Model Reduction of Semistable Distributed Parameter Systems. - Ingvar M. Ziemann; Yishao Zhou (ECC, 2019)

Parameter Privacy versus Control Performance: Fisher Information Regularized Control. - Ingvar M. Ziemann; Henrik Sandberg (ACC, 2020)

Regret Lower Bounds for Unbiased Adaptive Control of Linear Quadratic Regulators. - Ingvar M. Ziemann; Henrik Sandberg (IEEE Control. Syst. Lett., 2020)

Resource Constrained Sensor Attacks by Minimizing Fisher Information. - Ingvar M. Ziemann; Henrik Sandberg (ACC, 2021)

On Uninformative Optimal Policies in Adaptive LQR with Unknown B-Matrix. - Ingvar M. Ziemann; Henrik Sandberg (L4DC, 2021)

On a Phase Transition of Regret in Linear Quadratic Control: The Memoryless Case. - Ingvar M. Ziemann; Henrik Sandberg (IEEE Control. Syst. Lett., 2021)

How are policy gradient methods affected by the limits of control? - Ingvar M. Ziemann; Anastasios Tsiamis; Henrik Sandberg; Nikolai Matni (CDC, 2022)

Learning to Control Linear Systems can be Hard. - Anastasios Tsiamis; Ingvar M. Ziemann; Manfred Morari; Nikolai Matni; George J. Pappas (COLT, 2022)

Single Trajectory Nonparametric Learning of Nonlinear Dynamics. - Ingvar M. Ziemann; Henrik Sandberg; Nikolai Matni (COLT, 2022)

Learning with little mixing. - Ingvar M. Ziemann; Stephen Tu (NeurIPS, 2022)

Statistical Learning Theory for Control: A Finite Sample Perspective. - Anastasios Tsiamis; Ingvar M. Ziemann; Nikolai Matni; George J. Pappas (CoRR, 2022)

Statistical Learning, Dynamics and Control: Fast Rates and Fundamental Limits for Square Loss. - Ingvar Max Ziemann (2022)

The Fundamental Limitations of Learning Linear-Quadratic Regulators. - Bruce D. Lee; Ingvar M. Ziemann; Anastasios Tsiamis; Henrik Sandberg; Nikolai Matni (CDC, 2023)

A Tutorial on the Non-Asymptotic Theory of System Identification. - Ingvar M. Ziemann; Anastasios Tsiamis; Bruce D. Lee; Yassir Jedra; Nikolai Matni; George J. Pappas (CDC, 2023)

The noise level in linear regression with dependent data. - Ingvar M. Ziemann; Stephen Tu; George J. Pappas; Nikolai Matni (NeurIPS, 2023)

Finite Sample Analysis for a Class of Subspace Identification Methods. - Jiabao He; Ingvar M. Ziemann; Cristian R. Rojas; Håkan Hjalmarsson (CDC, 2024)

Active Learning for Control-Oriented Identification of Nonlinear Systems. - Bruce D. Lee; Ingvar M. Ziemann; George J. Pappas; Nikolai Matni (CDC, 2024)

Rate-Optimal Non-Asymptotics for the Quadratic Prediction Error Method. - Charis J. Stamouli; Ingvar M. Ziemann; George J. Pappas (CDC, 2024)

Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples. - Thomas T. C. K. Zhang; Bruce D. Lee; Ingvar M. Ziemann; George J. Pappas; Nikolai Matni (ICML, 2024)

Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss. - Ingvar M. Ziemann; Stephen Tu; George J. Pappas; Nikolai Matni (ICML, 2024)

Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples. - Thomas T. Zhang; Bruce D. Lee; Ingvar M. Ziemann; George J. Pappas; Nikolai Matni (CoRR, 2024)

A Note on the Smallest Eigenvalue of the Empirical Covariance of Causal Gaussian Processes. - Ingvar Max Ziemann (IEEE Trans. Autom. Control., 2024)

Shallow diffusion networks provably learn hidden low-dimensional structure. - Nicholas Matthew Boffi; Arthur Jacot; Stephen Tu; Ingvar M. Ziemann (ICLR, 2025)

Nonconvex Linear System Identification with Minimal State Representation. - Uday Kiran Reddy Tadipatri; Benjamin D. Haeffele; Joshua Agterberg; Ingvar M. Ziemann; René Vidal (L4DC, 2025)

Logarithmic Regret for Nonlinear Control. - James Wang; Bruce D. Lee; Ingvar M. Ziemann; Nikolai Matni (L4DC, 2025)

A Short Information-Theoretic Analysis of Linear Auto-Regressive Learning. - Ingvar M. Ziemann (L4DC, 2025)

State space models, emergence, and ergodicity: How many parameters are needed for stable predictions? - Ingvar M. Ziemann; Nikolai Matni; George J. Pappas (L4DC, 2025)

A Vector Bernstein Inequality for Self-Normalized Martingales. - Ingvar M. Ziemann (Trans. Mach. Learn. Res., 2025)

Finite Sample Analysis of Subspace Identification Methods. - Jiabao He; Ingvar M. Ziemann; Cristian R. Rojas; S. Joe Qin; Håkan Hjalmarsson (CoRR, 2025)

Regret Lower Bounds for Learning Linear Quadratic Gaussian Systems. - Ingvar M. Ziemann; Henrik Sandberg (IEEE Trans. Autom. Control., 2025)