Search Results for author: Jianjun Yuan

Found 5 papers, 1 papers with code

Regret Guarantees for Online Receding Horizon Learning Control

no code implementations14 Oct 2020 Deepan Muthirayan, Jianjun Yuan, Pramod P. Khargonekar

In this paper we provide provable regret guarantees for an online learning receding horizon type control policy in a setting where the system to be controlled is an unknown linear dynamical system, the cost for the controller is a general additive function over a finite period $T$, and there exist control input constraints that when violated incur an additional cost.

Optimization and Control Systems and Control Systems and Control

Online Convex Optimization in Changing Environments and its Application to Resource Allocation

no code implementations30 Sep 2020 Jianjun Yuan

What's more, sequential data is usually changing dynamically, and needs to be understood on-the-fly in order to capture the changes.

Trading-Off Static and Dynamic Regret in Online Least-Squares and Beyond

no code implementations6 Sep 2019 Jianjun Yuan, Andrew Lamperski

In order to obtain more computationally efficient algorithms, our second contribution is a novel gradient descent step size rule for strongly convex functions.

Online Adaptive Principal Component Analysis and Its extensions

1 code implementation23 Jan 2019 Jianjun Yuan, Andrew Lamperski

We propose algorithms for online principal component analysis (PCA) and variance minimization for adaptive settings.

Online convex optimization for cumulative constraints

no code implementations NeurIPS 2018 Jianjun Yuan, Andrew Lamperski

For convex objectives, our regret bounds generalize existing bounds, and for strongly convex objectives we give improved regret bounds.

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