4 papers · 1 filter
Stabilizing the Kumaraswamy Distribution
Max Wasserman, Gonzalo Mateos
Large-scale latent variable models require expressive continuous distributions that support efficient sampling and low-variance differentiation, achievable through the reparameteri…
Graph Structure Learning with Interpretable Bayesian Neural Networks
Max Wasserman, Gonzalo Mateos
Graphs serve as generic tools to encode the underlying relational structure of data. Often this graph is not given, and so the task of inferring it from nodal observations becomes…
pyGSL: A Graph Structure Learning Toolkit
Max Wasserman, Gonzalo Mateos
We introduce pyGSL, a Python library that provides efficient implementations of state-of-the-art graph structure learning models along with diverse datasets to evaluate them on. Th…
Learning Graph Structure from Convolutional Mixtures
Max Wasserman, Saurabh Sihag, Gonzalo Mateos +1
Machine learning frameworks such as graph neural networks typically rely on a given, fixed graph to exploit relational inductive biases and thus effectively learn from network data…