452 citations · 818 across the 40 of their papers we have counts for
42 papers
Long-Horizon Wireless Link Scheduling with State-Augmented Graph Neural Networks
Romina Garcia Camargo, Zhiyang Wang, Navid NaderiAlizadeh +1
We address optimal link scheduling in large-scale wireless networks. The goal is to schedule transmissions over a time horizon so that to maximize sum rate while ensuring that aver…
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…
LUNA: Linear Universal Neural Attention with Generalization Guarantees
Ashkan Shahbazi, Ping He, Ali Abbasi +6
Scaling attention faces a critical bottleneck: the quadratic computational cost of softmax attention, which limits its application in long-sequence domains. Whil…
Efficient Transferable Optimal Transport via Min-Sliced Transport Plans
Xinran Liu, Elaheh Akbari, Rocio Diaz Martin +2
Optimal Transport (OT) offers a powerful framework for finding correspondences between distributions and addressing matching and alignment problems in various areas of computer vis…
Reinforcement Learning-Based Optimization of CT Acquisition and Reconstruction Parameters Through Virtual Imaging Trials
David Fenwick, Navid NaderiAlizadeh, Vahid Tarokh +7
Protocol optimization is critical in Computed Tomography (CT) to achieve high diagnostic image quality while minimizing radiation dose. However, due to the complex interdependencie…
LOTFormer: Doubly-Stochastic Linear Attention via Low-Rank Optimal Transport
Ashkan Shahbazi, Chayne Thrash, Yikun Bai +3
Transformers have proven highly effective across modalities, but standard softmax attention scales quadratically with sequence length, limiting long context modeling. Linear attent…
Constrained Sliced Wasserstein Embedding
Navid NaderiAlizadeh, Darian Salehi, Xinran Liu +1
Sliced Wasserstein (SW) distances offer an efficient method for comparing high-dimensional probability measures by projecting them onto multiple 1-dimensional probability distribut…
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…
ESPFormer: Doubly-Stochastic Attention with Expected Sliced Transport Plans
Ashkan Shahbazi, Elaheh Akbari, Darian Salehi +3
While self-attention has been instrumental in the success of Transformers, it can lead to over-concentration on a few tokens during training, resulting in suboptimal information fl…
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…
Stochastic Unrolled Federated Learning
Samar Hadou, Navid NaderiAlizadeh, Alejandro Ribeiro
Algorithm unrolling has emerged as a learning-based optimization paradigm that unfolds truncated iterative algorithms in trainable neural-network optimizers. We introduce Stochasti…
A State-Augmented Approach for Learning Optimal Resource Management Decisions in Wireless Networks
Yiğit Berkay Uslu, Navid NaderiAlizadeh, Mark Eisen +1
We consider a radio resource management (RRM) problem in a multi-user wireless network, where the goal is to optimize a network-wide utility function subject to constraints on the…
Federated Representation Learning via Maximal Coding Rate Reduction
Juan Cervino, Navid NaderiAlizadeh, Alejandro Ribeiro
We propose a federated methodology to learn low-dimensional representations from a dataset that is distributed among several clients. In particular, we move away from the commonly-…
State-Augmented Learnable Algorithms for Resource Management in Wireless Networks
Navid NaderiAlizadeh, Mark Eisen, Alejandro Ribeiro
We consider resource management problems in multi-user wireless networks, which can be cast as optimizing a network-wide utility function, subject to constraints on the long-term a…
Learning Resilient Radio Resource Management Policies with Graph Neural Networks
Navid NaderiAlizadeh, Mark Eisen, Alejandro Ribeiro
We consider the problems of user selection and power control in wireless interference networks, comprising multiple access points (APs) communicating with a group of user equipment…
A Lagrangian Duality Approach to Active Learning
Juan Elenter, Navid NaderiAlizadeh, Alejandro Ribeiro
We consider the pool-based active learning problem, where only a subset of the training data is labeled, and the goal is to query a batch of unlabeled samples to be labeled so as t…
Wireless Link Scheduling via Graph Representation Learning: A Comparative Study of Different Supervision Levels
Navid Naderializadeh
We consider the problem of binary power control, or link scheduling, in wireless interference networks, where the power control policy is trained using graph representation learnin…
Set Representation Learning with Generalized Sliced-Wasserstein Embeddings
Navid Naderializadeh, Soheil Kolouri, Joseph F. Comer +2
An increasing number of machine learning tasks deal with learning representations from set-structured data. Solutions to these problems involve the composition of permutation-equiv…