Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
On the certainty-equivalence approach to direct data-driven LQR design
The linear quadratic regulator (LQR) problem is a cornerstone of automatic control, and it
has been widely studied in the data-driven setting. The various data-driven approaches can …
has been widely studied in the data-driven setting. The various data-driven approaches can …
Logarithmic regret bound in partially observable linear dynamical systems
We study the problem of system identification and adaptive control in partially observable
linear dynamical systems. Adaptive and closed-loop system identification is a challenging …
linear dynamical systems. Adaptive and closed-loop system identification is a challenging …
Active learning for nonlinear system identification with guarantees
While the identification of nonlinear dynamical systems is a fundamental building block of
model-based reinforcement learning and feedback control, its sample complexity is only …
model-based reinforcement learning and feedback control, its sample complexity is only …
Non-asymptotic and accurate learning of nonlinear dynamical systems
We consider the problem of learning a nonlinear dynamical system governed by a nonlinear
state equation ht+ 1= ϕ (ht, ut; θ)+ wt. Here θ is the unknown system dynamics, ht is the …
state equation ht+ 1= ϕ (ht, ut; θ)+ wt. Here θ is the unknown system dynamics, ht is the …
Sample complexity of linear quadratic gaussian (LQG) control for output feedback systems
This paper studies a class of partially observed Linear Quadratic Gaussian (LQG) problems
with unknown dynamics. We establish an end-to-end sample complexity bound on learning …
with unknown dynamics. We establish an end-to-end sample complexity bound on learning …
Active learning for nonlinear system identification with guarantees
While the identification of nonlinear dynamical systems is a fundamental building block of
model-based reinforcement learning and feedback control, its sample complexity is only …
model-based reinforcement learning and feedback control, its sample complexity is only …
Learning mixtures of linear dynamical systems
Y Chen, HV Poor - International conference on machine …, 2022 - proceedings.mlr.press
We study the problem of learning a mixture of multiple linear dynamical systems (LDSs) from
unlabeled short sample trajectories, each generated by one of the LDS models. Despite the …
unlabeled short sample trajectories, each generated by one of the LDS models. Despite the …
Online learning of the kalman filter with logarithmic regret
In this article, we consider the problem of predicting observations generated online by an
unknown, partially observable linear system, which is driven by Gaussian noise. In the linear …
unknown, partially observable linear system, which is driven by Gaussian noise. In the linear …
Smoothed online learning for prediction in piecewise affine systems
The problem of piecewise affine (PWA) regression and planning is of foundational
importance to the study of online learning, control, and robotics, where it provides a …
importance to the study of online learning, control, and robotics, where it provides a …
Adaptive control and regret minimization in linear quadratic Gaussian (LQG) setting
We study the problem of adaptive control in partially observable linear quadratic Gaussian
control systems, where the model dynamics are unknown a priori. We propose LQGOPT, a …
control systems, where the model dynamics are unknown a priori. We propose LQGOPT, a …