activity
20172019
most citedOrder Optimal One-Shot Distributed Learning

4 citations · 5 across the 5 of their papers we have counts for

collaborators

6 papers

cs.LG2019★ 4 cited

Order Optimal One-Shot Distributed Learning

Arsalan Sharifnassab, Saber Salehkaleybar, S. Jamaloddin Golestani

We consider distributed statistical optimization in one-shot setting, where there are machines each observing i.i.d. samples. Based on its observed samples, each machine th…

eess.SY2019★ 1 cited

Nonexpansive Piecewise Constant Hybrid Systems are Conservative

Arsalan Sharifnassab, John N. Tsitsiklis, S. Jamaloddin Golestani

Consider a partition of into finitely many polyhedral regions and associated drift vectors . We study ``hybrid'' dynamical systems whose trajectories have a…

eess.SY2019

When do Trajectories have Bounded Sensitivity to Cumulative Perturbations?

Arsalan Sharifnassab, S. Jamaloddin Golestani

We investigate sensitivity to cumulative perturbations for a few dynamical system classes of practical interest. A system is said to have bounded sensitivity to cumulative perturba…

cs.NI2018

Fluctuation Bounds for the Max-Weight Policy, with Applications to State Space Collapse

Arsalan Sharifnassab, John N. Tsitsiklis, S. Jamaloddin Golestani

We consider a multi-hop switched network operating under a Max-Weight (MW) scheduling policy, and show that the distance between the queue length process and a fluid solution remai…

eess.SY2018

Sensitivity to Cumulative Perturbations for a Class of Piecewise Constant Hybrid Systems

Arsalan Sharifnassab, John N. Tsitsiklis, Jamaloddin Golestani

We consider a class of continuous-time hybrid dynamical systems that correspond to subgradient flows of a piecewise linear and convex potential function with finitely many pieces,…

cs.DC2017

Distributed Voting/Ranking with Optimal Number of States per Node

Saber Salehkaleybar, Arsalan Sharif-Nassab, S. Jamaloddin Golestani

Considering a network with nodes, where each node initially votes for one (or more) choices out of possible choices, we present a Distributed Multi-choice Voting/Ranking (D…