2 citations · 2 across the 4 of their papers we have counts for
6 papers
DeepWeightFlow: Re-Basined Flow Matching for Generating Neural Network Weights
Saumya Gupta, Scott Biggs, Moritz Laber +3
Building efficient and effective generative models for neural network weights has been a research focus of significant interest that faces challenges posed by the high-dimensional…
REGE: A Method for Incorporating Uncertainty in Graph Embeddings
Zohair Shafi, Germans Savcisens, Tina Eliassi-Rad
Machine learning models for graphs in real-world applications are prone to two primary types of uncertainty: (1) those that arise from incomplete and noisy data and (2) those that…
Attacking Shortest Paths by Cutting Edges
Benjamin A. Miller, Zohair Shafi, Wheeler Ruml +3
Identifying shortest paths between nodes in a network is a common graph analysis problem that is important for many applications involving routing of resources. An adversary that c…
Optimal Edge Weight Perturbations to Attack Shortest Paths
Benjamin A. Miller, Zohair Shafi, Wheeler Ruml +3
Finding shortest paths in a given network (e.g., a computer network or a road network) is a well-studied task with many applications. We consider this task under the presence of an…
PATHATTACK: Attacking Shortest Paths in Complex Networks
Benjamin A. Miller, Zohair Shafi, Wheeler Ruml +3
Shortest paths in complex networks play key roles in many applications. Examples include routing packets in a computer network, routing traffic on a transportation network, and inf…
RAWLSNET: Altering Bayesian Networks to Encode Rawlsian Fair Equality of Opportunity
David Liu, Zohair Shafi, William Fleisher +2
We present RAWLSNET, a system for altering Bayesian Network (BN) models to satisfy the Rawlsian principle of fair equality of opportunity (FEO). RAWLSNET's BN models generate aspir…