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 partial …

Abstract Probabilistic Graphical Models are often used to understand dynamics of a system.
They can model relationships between features (nodes) and the underlying distribution …

Sparse graph recovery methods work well where the data follows their assumptions,
however, they are not always designed for doing downstream probabilistic queries. This …

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 benefit from …

Conditional Independence (CI) graph is a special type of a Probabilistic Graphical Model
(PGM) where the feature connections are modeled using an undirected graph and the edge …

Segmentation of multivariate time series data is a valuable technique for identifying
meaningful patterns or changes in the time series that can signal a shift in the system's …

Obstacle-aware trajectory navigation is crucial for many systems. For example, in real-world
navigation tasks, an agent must avoid obstacles, such as furniture in a room, while planning …

We frequently encounter multiple series that are temporally correlated in our surroundings,
such as EEG data to examine alterations in brain activity or sensors to monitor body …

There is a considerable body of work in the field of computer science on the topic of sparse
graph recovery, particularly with regards to the innovative deep learning approaches that …

Segmentation of multivariate time series data is a valuable technique for identifying
meaningful patterns or changes in the time series that can signal a shift in the system's …