no code implementations • 4 Apr 2024 • Xingran Chen, Navid Naderializadeh, Alejandro Ribeiro, Shirin Saeedi Bidokhti
Our goal is to minimize time-average estimation error and/or age of information with decentralized scalable sampling and transmission policies, considering both oblivious (where decision-making is independent of the physical processes) and non-oblivious policies (where decision-making depends on physical processes).
2 code implementations • 25 Dec 2023 • Samar Hadou, Navid Naderializadeh, Alejandro Ribeiro
To tackle these problems, we provide deep unrolled architectures with a stochastic descent nature by imposing descending constraints during training.
1 code implementation • 30 Sep 2023 • 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 to only local information.
no code implementations • 29 Sep 2023 • Juan Elenter, Navid Naderializadeh, Tara Javidi, Alejandro Ribeiro
Continual learning is inherently a constrained learning problem.
1 code implementation • 24 May 2023 • Samar Hadou, Navid Naderializadeh, Alejandro Ribeiro
We introduce Stochastic UnRolled Federated learning (SURF), a method that expands algorithm unrolling to federated learning in order to expedite its convergence.
no code implementations • 28 Oct 2022 • Yiğit Berkay Uslu, Navid Naderializadeh, Mark Eisen, Alejandro Ribeiro
We propose a state-augmented parameterization for the RRM policy, where alongside the instantaneous network states, the RRM policy takes as input the set of dual variables corresponding to the constraints.
no code implementations • 1 Oct 2022 • 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.
1 code implementation • 5 Jul 2022 • 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 average performance of users across the network.
1 code implementation • 7 Mar 2022 • 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 devices (UEs) over a shared wireless medium.
no code implementations • 8 Feb 2022 • 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 to maximally improve model performance.
1 code implementation • NeurIPS 2021 • Navid Naderializadeh, Joseph Comer, Reed Andrews, Heiko Hoffmann, Soheil Kolouri
Learning representations from sets has become increasingly important with many applications in point cloud processing, graph learning, image/video recognition, and object detection.
1 code implementation • 4 Oct 2021 • 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 learning.
no code implementations • 5 Mar 2021 • Navid Naderializadeh, Soheil Kolouri, Joseph F. Comer, Reed W. Andrews, Heiko Hoffmann
An increasing number of machine learning tasks deal with learning representations from set-structured data.
1 code implementation • 22 Oct 2020 • Navid Naderializadeh
We propose a new approach for power control in wireless networks using self-supervised learning.
1 code implementation • 9 Oct 2020 • Navid Naderializadeh, Fan H. Hung, Sean Soleyman, Deepak Khosla
We propose a novel framework for value function factorization in multi-agent deep reinforcement learning (MARL) using graph neural networks (GNNs).
1 code implementation • ICLR 2021 • Soheil Kolouri, Navid Naderializadeh, Gustavo K. Rohde, Heiko Hoffmann
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 applicable for graph-level prediction tasks.
Ranked #3 on Graph Classification on RE-M5K
no code implementations • 17 Feb 2020 • Navid Naderializadeh, Mark Eisen, Alejandro Ribeiro
We consider the problem of downlink power control in wireless networks, consisting of multiple transmitter-receiver pairs communicating with each other over a single shared wireless medium.
no code implementations • 14 Feb 2020 • Navid Naderializadeh, Jaroslaw Sydir, Meryem Simsek, Hosein Nikopour
We propose a mechanism for distributed resource management and interference mitigation in wireless networks using multi-agent deep reinforcement learning (RL).
no code implementations • 10 Feb 2020 • Navid Naderializadeh
We consider a wireless network comprising $n$ nodes located within a circular area of radius $R$, which are participating in a decentralized learning algorithm to optimize a global objective function using their local datasets.
no code implementations • 22 Dec 2019 • Navid Naderializadeh, Morteza Hashemi
We investigate the problem of computation offloading in a mobile edge computing architecture, where multiple energy-constrained users compete to offload their computational tasks to multiple servers through a shared wireless medium.
no code implementations • 9 Dec 2019 • Navid Naderializadeh, Seyed Mohammad Asghari
We consider a system comprising a file library and a network with a server and multiple users equipped with cache memories.
no code implementations • 20 Jun 2019 • Navid Naderializadeh, Jaroslaw Sydir, Meryem Simsek, Hosein Nikopour, Shilpa Talwar
Interference among concurrent transmissions in a wireless network is a key factor limiting the system performance.