69 citations · 238 across the 13 of their papers we have counts for
8 papers · 1 filter
Federated Learning with Neural Graphical Models
Urszula Chajewska, Harsh Shrivastava
Federated Learning (FL) addresses the need to create models based on proprietary data in such a way that multiple clients retain exclusive control over their data, while all benefi…
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?
Zhi Chen, Sarah Tan, Urszula Chajewska +2
Missing values are a fundamental problem in data science. Many datasets have missing values that must be properly handled because the way missing values are treated can have large…
Neural Graph Revealers
Harsh Shrivastava, Urszula Chajewska
Sparse graph recovery methods work well where the data follows their assumptions but often they are not designed for doing downstream probabilistic queries. This limits their adopt…
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…