15 papers · 1 filter
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…
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)-…
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…
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…
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.…
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…