Tianshi Chen

Affiliations:
  • Chinese University of Hong Kong, School of Data Science, Hong Kong
  • Chinese University of Hong Kong,School of Science and Engineering, Hong Kong (2015 - 2020)
  • Linköping University, Department of Electrical Engineering, Sweden (2009 - 2015)
  • Chinese University of Hong Kong, Hong Kong (PhD 2008)
  • Harbin Institute of Technology, China (former)


According to our database1, Tianshi Chen authored at least 81 papers between 2004 and 2024.

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Bibliography

2024
When cannot regularization improve the least squares estimate in the kernel-based regularized system identification.
Autom., February, 2024

On kernel design for regularized non-causal system identification.
Autom., January, 2024

2023
Asymptotic Theory for Regularized System Identification Part I: Empirical Bayes Hyperparameter Estimator.
IEEE Trans. Autom. Control., December, 2023

Data-Driven Distributed Adaptive Consensus Tracking of Nonlinear Multiagent Systems: A Controller-Based Dynamic Linearization Method.
IEEE Trans. Syst. Man Cybern. Syst., November, 2023

Input Design for Regularized System Identification: Stationary Conditions and Sphere Preserving Algorithm.
IEEE Trans. Autom. Control., September, 2023

Kernel-based regularized iterative learning control of repetitive linear time-varying systems.
Autom., August, 2023

Identifiability Analysis of Noise Covariances for LTI Stochastic Systems With Unknown Inputs.
IEEE Trans. Autom. Control., July, 2023

An efficient implementation for spatial-temporal Gaussian process regression and its applications.
Autom., 2023

On embeddings and inverse embeddings of input design for regularized system identification.
Autom., 2023

An Efficient Implementation for Bayesian Manifold Regularization Method.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

A Family of Hyper-parameter Estimators for Regularized Linear System Identification.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

An Efficient Implementation for Kernel-Based Regularized System Identification with Periodic Input Signals.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

Towards Scalable Kernel-Based Regularized System Identification.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

2022
The noise covariances of linear Gaussian systems with unknown inputs are not uniquely identifiable using autocovariance least-squares.
Syst. Control. Lett., 2022

An Efficient Implementation for Spatial-Temporal Gaussian Process Regression and Its Applications.
CoRR, 2022

Asymptotic Theory for Regularized System Identification Part I: Empirical Bayes Hyper-parameter Estimator.
CoRR, 2022

Persistence of excitation for identifying switched linear systems.
Autom., 2022

2021
Tutorial on Asymptotic Properties of Regularized Least Squares Estimator for Finite Impulse Response Model.
CoRR, 2021

Identification of Switched Linear Systems: Persistence of Excitation and Numerical Algorithms.
CoRR, 2021

Kalman filtering under unknown inputs and norm constraints.
Autom., 2021

On semiseparable kernels and efficient implementation for regularized system identification and function estimation.
Autom., 2021

Bus arrival time prediction and reliability analysis: An experimental comparison of functional data analysis and Bayesian support vector regression.
Appl. Soft Comput., 2021

On Asymptotic Distribution of Generalized Cross Validation Hyper-parameter Estimator for Regularized System Identification<sup>*</sup>.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

2020
Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series.
IEEE Trans. Signal Process., 2020

Smoothing Splines and Rank Structured Matrices: Revisiting the Spline Kernel.
SIAM J. Matrix Anal. Appl., 2020

A shift in paradigm for system identification.
Int. J. Control, 2020

Accelerated Sparse Bayesian Learning via Screening Test and Its Applications.
CoRR, 2020

On Effects of Condition Number of Regression Matrix upon Hyper-parameter Estimators for Kernel-based Regularization Methods.
CoRR, 2020

Regularized LTI system identification in the presence of outliers: A variational EM approach.
Autom., 2020

On the Influence of Ill-conditioned Regression Matrix on Hyper-parameter Estimators for Kernel-based Regularization Methods.
Proceedings of the 59th IEEE Conference on Decision and Control, 2020

2019
Cramér-Rao Bounds for Filtering Based on Gaussian Process State-Space Models.
IEEE Trans. Signal Process., 2019

Distributed Gaussian Processes Hyperparameter Optimization for Big Data Using Proximal ADMM.
IEEE Signal Process. Lett., 2019

Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series.
CoRR, 2019

Parameter estimation of discrete-time sinusoidal signals: A nonlinear control approach.
Autom., 2019

Recursive Implementation of Gaussian Process Regression for Spatial-Temporal Data Modeling.
Proceedings of the 11th International Conference on Wireless Communications and Signal Processing, 2019

2018
Continuous-Time DC Kernel - A Stable Generalized First-Order Spline Kernel.
IEEE Trans. Autom. Control., 2018

On asymptotic properties of hyperparameter estimators for kernel-based regularization methods.
Autom., 2018

On input design for regularized LTI system identification: Power-constrained input.
Autom., 2018

On the stability of reproducing kernel Hilbert spaces of discrete-time impulse responses.
Autom., 2018

