3 citations · 10 across the 8 of their papers we have counts for
3 papers · 1 filter
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…
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…
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…