268 citations · 1.1k across the 58 of their papers we have counts for
6 papers · 2 filters
Joint Precision Neural Networks: Task-Aware Dependency and Predictive Learning
Andrea Cavallo, Samuel Rey, Antonio G. Marques +1
Exploiting meaningful latent structures from data to solve downstream tasks is a fundamental challenge in signal processing and machine learning. While Principal Component Analysis…
Spatiotemporal Kronecker Covariance Neural Networks
Andrea Cavallo, Athanasios Georgoutsos, Elvin Isufi
Multivariate time series contain complex patterns that span across both space and time. While covariance-based statistical tools like spatiotemporal Principal Component Analysis (S…
Topological Signal Processing With Unoriented Operators
Andrea Cavallo, Varun Sarathchandran, Geert Leus +1
Topological signal processing (TSP) processes signals on simplicial complexes with oriented boundary operators, which is the natural choice for flow signals or when the topological…
Learning with Covariance Matrices: Principal Component Analysis Meets Learning with Graphs
Saurabh Sihag, Andrea Cavallo, Elvin Isufi +2
This feature article provides an overview of the theoretical foundations for coVariance neural networks (VNNs), i.e., graph neural networks (GNNs) operating on covariance matrices…
Stochastic Sequential Decision Making over Expanding Networks with Graph Filtering
Zhan Gao, Bishwadeep Das, Elvin Isufi
Graph filters leverage topological information to process networked data with existing methods mainly studying fixed graphs, ignoring that graphs often expand as nodes continually…
Flow Matching Policy Optimization with Mirror Descent and Entropy Constraints
Ting Gao, Stavros Orfanoudakis, Nan Lin +3
Balancing policy expressiveness with the exploration-exploitation trade-off is a core challenge in online Reinforcement Learning (RL). While Stochastic Differential Equation (SDE)-…