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We study the problem of system identification and adaptive control in partially observable linear dynamical systems.
Online control with adversarial disturbances
Naman Agarwal, Brian Bullins, Elad Hazan, Sham M Kakade, and Karan Singh · 1902
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A new approach to linear filtering and prediction problems
Rudolph Emil Kalman · 1960
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Effective construction of linear state-variable models from input/output functions
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Per-Åke Wedin · 1973
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Modern wiener-hopf design of optimal controllers–part ii: The multivariable case
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Integrated system identification and state estimation for control offlexible space structures
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Peter Van Overschee and Bart De Moor · 1994
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Michel Verhaegen · 1994
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Dynamic programming and optimal control , volume 2
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Thomas Kailath, Ali H Sayed, and Babak Hassibi · 2000
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Using confidence bounds for exploitation-exploration trade-offs
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S Joe Qin and Lennart Ljung · 2003
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The optimal perturbation bounds of the moore–penrose inverse under the frobenius norm
Lingsheng Meng and Bing Zheng · 2010
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Regret bounds for the adaptive control of linear quadratic systems
Yasin Abbasi-Yadkori and Csaba Szepesvári · 2011
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Improved algorithms for linear stochastic bandits
Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári · 2011
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Online linear quadratic control
Alon Cohen, Avinatan Hassidim, Tomer Koren, Nevena Lazic, Yishay Mansour, and Kunal Talwar · 2018
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Finite-time system identification for partially observed lti systems of unknown order
Tuhin Sarkar, Alexander Rakhlin, and Munther A Dahleh · 2019
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Finite sample analysis of stochastic system identification
Anastasios Tsiamis and George J Pappas · 2019
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Certainty equivalent control of lqr is efficient
Horia Mania, Stephen Tu, and Benjamin Recht · 2019
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Learning linear dynamical systems with semi-parametric least squares
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User-friendly tail bounds for sums of random matrices
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Online learning for adversaries with memory: price of past mistakes
Oren Anava, Elad Hazan, and Shie Mannor · 2015
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Learning linear dynamical systems via spectral filtering
Elad Hazan, Karan Singh, and Cyril Zhang · 2017
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Optimism-based adaptive regulation of linear-quadratic systems
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Thompson sampling for linear-quadratic control problems
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Max Simchowitz, Ross Boczar, and Benjamin Recht · 2019
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Bruce Lee and Andrew Lamperski · 2019
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Sample complexity of kalman filtering for unknown systems
Anastasios Tsiamis, Nikolai Matni, and George J Pappas · 2019
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Robust guarantees for learning an autoregressive filter
Holden Lee and Cyril Zhang · 2019
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Learning linear-quadratic regulators efficiently with only T \sqrt{T} regret
Alon Cohen, Tomer Koren, and Yishay Mansour · 2019
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Model-free linear quadratic control via reduction to expert prediction
Yasin Abbasi-Yadkori, Nevena Lazic, and Csaba Szepesvári · 2019
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The nonstochastic control problem
Elad Hazan, Sham M Kakade, and Karan Singh · 2019
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Improper learning for non-stochastic control
Max Simchowitz, Karan Singh, and Elad Hazan · 2020
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Regret minimization in partially observable linear quadratic control
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Online learning of the kalman filter with logarithmic regret
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No-regret prediction in marginally stable systems
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Logarithmic regret for learning linear quadratic regulators efficiently
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Naive exploration is optimal for online lqr
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Logarithmic regret for adversarial online control
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