5 citations · 10 across the 5 of their papers we have counts for
8 papers
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…
Graph-Based Nearest-Neighbor Search without the Spread
Jeff Giliberti, Sariel Har-Peled, Jonas Sauer +1
Recent work showed how to construct nearest-neighbor graphs of linear size, on a given set of points in , such that one can answer ap…
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…
Improved parallel derandomization via finite automata with applications
Jeff Giliberti, David G. Harris
A central approach to algorithmic derandomization is to construct probability distributions with small support that "fool" randomized algorithms, often enabling efficient parallel…
Massively Parallel Ruling Set Made Deterministic
Jeff Giliberti, Zahra Parsaeian
We study the deterministic complexity of the -Ruling Set problem in the model of Massively Parallel Computation (MPC) with linear and strongly sublinear local memory. Linear MPC…
Deterministic Massively Parallel Symmetry Breaking for Sparse Graphs
Manuela Fischer, Jeff Giliberti, Christoph Grunau
We consider the problem of designing deterministic graph algorithms for the model of Massively Parallel Computation (MPC) that improve with the sparsity of the input graph, as meas…
Improved Deterministic Connectivity in Massively Parallel Computation
Manuela Fischer, Jeff Giliberti, Christoph Grunau
A long line of research about connectivity in the Massively Parallel Computation model has culminated in the seminal works of Andoni et al. [FOCS'18] and Behnezhad et al. [FOCS'19]…
Improved Online Algorithm for Fractional Knapsack in the Random Order Model
Jeff Giliberti, Andreas Karrenbauer
The fractional knapsack problem is one of the classical problems in combinatorial optimization, which is well understood in the offline setting. However, the corresponding online s…