On kernel design for regularized LTI system identification.
Autom., 2018

Sparse Structure Enabled Grid Spectral Mixture Kernel for Temporal Gaussian Process Regression.
Proceedings of the 21st International Conference on Information Fusion, 2018

Asymptotic Properties of Hyperparameter Estimators by Using Cross-Validations for Regularized System Identification.
Proceedings of the 57th IEEE Conference on Decision and Control, 2018

2017
Maximum Entropy Kernels for System Identification.
IEEE Trans. Autom. Control., 2017

On the input design for kernel-based regularized LTI system identification: Power-constrained inputs.
Proceedings of the 56th IEEE Annual Conference on Decision and Control, 2017

2016
Background of shape contexts for point matching.
Pattern Recognit. Lett., 2016

On kernel design for regularized LTI system identification.
CoRR, 2016

Regularized linear system identification using atomic, nuclear and kernel-based norms: The role of the stability constraint.
Autom., 2016

Transfer function and transient estimation by Gaussian process regression in the frequency domain.
Autom., 2016

Maximum entropy properties of discrete-time first-order stable spline kernel.
Autom., 2016

Continuous-time DC kernel - A stable generalized first order spline kernel.
Proceedings of the 55th IEEE Conference on Decision and Control, 2016

2015
Maximum entropy property of discrete-time stable spline kernel.
Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

Regularized system identification using orthonormal basis functions.
Proceedings of the 14th European Control Conference, 2015

Spectral analysis of the DC kernel for regularized system identification.
Proceedings of the 54th IEEE Conference on Decision and Control, 2015

2014
System Identification Via Sparse Multiple Kernel-Based Regularization Using Sequential Convex Optimization Techniques.
IEEE Trans. Autom. Control., 2014

Maximum Entropy Property of Discrete-time First Order Stable Spline Kernel.
CoRR, 2014

Kernel methods in system identification, machine learning and function estimation: A survey.
Autom., 2014

Scalable anomaly detection in large homogeneous populations.
Autom., 2014

On the design of multiple kernels for nonparametric linear system identification.
Proceedings of the 53rd IEEE Conference on Decision and Control, 2014

Anomaly detection in homogenous populations: A sparse multiple kernel-based regularization method.
Proceedings of the 53rd IEEE Conference on Decision and Control, 2014

2013
Scalable Anomaly Detection in Large Homogenous Populations.
CoRR, 2013

Implementation of algorithms for tuning parameters in regularized least squares problems in system identification.
Autom., 2013

Convexity issues in system identification.
Proceedings of the 10th IEEE International Conference on Control and Automation, 2013

Kernel-based model order selection for identification and prediction of linear dynamic systems.
Proceedings of the 52nd IEEE Conference on Decision and Control, 2013

Regularization strategies for nonparametric system identification.
Proceedings of the 52nd IEEE Conference on Decision and Control, 2013

Rank-1 kernels for regularized system identification.
Proceedings of the 52nd IEEE Conference on Decision and Control, 2013

Kernel-Based Model Order Selection for Linear System Identification.
Proceedings of the 11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, 2013

What Can Regularization Offer for Estimation of Dynamical Systems?
Proceedings of the 11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, 2013

2012
On the estimation of transfer functions, regularizations and Gaussian processes - Revisited.
Autom., 2012

Sparse multiple kernels for impulse response estimation with majorization minimization algorithms.
Proceedings of the 51th IEEE Conference on Decision and Control, 2012

On the estimation of hyperparameters for Bayesian system identification with exponentially decaying kernels.
Proceedings of the 51th IEEE Conference on Decision and Control, 2012

2011
Decentralized Particle Filter With Arbitrary State Decomposition.
IEEE Trans. Signal Process., 2011

Kernel selection in linear system identification part II: A classical perspective.
Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, 2011

2010
A small gain approach to global stabilization of nonlinear feedforward systems with input unmodeled dynamics.
Autom., 2010

Comments on "State estimation for linear systems with state equality constraints" [Automatica 43 (2007) 1363-1368].
Autom., 2010

Decentralization of particle filters using arbitrary state decomposition.
Proceedings of the 49th IEEE Conference on Decision and Control, 2010

2009
Global Robust Output Regulation by State Feedback for Strict Feedforward Systems.
IEEE Trans. Autom. Control., 2009

2008
Disturbance Attenuation of Feedforward Systems With Dynamic Uncertainty.
IEEE Trans. Autom. Control., 2008

Global robust stabilization of nonlinear strict feedforward systems with input unmodeled dynamics.
Proceedings of the American Control Conference, 2008

2007
Global robust stabilization of a class of uncertain feedforward systems.
Proceedings of the 46th IEEE Conference on Decision and Control, 2007

2005
Inverse optimal constrained input-to-state stabilization of nonlinear systems.
Proceedings of the American Control Conference, 2005

2004
An approach to integral input-to-state stabilization via satisficing strategy.
Proceedings of the 8th International Conference on Control, 2004


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