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[1] Munther A. Dahleh,et al. Finite-Time System Identification for Partially Observed LTI Systems of Unknown Order , 2019, ArXiv.
[2] T. Lai,et al. Asymptotically efficient self-tuning regulators , 1987 .
[3] T. Lai,et al. Self-Normalized Processes: Limit Theory and Statistical Applications , 2001 .
[4] Magnus Jansson,et al. Subspace Identification and ARX Modeling , 2003 .
[5] Nikolai Matni,et al. Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator , 2018, NeurIPS.
[6] Dante C. Youla,et al. Modern Wiener-Hopf Design of Optimal Controllers. Part I , 1976 .
[7] Ambuj Tewari,et al. Input Perturbations for Adaptive Regulation and Learning , 2018, ArXiv.
[8] Petre Stoica,et al. Decentralized Control , 2018, The Control Systems Handbook.
[9] Bruce Lee,et al. Non-asymptotic Closed-Loop System Identification using Autoregressive Processes and Hankel Model Reduction , 2019, 2020 59th IEEE Conference on Decision and Control (CDC).
[10] Gábor Lugosi,et al. Prediction, learning, and games , 2006 .
[11] Sham M. Kakade,et al. The Nonstochastic Control Problem , 2020, ALT.
[12] Shie Mannor,et al. Online Learning for Adversaries with Memory: Price of Past Mistakes , 2015, NIPS.
[13] Holden Lee,et al. Robust guarantees for learning an autoregressive filter , 2019, ALT.
[14] Sham M. Kakade,et al. Online Control with Adversarial Disturbances , 2019, ICML.
[15] Si-Zhao Joe Qin,et al. An overview of subspace identification , 2006, Comput. Chem. Eng..
[16] Β. L. HO,et al. Editorial: Effective construction of linear state-variable models from input/output functions , 1966 .
[17] Babak Hassibi,et al. Regret Minimization in Partially Observable Linear Quadratic Control , 2020, ArXiv.
[18] Steven M. LaValle,et al. Planning algorithms , 2006 .
[19] Michel Verhaegen,et al. Identification of the deterministic part of MIMO state space models given in innovations form from input-output data , 1994, Autom..
[20] Bart De Moor,et al. N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems , 1994, Autom..
[21] Ambuj Tewari,et al. Optimism-Based Adaptive Regulation of Linear-Quadratic Systems , 2017, IEEE Transactions on Automatic Control.
[22] Lennart Ljung,et al. Closed-loop identification revisited , 1999, Autom..
[23] Yi Zhang,et al. Spectral Filtering for General Linear Dynamical Systems , 2018, NeurIPS.
[24] M. Phan,et al. Integrated system identification and state estimation for control offlexible space structures , 1992 .
[25] Kamyar Azizzadenesheli,et al. Adaptive Control and Regret Minimization in Linear Quadratic Gaussian (LQG) Setting , 2020, 2021 American Control Conference (ACC).
[26] B. Moor,et al. Closed loop subspace system identification , 1997 .
[27] J. W. Nieuwenhuis,et al. Boekbespreking van D.P. Bertsekas (ed.), Dynamic programming and optimal control - volume 2 , 1999 .
[28] Max Simchowitz,et al. Logarithmic Regret for Adversarial Online Control , 2020, ICML.
[29] Claude-Nicolas Fiechter,et al. PAC adaptive control of linear systems , 1997, COLT '97.
[30] Alon Cohen,et al. Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently , 2020, ICML.
[31] Max Simchowitz,et al. Improper Learning for Non-Stochastic Control , 2020, COLT.
[32] Csaba Szepesvári,et al. Online Least Squares Estimation with Self-Normalized Processes: An Application to Bandit Problems , 2011, ArXiv.
[33] Lennart Ljung,et al. Closed-Loop Subspace Identification with Innovation Estimation , 2003 .
[34] Robert F. Stengel,et al. Optimal Control and Estimation , 1994 .
[35] Csaba Szepesvári,et al. Regret Bounds for the Adaptive Control of Linear Quadratic Systems , 2011, COLT.
[36] Avinatan Hassidim,et al. Online Linear Quadratic Control , 2018, ICML.
[37] Joel A. Tropp,et al. User-Friendly Tail Bounds for Sums of Random Matrices , 2010, Found. Comput. Math..
[38] Karan Singh,et al. Logarithmic Regret for Online Control , 2019, NeurIPS.
