33 citations · 117 across the 22 of their papers we have counts for
6 papers · 1 filter
ROC++: Robust Optimization in C++
Phebe Vayanos, Qing Jin, George Elissaios
Robust optimization is a very popular means to address decision-making problems affected by uncertainty. Its success has been fueled by its attractive robustness and scalability pr…
Exploring Algorithmic Fairness in Robust Graph Covering Problems
Aida Rahmattalabi, Phebe Vayanos, Anthony Fulginiti +4
Fueled by algorithmic advances, AI algorithms are increasingly being deployed in settings subject to unanticipated challenges with complex social effects. Motivated by real-world d…
Fair Influence Maximization: A Welfare Optimization Approach
Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju +5
Several behavioral, social, and public health interventions, such as suicide/HIV prevention or community preparedness against natural disasters, leverage social network information…
Robust Optimization with Decision-Dependent Information Discovery
Phebe Vayanos, Angelos Georghiou, Han Yu
Robust optimization is a popular paradigm for modeling and solving two- and multi-stage decision-making problems affected by uncertainty. In many real-world applications, the time…
Robust Active Preference Elicitation
Phebe Vayanos, Yingxiao Ye, Duncan McElfresh +2
We study the problem of eliciting the preferences of a decision-maker through a moderate number of pairwise comparison queries to make them a high quality recommendation for a spec…
Learning Optimal Classification Trees: Strong Max-Flow Formulations
Sina Aghaei, Andres Gomez, Phebe Vayanos
We consider the problem of learning optimal binary classification trees. Literature on the topic has burgeoned in recent years, motivated both by the empirical suboptimality of heu…