6 papers
Learning Policy Representations for Steerable Behavior Synthesis
Beiming Li, Sergio Rozada, Alejandro Ribeiro
Given a Markov decision process (MDP), we seek to learn representations for a range of policies to facilitate behavior steering at test time. As policies of an MDP are uniquely det…
Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks
Xingran Chen, Navid NaderiAlizadeh, Alejandro Ribeiro +1
We address real-time sampling and estimation of autoregressive Markovian sources in dynamic yet structurally similar multi-hop wireless networks. Each node caches samples from othe…
Learning Optimal Power Flow with Pointwise Constraints
Damian Owerko, Anna Scaglione, Alejandro Ribeiro
Training learning parameterizations to solve optimal power flow (OPF) with pointwise constraints is proposed. In this novel training approach, a learning parameterization is substi…
Wireless Link Scheduling with State-Augmented Graph Neural Networks
Romina Garcia Camargo, Zhiyang Wang, Navid NaderiAlizadeh +1
We consider the problem of optimal link scheduling in large-scale wireless ad hoc networks. We specifically aim for the maximum long-term average performance, subject to a minimum…
Opportunistic Routing in Wireless Communications via Learnable State-Augmented Policies
Sourajit Das, Kirtan Gopal Panda, Navid NaderiAlizadeh
This paper addresses the challenge of packet-based information routing in large-scale wireless communication networks. The problem is framed as a constrained statistical learning t…
Generative Diffusion Models for Resource Allocation in Wireless Networks
Yigit Berkay Uslu, Samar Hadou, Shirin Saeedi Bidokhti +1
This paper proposes a supervised training algorithm for learning stochastic resource allocation policies with generative diffusion models (GDMs). We formulate the allocation proble…