| Statistical learning theory for control: A finite-sample perspective A Tsiamis, I Ziemann, N Matni, GJ Pappas IEEE Control Systems Magazine 43 (6), 67-97, 2023 | 84 | 2023 |
| Learning with little mixing I Ziemann, S Tu Advances in Neural Information Processing Systems 35, 4626-4637, 2022 | 36 | 2022 |
| Single trajectory nonparametric learning of nonlinear dynamics I Ziemann, H Sandberg, N Matni conference on Learning Theory, 3333-3364, 2022 | 31 | 2022 |
| A tutorial on the non-asymptotic theory of system identification I Ziemann, A Tsiamis, B Lee, Y Jedra, N Matni, GJ Pappas 2023 62nd IEEE Conference on Decision and Control (CDC), 8921-8939, 2023 | 24 | 2023 |
| Regret lower bounds for learning linear quadratic gaussian systems I Ziemann, H Sandberg IEEE Transactions on Automatic Control, 2024 | 23 | 2024 |
| Parameter privacy versus control performance: Fisher information regularized control I Ziemann, H Sandberg 2020 American Control Conference (ACC), 1259-1265, 2020 | 18 | 2020 |
| Learning to control linear systems can be hard A Tsiamis, I Ziemann, M Morari, N Matni, GJ Pappas Conference on Learning Theory, 3820-3857, 2022 | 16 | 2022 |
| How are policy gradient methods affected by the limits of control? I Ziemann, A Tsiamis, H Sandberg, N Matni 2022 IEEE 61st Conference on Decision and Control (CDC), 5992-5999, 2022 | 14 | 2022 |
| On uninformative optimal policies in adaptive lqr with unknown b-matrix I Ziemann, H Sandberg Learning for Dynamics and Control, 213-226, 2021 | 10 | 2021 |
| Sharp rates in dependent learning theory: Avoiding sample size deflation for the square loss I Ziemann, S Tu, GJ Pappas, N Matni Forty-first International Conference on Machine Learning, 2024 | 8 | 2024 |
| On a phase transition of regret in linear quadratic control: The memoryless case I Ziemann, H Sandberg IEEE Control Systems Letters 5 (2), 695-700, 2020 | 8 | 2020 |
| Regret Lower Bounds for Unbiased Adaptive Control of Linear Quadratic Regulators I Ziemann, H Sandberg | 8 | 2019 |
| Active Learning for Control-Oriented Identification of Nonlinear Systems BD Lee, I Ziemann, GJ Pappas, N Matni 2024 63rd IEEE Conference on Decision and Control (CDC), 2024 | 7 | 2024 |
| The fundamental limitations of learning linear-quadratic regulators BD Lee, I Ziemann, A Tsiamis, H Sandberg, N Matni 2023 62nd IEEE Conference on Decision and Control (CDC), 4053-4060, 2023 | 6 | 2023 |
| Noninvasively improving the orbit-response matrix while continuously correcting the orbit I Ziemann, V Ziemann Physical Review Accelerators and Beams 24 (7), 072804, 2021 | 6 | 2021 |
| The noise level in linear regression with dependent data I Ziemann, S Tu, GJ Pappas, N Matni Advances in Neural Information Processing Systems 36, 2023 | 5 | 2023 |
| A note on the smallest eigenvalue of the empirical covariance of causal gaussian processes I Ziemann IEEE Transactions on Automatic Control 69 (2), 1372-1376, 2023 | 5 | 2023 |
| Model reduction of semistable distributed parameter systems I Ziemann, Y Zhou 2019 18th European Control Conference (ECC), 1944-1950, 2019 | 4 | 2019 |
| Shallow diffusion networks provably learn hidden low-dimensional structure NM Boffi, A Jacot, S Tu, I Ziemann arXiv preprint arXiv:2410.11275, 2024 | 3 | 2024 |
| Finite Sample Analysis for a Class of Subspace Identification Methods J He, I Ziemann, CR Rojas, H Hjalmarsson 2024 63rd IEEE Conference on Decision and Control (CDC), 2024 | 2 | 2024 |
Nikolai MatniAssistant Professor of Electrical and Systems Engineering, University of PennsylvaniaВерификована је имејл адреса на seas.upenn.edu
