268 citations · 1.1k across the 58 of their papers we have counts for
34 papers · 1 filter
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)-…
Covariance Scattering Transforms
Andrea Cavallo, Ayushman Raghuvanshi, Sundeep Prabhakar Chepuri +1
Machine learning and data processing techniques relying on covariance information are widespread as they identify meaningful patterns in unsupervised and unlabeled settings. As a p…
Graph-Aware Diffusion for Signal Generation
Sergio Rozada, Vimal K. B., Andrea Cavallo +3
We study the problem of generating graph signals from unknown distributions defined over given graphs, relevant to domains such as recommender systems or sensor networks. Our appro…
CoVariance Filters and Neural Networks over Hilbert Spaces
Claudio Battiloro, Andrea Cavallo, Elvin Isufi
CoVariance Neural Networks (VNNs) perform graph convolutions on the empirical covariance matrix of signals defined over finite-dimensional Hilbert spaces, motivated by robustness a…
Q-Net: Queue Length Estimation via Kalman-based Neural Networks
Ting Gao, Elvin Isufi, Winnie Daamen +2
Estimating queue lengths at signalized intersections is a long-standing challenge in traffic management. Partial observability of vehicle flows complicates this task despite the av…
Precision Neural Networks: Joint Graph And Relational Learning
Andrea Cavallo, Samuel Rey, Antonio G. Marques +1
CoVariance Neural Networks (VNNs) perform convolutions on the graph determined by the covariance matrix of the data, which enables expressive and stable covariance-based learning.…
GKNet: Graph Kalman Filtering and Model Inference via Model-based Deep Learning
Mohammad Sabbaqi, Riccardo Taormina, Elvin Isufi
Inference tasks with time series over graphs are of importance in applications such as urban water networks, economics, and networked neuroscience. Addressing these tasks typically…
Directed Semi-Simplicial Learning with Applications to Brain Activity Decoding
Manuel Lecha, Andrea Cavallo, Francesca Dominici +5
Graph Neural Networks (GNNs) excel at learning from pairwise interactions but often overlook multi-way and hierarchical relationships. Topological Deep Learning (TDL) addresses thi…
Sparse Covariance Neural Networks
Andrea Cavallo, Zhan Gao, Elvin Isufi
Covariance Neural Networks (VNNs) perform graph convolutions on the covariance matrix of input data to leverage correlation information as pairwise connections. They have achieved…
Spatiotemporal Covariance Neural Networks
Andrea Cavallo, Mohammad Sabbaqi, Elvin Isufi
Modeling spatiotemporal interactions in multivariate time series is key to their effective processing, but challenging because of their irregular and often unknown structure. Stati…
Online Learning Of Expanding Graphs
Samuel Rey, Bishwadeep Das, Elvin Isufi
This paper addresses the problem of online network topology inference for expanding graphs from a stream of spatiotemporal signals. Online algorithms for dynamic graph learning are…
Online Graph Filtering Over Expanding Graphs
Bishwadeep Das, Elvin Isufi
Graph filters are a staple tool for processing signals over graphs in a multitude of downstream tasks. However, they are commonly designed for graphs with a fixed number of nodes,…
Fair CoVariance Neural Networks
Andrea Cavallo, Madeline Navarro, Santiago Segarra +1
Covariance-based data processing is widespread across signal processing and machine learning applications due to its ability to model data interconnectivities and dependencies. How…
Higher-Order Topological Directionality and Directed Simplicial Neural Networks
Manuel Lecha, Andrea Cavallo, Francesca Dominici +2
Topological Deep Learning (TDL) has emerged as a paradigm to process and learn from signals defined on higher-order combinatorial topological spaces, such as simplicial or cell com…
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks
Alexander Möllers, Alexander Immer, Elvin Isufi +1
Graph contrastive learning has shown great promise when labeled data is scarce, but large unlabeled datasets are available. However, it often does not take uncertainty estimation i…
Hodge-Aware Contrastive Learning
Alexander Möllers, Alexander Immer, Vincent Fortuin +1
Simplicial complexes prove effective in modeling data with multiway dependencies, such as data defined along the edges of networks or within other higher-order structures. Their sp…
Convolutional Learning on Simplicial Complexes
Maosheng Yang, Elvin Isufi
We propose a simplicial complex convolutional neural network (SCCNN) to learn data representations on simplicial complexes. It performs convolutions based on the multi-hop simplici…
Deep Statistical Solver for Distribution System State Estimation
Benjamin Habib, Elvin Isufi, Ward van Breda +2
Implementing accurate Distribution System State Estimation (DSSE) faces several challenges, among which the lack of observability and the high density of the distribution system. W…
Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis
Mohammad Sabbaqi, Elvin Isufi
Devising and analyzing learning models for spatiotemporal network data is of importance for tasks including forecasting, anomaly detection, and multi-agent coordination, among othe…