activity
20152026
most citedKernelization via Sampling with Applications to Dynamic Graph Streams

13 citations · 30 across the 11 of their papers we have counts for

collaborators
Showing cs.DSShow all

11 papers · 1 filter

cs.DS2026

Adversarially Robust Approximate Furthest Neighbor

Kiarash Banihashem, Jeff Giliberti, Prashant Gokhale +5

We work in the adaptive query model, where one is given a point set and seeks to construct a data structure that can answer correctly and efficiently a seq…

cs.DS2025

Dynamic Diameter in High-Dimensions against Adaptive Adversary and Beyond

Kiarash Banihashem, Jeff Giliberti, Samira Goudarzi +3

In this paper, we study the fundamental problems of maintaining the diameter and a -center clustering of a dynamic point set , where points may be insert…

cs.DS2024

A Dynamic Algorithm for Weighted Submodular Cover Problem

Kiarash Banihashem, Samira Goudarzi, MohammadTaghi Hajiaghayi +2

We initiate the study of the submodular cover problem in dynamic setting where the elements of the ground set are inserted and deleted. In the classical submodular cover problem, w…

cs.DS2023

Dynamic Non-monotone Submodular Maximization

Kiarash Banihashem, Leyla Biabani, Samira Goudarzi +3

Maximizing submodular functions has been increasingly used in many applications of machine learning, such as data summarization, recommendation systems, and feature selection. More…

cs.DS2023★ 2 cited

Dynamic Algorithms for Matroid Submodular Maximization

Kiarash Banihashem, Leyla Biabani, Samira Goudarzi +3

Submodular maximization under matroid and cardinality constraints are classical problems with a wide range of applications in machine learning, auction theory, and combinatorial op…

cs.DS2023

Dynamic Constrained Submodular Optimization with Polylogarithmic Update Time

Kiarash Banihashem, Leyla Biabani, Samira Goudarzi +3

Maximizing a monotone submodular function under cardinality constraint is a core problem in machine learning and database with many basic applications, including video and data…