1 citations · 1 across the 7 of their papers we have counts for
8 papers
Scalar Communication via Random Direction Refreshing for Distributed Optimization
Mohammadreza Rostami, Solmaz S. Kia
Distributed optimization over networks requires agents to repeatedly exchange decision variables with their neighbors. When the decision dimension is large, these exchanges dom…
The Price of Feasibility: Greedy Approximation Bounds for String Supermodular Optimization over Oracle-Conditioned Greedoids
Joan Vendrell Gallart, Russell Bent, Solmaz Kia
Greedy algorithms efficiently approximate combinatorial optimization problems, but their guarantees weaken when feasibility couples combinatorial structure with global physical con…
Chain-based Adaptive Reconfiguration Over Lattices for Hallucination Reduction
Joan Vendrell Gallart, Solmaz Kia, Russell Bent +1
We introduce CAROL (Chain-based Adaptive Reconfiguration Over Lattices), a probabilistic framework for test-time hallucination reduction in large language models. Rather than relyi…
Adaptive Threshold-Driven Continuous Greedy Method for Scalable Submodular Optimization
Mohammadreza Rostami, Solmaz S. Kia
Submodular maximization under matroid constraints is a fundamental problem in combinatorial optimization with applications in sensing, data summarization, active learning, and reso…
Scalar Federated Learning for Linear Quadratic Regulator
Mohammadreza Rostami, Shahriar Talebi, Solmaz S. Kia
We propose ScalarFedLQR, a communication-efficient federated algorithm for model-free learning of a common policy in linear quadratic regulator (LQR) control of cooperative agents.…
FedMPDD: Communication-Efficient Federated Learning with Privacy Preservation Attributes via Projected Directional Derivative
Mohammadreza Rostami, Solmaz S. Kia
This paper introduces \texttt{FedMPDD} (\textbf{Fed}erated Learning via \textbf{M}ulti-\textbf{P}rojected \textbf{D}irectional \textbf{D}erivatives), a novel algorithm that simulta…
Time-Varying Convex Optimization with Computational Complexity
M. Rostami, S. S. Kia
In this article, we consider the problem of unconstrained time-varying convex optimization, where the cost function changes with time. We provide an in-depth technical analysis of…
FedScalar: Federated Learning with Scalar Communication for Bandwidth-Constrained Networks
M. Rostami, S. S. Kia
In bandwidth-constrained federated learning~(FL) settings, the repeated upload of high-dimensional model updates from agents to a central server constitutes the primary bottleneck,…