1 citations · 1 across the 2 of their papers we have counts for
7 papers
Centrality with Diversity
Liang Lyu, Brandon Fain, Kamesh Munagala +1
Graph centrality measures use the structure of a network to quantify central or "important" nodes, with applications in web search, social media analysis, and graphical data mining…
Budget Sharing for Multi-Analyst Differential Privacy
David Pujol, Yikai Wu, Brandon Fain +1
Large organizations that collect data about populations (like the US Census Bureau) release summary statistics that are used by multiple stakeholders for resource allocation and po…
Concentration of Distortion: The Value of Extra Voters in Randomized Social Choice
Brandon Fain, William Fan, Kamesh Munagala
We study higher statistical moments of Distortion for randomized social choice in a metric implicit utilitarian model. The Distortion of a social choice mechanism is the expected a…
Proportionally Fair Clustering
Xingyu Chen, Brandon Fain, Liang Lyu +1
We extend the fair machine learning literature by considering the problem of proportional centroid clustering in a metric context. For clustering points with centers, we de…
Random Dictators with a Random Referee: Constant Sample Complexity Mechanisms for Social Choice
Brandon Fain, Ashish Goel, Kamesh Munagala +1
We study social choice mechanisms in an implicit utilitarian framework with a metric constraint, where the goal is to minimize \textit{Distortion}, the worst case social cost of an…
Fair Allocation of Indivisible Public Goods
Brandon Fain, Kamesh Munagala, Nisarg Shah
We consider the problem of fairly allocating indivisible public goods. We model the public goods as elements with feasibility constraints on what subsets of elements can be chosen,…