42 citations · 82 across the 10 of their papers we have counts for
14 papers
Do Not Harm Protected Groups in Debiasing Language Representation Models
Chloe Qinyu Zhu, Rickard Stureborg, Brandon Fain
Language Representation Models (LRMs) trained with real-world data may capture and exacerbate undesired bias and cause unfair treatment of people in various demographic groups. Sev…
Multi-objective Reinforcement Learning with Nonlinear Preferences: Provable Approximation for Maximizing Expected Scalarized Return
Nianli Peng, Muhang Tian, Brandon Fain
We study multi-objective reinforcement learning with nonlinear preferences over trajectories. That is, we maximize the expected value of a nonlinear function over accumulated rewar…
Fairness in the Assignment Problem with Uncertain Priorities
Zeyu Shen, Zhiyi Wang, Xingyu Zhu +2
In the assignment problem, a set of items must be allocated to unit-demand agents who express ordinal preferences (rankings) over the items. In the assignment problem with prioriti…
Multi-Analyst Differential Privacy for Online Query Answering
David Pujol, Albert Sun, Brandon Fain +1
Most differentially private mechanisms are designed for the use of a single analyst. In reality, however, there are often multiple stakeholders with different and possibly conflict…
Welfare and Fairness in Multi-objective Reinforcement Learning
Zimeng Fan, Nianli Peng, Muhang Tian +1
We study fair multi-objective reinforcement learning in which an agent must learn a policy that simultaneously achieves high reward on multiple dimensions of a vector-valued reward…
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,…
Sequential Deliberation for Social Choice
Brandon Fain, Ashish Goel, Kamesh Munagala +1
In large scale collective decision making, social choice is a normative study of how one ought to design a protocol for reaching consensus. However, in instances where the underlyi…
The Core of the Participatory Budgeting Problem
Brandon Fain, Ashish Goel, Kamesh Munagala
In participatory budgeting, communities collectively decide on the allocation of public tax dollars for local public projects. In this work, we consider the question of fairly aggr…
ROBUS: Fair Cache Allocation for Multi-tenant Data-parallel Workloads
Mayuresh Kunjir, Brandon Fain, Kamesh Munagala +1
Systems for processing big data---e.g., Hadoop, Spark, and massively parallel databases---need to run workloads on behalf of multiple tenants simultaneously. The abundant disk-base…