24 citations · 33 across the 10 of their papers we have counts for
10 papers
Effective, Efficient, and General Information Abstraction for Imperfect-Information Extensive-Form Games
Boning Li, Longbo Huang
Information abstraction reduces the computational cost of solving imperfect-information games by clustering information sets into a smaller number of \emph{buckets}. Existing metho…
GLANCE: Graph-based Learnable Digital Twin for Communication Networks
Boning Li, Gunjan Verma, Timofey Efimov +2
As digital twins (DTs) to physical communication systems, network simulators can aid the design and deployment of communication networks. However, time-consuming simulations must b…
Deep Demixing: Reconstructing the Evolution of Network Epidemics
Boning Li, Gojko Čutura, Ananthram Swami +1
We propose the deep demixing (DDmix) model, a graph autoencoder that can reconstruct epidemics evolving over networks from partial or aggregated temporal information. Assuming know…
Learnable Digital Twin for Efficient Wireless Network Evaluation
Boning Li, Timofey Efimov, Abhishek Kumar +4
Network digital twins (NDTs) facilitate the estimation of key performance indicators (KPIs) before physically implementing a network, thereby enabling efficient optimization of the…
Hypergraphs with Edge-Dependent Vertex Weights: Spectral Clustering based on the 1-Laplacian
Yu Zhu, Boning Li, Santiago Segarra
We propose a flexible framework for defining the 1-Laplacian of a hypergraph that incorporates edge-dependent vertex weights. These weights are able to reflect varying importance o…
Learning to Transmit with Provable Guarantees in Wireless Federated Learning
Boning Li, Jake Perazzone, Ananthram Swami +1
We propose a novel data-driven approach to allocate transmit power for federated learning (FL) over interference-limited wireless networks. The proposed method is useful in challen…
Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks
Boning Li, Gunjan Verma, Santiago Segarra
We develop a novel graph-based trainable framework to maximize the weighted sum energy efficiency (WSEE) for power allocation in wireless communication networks. To address the non…
Power Allocation for Wireless Federated Learning using Graph Neural Networks
Boning Li, Ananthram Swami, Santiago Segarra
We propose a data-driven approach for power allocation in the context of federated learning (FL) over interference-limited wireless networks. The power policy is designed to maximi…
Co-clustering Vertices and Hyperedges via Spectral Hypergraph Partitioning
Yu Zhu, Boning Li, Santiago Segarra
We propose a novel method to co-cluster the vertices and hyperedges of hypergraphs with edge-dependent vertex weights (EDVWs). In this hypergraph model, the contribution of every v…
Deep Demixing: Reconstructing the Evolution of Epidemics Using Graph Neural Networks
Gojko Cutura, Boning Li, Ananthram Swami +1
We study the temporal reconstruction of epidemics evolving over networks. Given partial or aggregated temporal information of the epidemic, our goal is to estimate the complete evo…