16 citations · 37 across the 13 of their papers we have counts for
3 papers · 1 filter
Methods for Recovering Conditional Independence Graphs: A Survey
Harsh Shrivastava, Urszula Chajewska
Conditional Independence (CI) graphs are a type of probabilistic graphical models that are primarily used to gain insights about feature relationships. Each edge represents the par…
Neural Graphical Models
Harsh Shrivastava, Urszula Chajewska
Probabilistic Graphical Models are often used to understand dynamics of a system. They can model relationships between features (nodes) and the underlying distribution. Theoretical…
uGLAD: Sparse graph recovery by optimizing deep unrolled networks
Harsh Shrivastava, Urszula Chajewska, Robin Abraham +1
Probabilistic Graphical Models (PGMs) are generative models of complex systems. They rely on conditional independence assumptions between variables to learn sparse representations…