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Found 5 papers, 0 papers with code
Regret Analysis of Learning-Based Linear Quadratic Gaussian Control with Additive Exploration
In this paper, we analyze the regret incurred by a computationally efficient exploration strategy, known as naive exploration, for controlling unknown partially observable systems within the Linear Quadratic Gaussian (LQG) framework.
Finite-sample analysis of identification of switched linear systems with arbitrary or restricted switching
To capture the effect of the parameters of the switching strategies on the LS estimation error, finite-sample error bounds are developed in this work.
Efficient learning of hidden state LTI state space models of unknown order
Finally, we propose an estimation algorithm for the minimal realization that uses both the Hankel penalized least square estimator and the Ho-Kalman based estimation procedure and guarantees with high probability that we recover the correct order of the system and satisfies a new fast rate in the $S_2$-norm with a polynomial reduction in the dependence on the dimension and other parameters of the problem.
Non asymptotic estimation lower bounds for LTI state space models with Cramér-Rao and van Trees
Our results extend and improve existing lower bounds to lower bounds in expectation of the mean square estimation risk and to systems with a general noise covariance.
Bayesian Model Selection for Change Point Detection and Clustering
We address the new problem of estimating a piece-wise constant signal with the purpose of detecting its change points and the levels of clusters.
