19 papers
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)-…
Topological Kalman Filtering on Cell Complexes
Chengen Liu, Rohan Money, Ting Gao +3
Inferring latent dynamics from multivariate time-series defined over topological cell complexes is crucial for capturing the complex, higher-order interactions inherent in real-wor…
Learning Product Graphs from Two-dimensional Stationary Signals
Andrei Buciulea, Bishwadeep Das, Elvin Isufi +1
Graph learning aims to infer a network structure directly from observed data, enabling the analysis of complex dependencies in irregular domains. Traditional methods focus on scala…
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…