Ingvar M. Ziemann

According to our database, Ingvar M. Ziemann authored at least 30 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Regret Lower Bounds for Learning Linear Quadratic Gaussian Systems.

Ingvar M. Ziemann, Henrik Sandberg

IEEE Trans. Autom. Control., January, 2025

Finite Sample Analysis of Subspace Identification Methods.

Jiabao He, Ingvar M. Ziemann, Cristian R. Rojas, S. Joe Qin, Håkan Hjalmarsson

CoRR, January, 2025

A Vector Bernstein Inequality for Self-Normalized Martingales.

Ingvar M. Ziemann

Trans. Mach. Learn. Res., 2025

State space models, emergence, and ergodicity: How many parameters are needed for stable predictions?

Ingvar M. Ziemann, Nikolai Matni, George J. Pappas

Proceedings of the 7th Annual Learning for Dynamics & Control Conference, 2025

A Short Information-Theoretic Analysis of Linear Auto-Regressive Learning.

Ingvar M. Ziemann

Proceedings of the 7th Annual Learning for Dynamics & Control Conference, 2025

Logarithmic Regret for Nonlinear Control.

James Wang, Bruce D. Lee, Ingvar M. Ziemann, Nikolai Matni

Proceedings of the 7th Annual Learning for Dynamics & Control Conference, 2025

Nonconvex Linear System Identification with Minimal State Representation.

Uday Kiran Reddy Tadipatri, Benjamin D. Haeffele, Joshua Agterberg, Ingvar M. Ziemann, René Vidal

Proceedings of the 7th Annual Learning for Dynamics & Control Conference, 2025

Shallow diffusion networks provably learn hidden low-dimensional structure.

Nicholas Matthew Boffi, Arthur Jacot, Stephen Tu, Ingvar M. Ziemann

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

A Note on the Smallest Eigenvalue of the Empirical Covariance of Causal Gaussian Processes.

Ingvar Max Ziemann

IEEE Trans. Autom. Control., February, 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

Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss.

Ingvar M. Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni

Proceedings of the Forty-first International Conference on Machine Learning, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Rate-Optimal Non-Asymptotics for the Quadratic Prediction Error Method.

Charis J. Stamouli, Ingvar M. Ziemann, George J. Pappas

Proceedings of the 63rd IEEE Conference on Decision and Control, 2024

Active Learning for Control-Oriented Identification of Nonlinear Systems.

Bruce D. Lee, Ingvar M. Ziemann, George J. Pappas, Nikolai Matni

Proceedings of the 63rd IEEE Conference on Decision and Control, 2024

Finite Sample Analysis for a Class of Subspace Identification Methods.

Jiabao He, Ingvar M. Ziemann, Cristian R. Rojas, Håkan Hjalmarsson

Proceedings of the 63rd IEEE Conference on Decision and Control, 2024

2023

The noise level in linear regression with dependent data.

Ingvar M. Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

The Fundamental Limitations of Learning Linear-Quadratic Regulators.

Bruce D. Lee, Ingvar M. Ziemann, Anastasios Tsiamis, Henrik Sandberg, Nikolai Matni

Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

2022

Statistical Learning, Dynamics and Control: Fast Rates and Fundamental Limits for Square Loss.

Ingvar Max Ziemann

PhD thesis, 2022

Statistical Learning Theory for Control: A Finite Sample Perspective.

Anastasios Tsiamis, Ingvar M. Ziemann, Nikolai Matni, George J. Pappas

CoRR, 2022

Learning with little mixing.

Ingvar M. Ziemann, Stephen Tu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Single Trajectory Nonparametric Learning of Nonlinear Dynamics.

Ingvar M. Ziemann, Henrik Sandberg, Nikolai Matni

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Learning to Control Linear Systems can be Hard.

Anastasios Tsiamis, Ingvar M. Ziemann, Manfred Morari, Nikolai Matni, George J. Pappas

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

How are policy gradient methods affected by the limits of control?

Ingvar M. Ziemann, Anastasios Tsiamis, Henrik Sandberg, Nikolai Matni

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

2021

On a Phase Transition of Regret in Linear Quadratic Control: The Memoryless Case.

Ingvar M. Ziemann, Henrik Sandberg

IEEE Control. Syst. Lett., 2021

On Uninformative Optimal Policies in Adaptive LQR with Unknown B-Matrix.

Ingvar M. Ziemann, Henrik Sandberg

Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

Resource Constrained Sensor Attacks by Minimizing Fisher Information.

Ingvar M. Ziemann, Henrik Sandberg

Proceedings of the 2021 American Control Conference, 2021

2020

Regret Lower Bounds for Unbiased Adaptive Control of Linear Quadratic Regulators.

Ingvar M. Ziemann, Henrik Sandberg

IEEE Control. Syst. Lett., 2020

Parameter Privacy versus Control Performance: Fisher Information Regularized Control.

Ingvar M. Ziemann, Henrik Sandberg

Proceedings of the 2020 American Control Conference, 2020

2019

Model Reduction of Semistable Distributed Parameter Systems.

Ingvar M. Ziemann, Yishao Zhou

Proceedings of the 17th European Control Conference, 2019