452 citations · 818 across the 40 of their papers we have counts for
18 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning
Navid Naderializadeh, Fan H. Hung, Sean Soleyman +1
We propose a novel framework for value function factorization in multi-agent deep reinforcement learning (MARL) using graph neural networks (GNNs). In particular, we consider the t…
Wasserstein Embedding for Graph Learning
Soheil Kolouri, Navid Naderializadeh, Gustavo K. Rohde +1
We present Wasserstein Embedding for Graph Learning (WEGL), a novel and fast framework for embedding entire graphs in a vector space, in which various machine learning models are a…
Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning
Navid Naderializadeh, Jaroslaw Sydir, Meryem Simsek +1
We propose a mechanism for distributed resource management and interference mitigation in wireless networks using multi-agent deep reinforcement learning (RL). We equip each transm…
When Multiple Agents Learn to Schedule: A Distributed Radio Resource Management Framework
Navid Naderializadeh, Jaroslaw Sydir, Meryem Simsek +2
Interference among concurrent transmissions in a wireless network is a key factor limiting the system performance. One way to alleviate this problem is to manage the radio resource…