61 citations · 253 across the 29 of their papers we have counts for
7 papers · 1 filter
MIPaaL: Mixed Integer Program as a Layer
Aaron Ferber, Bryan Wilder, Bistra Dilkina +1
Machine learning components commonly appear in larger decision-making pipelines; however, the model training process typically focuses only on a loss that measures accuracy between…
Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling
Harshavardhan Kamarthi, Priyesh Vijayan, Bryan Wilder +2
A serious challenge when finding influential actors in real-world social networks is the lack of knowledge about the structure of the underlying network. Current state-of-the-art m…
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
Po-Wei Wang, Priya L. Donti, Bryan Wilder +1
Integrating logical reasoning within deep learning architectures has been a major goal of modern AI systems. In this paper, we propose a new direction toward this goal by introduci…
End to end learning and optimization on graphs
Bryan Wilder, Eric Ewing, Bistra Dilkina +1
Real-world applications often combine learning and optimization problems on graphs. For instance, our objective may be to cluster the graph in order to detect meaningful communitie…
Group-Fairness in Influence Maximization
Alan Tsang, Bryan Wilder, Eric Rice +2
Influence maximization is a widely used model for information dissemination in social networks. Recent work has employed such interventions across a wide range of social problems,…
End-to-End Game-Focused Learning of Adversary Behavior in Security Games
Andrew Perrault, Bryan Wilder, Eric Ewing +3
Stackelberg security games are a critical tool for maximizing the utility of limited defense resources to protect important targets from an intelligent adversary. Motivated by gree…