1 citations · 2 across the 11 of their papers we have counts for
12 papers
Dynamic Programming for A Linear-Gaussian Dual Control Problem
Ying Wang, Yuchao Li, Håkan Hjalmarsson
We consider an infinite-horizon discounted dynamic programming (DP) problem for a countable-innovation belief-state model. The model is a surrogate for a linear-Gaussian dual contr…
Bayes Estimators with Performance Comparable to Empirical Bayes Estimators and Improved Local Robustness
Yue Ju, Ying Wang, Jiabao He +2
Bayes estimation has been extensively studied and widely used in statistics, decision theory, signal processing, machine learning, and system identification. Among its variants, em…
Fundamental Limitations of Data-Driven Control: A Statistical Decision Perspective
Jiabao He, Feiran Zhao, Yushan Li +3
Substantial research efforts have been devoted to the design of data-driven controllers; however, comparatively less is known about their statistical performance and fundamental li…
Range Space or Null Space: Least-Squares Methods for the Realization Problem
Jiabao He, Yueyue Xu, Yue Ju +2
This contribution revisits the classical approximate realization problem, which involves determining matrices of a state-space model based on estimates of a truncated series of Mar…
Excess Mean Squared Error of Empirical Bayes Estimators
Yue Ju, Bo Wahlberg, Håkan Hjalmarsson
Empirical Bayes estimators are based on minimizing the average risk with the hyper-parameters in the weighting function being estimated from observed data. The performance of an em…
Bayes and Biased Estimators Without Hyper-parameter Estimation: Comparable Performance to the Empirical-Bayes-Based Regularized Estimator
Yue Ju, Bo Wahlberg, Håkan Hjalmarsson
Regularized system identification has become a significant complement to more classical system identification. It has been numerically shown that kernel-based regularized estimator…
Finite Sample Analysis of Open-loop Subspace Identification Methods
Jiabao He, Ingvar Ziemann, Cristian R. Rojas +2
Subspace identification methods (SIMs) are known for their simple parameterization for MIMO systems and robust numerical properties. However, a comprehensive statistical analysis o…
A Weighted Least-Squares Method for Non-Asymptotic Identification of Markov Parameters from Multiple Trajectories
Jiabao He, Cristian R. Rojas, Håkan Hjalmarsson
Markov parameters play a key role in system identification. There exists many algorithms where these parameters are estimated using least-squares in a first, pre-processing, step,…
Weighted Least-Squares PARSIM
Jiabao He, Cristian R. Rojas, Håkan Hjalmarsson
Subspace identification methods (SIMs) have proven very powerful for estimating linear state-space models. To overcome the deficiencies of classical SIMs, a significant number of a…
Merging Parameter Estimation and Classification Using LASSO
Le Wang, Ying Wang, Yu Qiu +2
Soft sensing is a way to indirectly obtain information of signals for which direct sensing is difficult or prohibitively expensive. It may not \textit{a priori} be evident which se…
Finite Sample Analysis for a Class of Subspace Identification Methods
Jiabao He, Ingvar Ziemann, Cristian R. Rojas +1
While subspace identification methods (SIMs) are appealing due to their simple parameterization for MIMO systems and robust numerical realizations, a comprehensive statistical anal…
Regret Minimization in Scalar, Static, Non-linear Optimization Problems
Ying Wang, Mirko Pasquini, Kévin Colin +1
We study the problem of determining an effective exploration strategy in static and non-linear optimization problems, which depend on an unknown scalar parameter to be learned from…