3 citations · 10 across the 8 of their papers we have counts for
9 papers
Polynomial Treedepth Bounds in Linear Colorings
Jeremy Kun, Michael P. O'Brien, Marcin Pilipczuk +1
Low-treedepth colorings are an important tool for algorithms that exploit structure in classes of bounded expansion; they guarantee subgraphs that use few colors have bounded treed…
A Confidence-Based Approach for Balancing Fairness and Accuracy
Benjamin Fish, Jeremy Kun, Ádám D. Lelkes
We study three classical machine learning algorithms in the context of algorithmic fairness: adaptive boosting, support vector machines, and logistic regression. Our goal is to mai…
Interception in Distance-Vector Routing Networks
David Burstein, Franklin Kenter, Jeremy Kun +1
Despite the large effort devoted to cybersecurity research over the last decades, cyber intrusions and attacks are still increasing. With respect to routing networks, route hijacki…
Network installation and recovery: approximation lower bounds and faster exact formulations
Alexander Gutfraind, Jeremy Kun, Ádám D. Lelkes +1
We study the Neighbor Aided Network Installation Problem (NANIP) introduced previously which asks for a minimal cost ordering of the vertices of a graph, where the cost of visiting…
On the Computational Complexity of MapReduce
Benjamin Fish, Jeremy Kun, Ádám Dániel Lelkes +2
In this paper we study MapReduce computations from a complexity-theoretic perspective. First, we formulate a uniform version of the MRC model of Karloff et al. (2010). We then show…
Locally Boosted Graph Aggregation for Community Detection
Jeremy Kun, Rajmonda Caceres, Kevin Carter
Learning the right graph representation from noisy, multi-source data has garnered significant interest in recent years. A central tenet of this problem is relational learning. Her…
On Coloring Resilient Graphs
Jeremy Kun, Lev Reyzin
We introduce a new notion of resilience for constraint satisfaction problems, with the goal of more precisely determining the boundary between NP-hardness and the existence of effi…
A Boosting Approach to Learning Graph Representations
Rajmonda Caceres, Kevin Carter, Jeremy Kun
Learning the right graph representation from noisy, multisource data has garnered significant interest in recent years. A central tenet of this problem is relational learning. Here…
Anti-Coordination Games and Stable Graph Colorings
Jeremy Kun, Brian Powers, Lev Reyzin
Motivated by understanding non-strict and strict pure strategy equilibria in network anti-coordination games, we define notions of stable and, respectively, strictly stable colorin…