lee, Ingvar M. Ziemann, Anastasios Tsiamis, Henrik Sandberg, Nikolai Matni:
The Fundamental Limitations of Learning Linear-Quadratic Regulators. CDC 2023: 4053-4060 - [c10]Ingvar M. Ziemann, Anastasios Tsiamis, Bruce D. Lee, Yassir Jedra, Nikolai Matni, George J. Pappas:
A Tutorial on the Non-Asymptotic Theory of System Identification. CDC 2023: 8921-8939 - [c9]Ingvar M. Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni:
The noise level in linear regression with dependent data. NeurIPS 2023 - 2022
- [c8]Ingvar M. Ziemann, Anastasios Tsiamis, Henrik Sandberg, Nikolai Matni:
How are policy gradient methods affected by the limits of control? CDC 2022: 5992-5999 - [c7]Ingvar M. Ziemann, Henrik Sandberg, Nikolai Matni:
Single Trajectory Nonparametric Learning of Nonlinear Dynamics. COLT 2022: 3333-3364 - [c6]Anastasios Tsiamis, Ingvar M. Ziemann, Manfred Morari, Nikolai Matni, George J. Pappas:
Learning to Control Linear Systems can be Hard. COLT 2022: 3820-3857 - [c5]Ingvar M. Ziemann, Stephen Tu:
Learning with little mixing. NeurIPS 2022 - 2021
- [c4]Ingvar M. Ziemann, Henrik Sandberg:
Resource Constrained Sensor Attacks by Minimizing Fisher Information. ACC 2021: 4580-4585 - [c3]Ingvar M. Ziemann, Henrik Sandberg:
On Uninformative Optimal Policies in Adaptive LQR with Unknown B-Matrix. L4DC 2021: 213-226 - 2020
- [c2]Ingvar M. Ziemann, Henrik Sandberg:
Parameter Privacy versus Control Performance: Fisher Information Regularized Control. ACC 2020: 1259-1265 - 2019
- [c1]Ingvar M. Ziemann, Yishao Zhou:
Model Reduction of Semistable Distributed Parameter Systems. ECC 2019: 1944-1950
Informal and Other Publications
- 2024
- [i16]Ingvar M. Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni:
Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss. CoRR abs/2402.05928 (2024) - [i15]Charis J. Stamouli, Ingvar M. Ziemann, George J. Pappas:
Rate-Optimal Non-Asymptotics for the Quadratic Prediction Error Method. CoRR abs/2404.07937 (2024) - [i14]Bruce D. Lee, Ingvar M. Ziemann, George J. Pappas, Nikolai Matni:
Active Learning for Control-Oriented Identification of Nonlinear Systems. CoRR abs/2404.09030 (2024) - [i13]Jiabao He, Ingvar M. Ziemann, Cristian R. Rojas, Håkan Hjalmarsson:
Finite Sample Analysis for a Class of Subspace Identification Methods. CoRR abs/2404.17331 (2024) - [i12]Ingvar M. Ziemann, Nikolai Matni, George J. Pappas:
State space models, emergence, and ergodicity: How many parameters are needed for stable predictions? CoRR abs/2409.13421 (2024) - 2023
- [i11]Bruce D. Lee, Ingvar M. Ziemann, Anastasios Tsiamis, Henrik Sandberg, Nikolai Matni:
The Fundamental Limitations of Learning Linear-Quadratic Regulators. CoRR abs/2303.15637 (2023) - [i10]Ingvar M. Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni:
The noise level in linear regression with dependent data. CoRR abs/2305.11165 (2023) - [i9]Ingvar M. Ziemann, Anastasios Tsiamis, Bruce D. Lee, Yassir Jedra, Nikolai Matni, George J. Pappas:
A Tutorial on the Non-Asymptotic Theory of System Identification. CoRR abs/2309.03873 (2023) - 2022
- [i8]Ingvar M. Ziemann, Henrik Sandberg:
Regret Lower Bounds for Learning Linear Quadratic Gaussian Systems. CoRR abs/2201.01680 (2022) - [i7]Ingvar M. Ziemann, Henrik Sandberg, Nikolai Matni:
Single Trajectory Nonparametric Learning of Nonlinear Dynamics. CoRR abs/2202.08311 (2022) - [i6]Anastasios Tsiamis, Ingvar M. Ziemann, Manfred Morari, Nikolai Matni, George J. Pappas:
Learning to Control Linear Systems can be Hard. CoRR abs/2205.14035 (2022) - [i5]Ingvar M. Ziemann, Anastasios Tsiamis, Henrik Sandberg, Nikolai Matni:
How are policy gradient methods affected by the limits of control? CoRR abs/2206.06863 (2022) - [i4]Ingvar M. Ziemann, Stephen Tu:
Learning with little mixing. CoRR abs/2206.08269 (2022) - [i3]Anastasios Tsiamis, Ingvar M. Ziemann, Nikolai Matni, George J. Pappas:
Statistical Learning Theory for Control: A Finite Sample Perspective. CoRR abs/2209.05423 (2022) - [i2]Ingvar M. Ziemann:
A note on the smallest eigenvalue of the empirical covariance of causal Gaussian processes. CoRR abs/2212.09508 (2022) - 2020
- [i1]Ingvar M. Ziemann, Henrik Sandberg:
On Uninformative Optimal Policies in Adaptive LQR with Unknown B-Matrix. CoRR abs/2011.09288 (2020)
Coauthor Index
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