3 citations · 9 across the 16 of their papers we have counts for
18 papers
Achievability of CSS codes performance limits over Pauli channels under separate ML decoding with bounded graphical complexity
Gulnihal Mercanoglu, Achilleas Anastasopoulos, S. Sandeep Pradhan
Capacity-approaching quantum error correction faces a fundamental tension: the required parity-check matrices need to become increasingly dense, while practical decoding and syndro…
On the Effect of Instability on Learning Continuous-Time Linear Control Systems
Reza Sadeghi Hafshejani, Mohamad Kazem Shirani Fradonbeh
We study the problem of system identification for stochastic continuous-time dynamics, based on a single finite-length state trajectory. We present a method for estimating the poss…
Thompson Sampling in Partially Observable Contextual Bandits
Hongju Park, Mohamad Kazem Shirani Faradonbeh
Contextual bandits constitute a classical framework for decision-making under uncertainty. In this setting, the goal is to learn the arms of highest reward subject to contextual in…
Analysis of Thompson Sampling for Controlling Unknown Linear Diffusion Processes
Mohamad Kazem Shirani Faradonbeh, Sadegh Shirani, Mohsen Bayati
Linear diffusion processes serve as canonical continuous-time models for dynamic decision-making under uncertainty. These systems evolve according to drift matrices that specify th…
Regret Analysis of Certainty Equivalence Policies in Continuous-Time Linear-Quadratic Systems
Mohamad Kazem Shirani Faradonbeh
This work theoretically studies a ubiquitous reinforcement learning policy for controlling the canonical model of continuous-time stochastic linear-quadratic systems. We show that…
Worst-case Performance of Greedy Policies in Bandits with Imperfect Context Observations
Hongju Park, Mohamad Kazem Shirani Faradonbeh
Contextual bandits are canonical models for sequential decision-making under uncertainty in environments with time-varying components. In this setting, the expected reward of each…
Efficient Algorithms for Learning to Control Bandits with Unobserved Contexts
Hongju Park, Mohamad Kazem Shirani Faradonbeh
Contextual bandits are widely-used in the study of learning-based control policies for finite action spaces. While the problem is well-studied for bandits with perfectly observed c…
Joint Learning-Based Stabilization of Multiple Unknown Linear Systems
Mohamad Kazem Shirani Faradonbeh, Aditya Modi
Learning-based control of linear systems received a lot of attentions recently. In popular settings, the true dynamical models are unknown to the decision-maker and need to be inte…
Bayesian Algorithms Learn to Stabilize Unknown Continuous-Time Systems
Mohamad Kazem Shirani Faradonbeh, Mohamad Sadegh Shirani Faradonbeh
Linear dynamical systems are canonical models for learning-based control of plants with uncertain dynamics. The setting consists of a stochastic differential equation that captures…
Joint Learning of Linear Time-Invariant Dynamical Systems
Aditya Modi, Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari +1
Linear time-invariant systems are very popular models in system theory and applications. A fundamental problem in system identification that remains rather unaddressed in extant li…
Reinforcement Learning Policies in Continuous-Time Linear Systems
Mohamad Kazem Shirani Faradonbeh, Mohamad Sadegh Shirani Faradonbeh
Linear dynamical systems that obey stochastic differential equations are canonical models. While optimal control of known systems has a rich literature, the problem is technically…
Departure Time Choice Models in Urban Transportation Systems Based on Mean Field Games
Mostafa Ameli, Mohamad Sadegh Shirani Faradonbeh, Jean-Patrick Lebacque +2
Departure time choice models play a crucial role in determining the traffic load in transportation systems. This paper introduces a new framework to model and analyze the departure…
Online Distributed Estimation of Principal Eigenspaces
Davoud Ataee Tarzanagh, Mohamad Kazem Shirani Faradonbeh, George Michailidis
Principal components analysis (PCA) is a widely used dimension reduction technique with an extensive range of applications. In this paper, an online distributed algorithm is propos…
Input Perturbations for Adaptive Control and Learning
Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis
This paper studies adaptive algorithms for simultaneous regulation (i.e., control) and estimation (i.e., learning) of Multiple Input Multiple Output (MIMO) linear dynamical systems…
Finite Time Adaptive Stabilization of LQ Systems
Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis
Stabilization of linear systems with unknown dynamics is a canonical problem in adaptive control. Since the lack of knowledge of system parameters can cause it to become destabiliz…
On Adaptive Linear-Quadratic Regulators
Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis
Performance of adaptive control policies is assessed through the regret with respect to the optimal regulator, which reflects the increase in the operating cost due to uncertainty…
Optimism-Based Adaptive Regulation of Linear-Quadratic Systems
Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis
The main challenge for adaptive regulation of linear-quadratic systems is the trade-off between identification and control. An adaptive policy needs to address both the estimation…
Finite Time Identification in Unstable Linear Systems
Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis
Identification of the parameters of stable linear dynamical systems is a well-studied problem in the literature, both in the low and high-dimensional settings. However, there are h…