3 papers
cs.LG2024
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…
cs.LG2022
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…
cs.LG2022
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…