Jeremy Kun

Personal

Name Jeremy Kun
Research summary I am a theoretical computer scientist with broad interests, including complexity theory, graph theory and network science, learning theory, combinatorics, and geometry. My research to date focuses on theoretical and applied graph theory. I currently work as a bitcoin engineer at 21 Inc.
Email jeremy /at/ 21.co
Webpage https://jeremykun.com

Publications

2016
Graphs, New Models, and Complexity. Jeremy Kun. The University of Illinois at Chicago.
2016
A Confidence-Based Approach for Balancing Fairness and Accuracy. Benjamin Fish, Jeremy Kun, Adam Lelkes. SIAM International Symposium on Data Mining.
2016
Interception in Distance-Vector Routing Networks. David Burstein, Franklin Kenter, Jeremy Kun, Feng Shi. Journal of Complex Networks.
2015
On the Computational Complexity of MapReduce. Benjamin Fish, Jeremy Kun, Adam Lelkes, Lev Reyzin, Gyorgy Turan. International Symposium on Distributed Computing.
2015
Network Installation Under Convex Costs. Alexander Gutfraind, Jeremy Kun, Adam Lelkes, Lev Reyzin. Journal of Complex Networks.
2015
Fair Boosting: a Case Study. Benjamin Fish, Jeremy Kun, Adam Lelkes. International Conference on Machine Learning Workshop on Fairness, Accountability, and Transparency in Machine Learning.
2015
Open Problem: Learning Quantum Circuits with Queries. Jeremy Kun, Lev Reyzin. Conference on Learning Theory.
2014
A Boosting Approach to Learning Graph Representations. Rajmonda Caceres, Kevin Carter, Jeremy Kun. SIAM International Conference on Data Mining Workshop on Mining Networks and Graphs.
2014
On Coloring Resilient Graphs. Jeremy Kun, Lev Reyzin. Mathematical Foundations of Computer Science.
2013
Anti-Coordination Games and Stable Graph Colorings. Jeremy Kun, Brian Powers, Lev Reyzin. Syposium on Algorithmic Game Theory.

Preprints

Locally Boosted Graph Aggregation for Community Detection. Rajmonda Caceres, Kevin Carter, Jeremy Kun.