4 citations · 4 across the 14 of their papers we have counts for
14 papers
RKHS Representation of Algebraic Convolutional Filters with Integral Operators
Alejandro Parada-Mayorga, Alejandro Ribeiro, Juan Bazerque
Integral operators play a central role in signal processing, underpinning classical convolution, and filtering on continuous network models such as graphons. While these operators…
Size-Transferability of Graph Transformers with Convolutional Positional Encodings
Javier Porras-Valenzuela, Zhiyang Wang, Xiaotao Shang +3
Transformers have achieved remarkable success across domains, motivating the rise of Graph Transformers (GTs) as attention-based architectures for graph-structured data. A key desi…
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…
Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression
Yigit Berkay Uslu, Navid NaderiAlizadeh, Mark Eisen +1
We consider resource allocation problems in multi-user wireless networks, where the goal is to optimize a network-wide utility function subject to constraints on the ergodic averag…
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…
Learning to Slice Wi-Fi Networks: A State-Augmented Primal-Dual Approach
Yiğit Berkay Uslu, Roya Doostnejad, Alejandro Ribeiro +1
Network slicing is a key feature in 5G/NG cellular networks that creates customized slices for different service types with various quality-of-service (QoS) requirements, which can…
Transferable Graphical MARL for Real-Time Estimation in Dynamic Wireless Networks
Xingran Chen, Navid NaderiAlizadeh, Alejandro Ribeiro +1
We study real-time sampling and estimation of autoregressive Markovian sources in decentralized and dynamic multi-hop networks that share similar structures. Nodes cache neighborin…
Robust Stochastically-Descending Unrolled Networks
Samar Hadou, Navid NaderiAlizadeh, Alejandro Ribeiro
Deep unrolling, or unfolding, is an emerging learning-to-optimize method that unrolls a truncated iterative algorithm in the layers of a trainable neural network. However, the conv…
Learning State-Augmented Policies for Information Routing in Communication Networks
Sourajit Das, Navid NaderiAlizadeh, Alejandro Ribeiro
This paper examines the problem of information routing in a large-scale communication network, which can be formulated as a constrained statistical learning problem having access t…
Primal Dual Continual Learning: Balancing Stability and Plasticity through Adaptive Memory Allocation
Juan Elenter, Navid NaderiAlizadeh, Tara Javidi +1
Continual learning is inherently a constrained learning problem. The goal is to learn a predictor under a no-forgetting requirement. Although several prior studies formulate it as…