activity
20172024
most citedA Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testing

3 citations · 6 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL2024

Examining the Role of Relationship Alignment in Large Language Models

Kristen M. Altenburger, Hongda Jiang, Robert E. Kraut +2

The rapid development and deployment of Generative AI in social settings raise important questions about how to optimally personalize them for users while maintaining accuracy and…

stat.ML2023★ 3 cited

A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testing

Yuan Yuan, Kristen M. Altenburger

The reliability of controlled experiments, commonly referred to as "A/B tests," is often compromised by network interference, where the outcomes of individual units are influenced…

cs.SI2023

Node Attribute Prediction on Multilayer Networks with Weighted and Directed Edges

Yiguang Zhang, Kristen Altenburger, Poppy Zhang +2

With the rapid development of digital platforms, users can now interact in endless ways from writing business reviews and comments to sharing information with their friends and fol…

cs.LG2022★ 1 cited

Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection

Peter Henderson, Ben Chugg, Brandon Anderson +5

We introduce a new setting, optimize-and-estimate structured bandits. Here, a policy must select a batch of arms, each characterized by its own context, that would allow it to both…

cs.SI2020

Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests

Yuan Yuan, Kristen M. Altenburger, Farshad Kooti

Randomized experiments, or "A/B" tests, remain the gold standard for evaluating the causal effect of a policy intervention or product change. However, experimental settings, such a…

cs.SI2017★ 2 cited

Bias and variance in the social structure of gender

Kristen M. Altenburger, Johan Ugander

The observation that individuals tend to be friends with people who are similar to themselves, commonly known as homophily, is a prominent and well-studied feature of social networ…