no code implementations • 27 Jul 2023 • Jianjun Yuan, Wei Lee Woon, Ludovik Coba
This paper presents an efficient algorithm to solve the sleeping bandit with multiple plays problem in the context of an online recommendation system.
no code implementations • 30 Oct 2022 • Deepan Muthirayan, Jianjun Yuan, Pramod P. Khargonekar
While many algorithmic advances have been made towards online optimization with long term constraints, these algorithms typically assume that the sequence of cost functions over a certain $T$ finite steps that determine the cost to the online learner are adversarially generated.
no code implementations • 30 Nov 2021 • Deepan Muthirayan, Jianjun Yuan, Dileep Kalathil, Pramod P. Khargonekar
Specifically, we study the online learning problem where the control algorithm does not know the true system model and has only access to a fixed-length (that does not grow with the control horizon) preview of the future cost functions.
no code implementations • 14 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
no code implementations • 30 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.
no code implementations • 6 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.
1 code implementation • 23 Jan 2019 • Jianjun Yuan, Andrew Lamperski
We propose algorithms for online principal component analysis (PCA) and variance minimization for adaptive settings.
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.