12 papers
Adaptive Node Feature Selection For Graph Neural Networks
Madeline Navarro, Ali Azizpour, Santiago Segarra
We propose an adaptive node feature selection approach for graph neural networks (GNNs) that identifies and removes unnecessary features during training. The ability to measure how…
Exploiting Non-Negativity in DAG Structure Learning
Samuel Rey, Madeline navarro, Gonzalo Mateos
This work addresses the problem of learning directed acyclic graphs (DAGs) from nodal observations generated by a linear structural equation model. DAG learning is a central task i…
Fair Feature Importance Scores via Feature Occlusion and Permutation
Camille Little, Madeline Navarro, Santiago Segarra +1
As machine learning models increasingly impact society, their opaque nature poses challenges to trust and accountability, particularly in fairness contexts. Understanding how indiv…
Stationarity and Spectral Characterization of Random Signals on Simplicial Complexes
Madeline Navarro, Andrei Buciulea, Santiago Segarra +1
It is increasingly common for data to possess intricate structure, necessitating new models and analytical tools. Graphs, a prominent type of structure, can encode the relationship…
Joint Network Topology Inference in the Presence of Hidden Nodes
Madeline Navarro, Samuel Rey, Andrei Buciulea +2
We investigate the increasingly prominent task of jointly inferring multiple networks from nodal observations. While most joint inference methods assume that observations are avail…
Learning Time-Varying Turn-Taking Behavior in Group Conversations
Madeline Navarro, Lisa O'Bryan, Santiago Segarra
We propose a flexible probabilistic model for predicting turn-taking patterns in group conversations based solely on individual characteristics and past speaking behavior. Many mod…