[39] Y. Halevi. Stable LQG controllers , 1994, IEEE Trans. Autom. Control..
[40] Nevena Lazic,et al. Model-Free Linear Quadratic Control via Reduction to Expert Prediction , 2018, AISTATS.
[41] Benjamin Recht,et al. Certainty Equivalent Control of LQR is Efficient , 2019, ArXiv.
[42] Han-Fu Chen,et al. Optimal adaptive control and consistent parameter estimates for ARMAX model with quadratic cost , 1986, 1986 25th IEEE Conference on Decision and Control.
[43] Thomas B. Schön,et al. Robust exploration in linear quadratic reinforcement learning , 2019, NeurIPS.
[44] M. Phan,et al. Identification of observer/Kalman filter Markov parameters: Theory and experiments , 1993 .
[45] L. Meng,et al. The optimal perturbation bounds of the Moore–Penrose inverse under the Frobenius norm , 2010 .
[46] Yishay Mansour,et al. Learning Linear-Quadratic Regulators Efficiently with only $\sqrt{T}$ Regret , 2019, ArXiv.
[47] Max Simchowitz,et al. Learning Linear Dynamical Systems with Semi-Parametric Least Squares , 2019, COLT.
[48] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[49] Ambuj Tewari,et al. Input perturbations for adaptive control and learning , 2018, Autom..
[50] Yi Zhang,et al. No-Regret Prediction in Marginally Stable Systems , 2020, COLT.
[51] Samet Oymak,et al. Non-asymptotic Identification of LTI Systems from a Single Trajectory , 2018, 2019 American Control Conference (ACC).
[52] Dimitri P. Bertsekas,et al. Dynamic Programming and Optimal Control, Two Volume Set , 1995 .
[53] P. Wedin. Perturbation theory for pseudo-inverses , 1973 .
[54] George J. Pappas,et al. Finite Sample Analysis of Stochastic System Identification , 2019, 2019 IEEE 58th Conference on Decision and Control (CDC).
[55] T. Lai,et al. Least Squares Estimates in Stochastic Regression Models with Applications to Identification and Control of Dynamic Systems , 1982 .
[56] Alessandro Chiuso,et al. Consistency analysis of some closed-loop subspace identification methods , 2005, Autom..
[57] Mohamad Kazem Shirani Faradonbeh,et al. Regret Analysis for Adaptive Linear-Quadratic Policies , 2017 .
[58] George J. Pappas,et al. Online Learning of the Kalman Filter With Logarithmic Regret , 2020, IEEE Transactions on Automatic Control.
[59] Alessandro Lazaric,et al. Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems , 2018, ICML.
[60] Biao Huang,et al. System Identification , 2000, Control Theory for Physicists.
[61] Max Simchowitz,et al. Naive Exploration is Optimal for Online LQR , 2020, ICML.
[62] Alessandro Lazaric,et al. Thompson Sampling for Linear-Quadratic Control Problems , 2017, AISTATS.
[63] Karan Singh,et al. Learning Linear Dynamical Systems via Spectral Filtering , 2017, NIPS.
[64] Varun Kanade,et al. Tracking Adversarial Targets , 2014, ICML.
[65] Sanjeev Arora,et al. Towards Provable Control for Unknown Linear Dynamical Systems , 2018, International Conference on Learning Representations.
[66] Richard W. Longman,et al. System identification from closed-loop data with known output feedback dynamics , 1994 .
[67] George J. Pappas,et al. Sample Complexity of Kalman Filtering for Unknown Systems , 2019, L4DC.
[68] Han-Fu Chen,et al. Optimal adaptive control and consistent parameter estimates for ARMAX model withquadratic cost , 1987 .
[69] Yishay Mansour,et al. Learning Linear-Quadratic Regulators Efficiently with only $\sqrt{T}$ Regret , 2019, ICML.
[70] Csaba Szepesvári,et al. Improved Algorithms for Linear Stochastic Bandits , 2011, NIPS.
[71] Peter Auer,et al. Using Confidence Bounds for Exploitation-Exploration Trade-offs , 2003, J. Mach. Learn. Res..
[72] T. Başar,et al. A New Approach to Linear Filtering and Prediction Problems , 2001 .
[73] Lennart Ljung,et al. Subspace identification from closed loop data , 1996, Signal Process..
[74] Kamyar Azizzadenesheli,et al. Regret Bound of Adaptive Control in Linear Quadratic Gaussian (LQG) Systems , 2020, ArXiv.