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Found 22 papers, 10 papers with code
Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks
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).
Robust Stochastically-Descending Unrolled Networks
To tackle these problems, we provide deep unrolled architectures with a stochastic descent nature by imposing descending constraints during training.
Learning State-Augmented Policies for Information Routing in Communication Networks
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.
Primal-Dual Continual Learning: Stability and Plasticity through Lagrange Multipliers
Continual learning is inherently a constrained learning problem.
Stochastic Unrolled Federated Learning
We introduce Stochastic UnRolled Federated learning (SURF), a method that expands algorithm unrolling to federated learning in order to expedite its convergence.
A State-Augmented Approach for Learning Optimal Resource Management Decisions in Wireless Networks
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.
Federated Representation Learning via Maximal Coding Rate Reduction
We propose a federated methodology to learn low-dimensional representations from a dataset that is distributed among several clients.
State-Augmented Learnable Algorithms for Resource Management in Wireless Networks
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.
Learning Resilient Radio Resource Management Policies with Graph Neural Networks
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.
A Lagrangian Duality Approach to Active Learning
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.
Pooling by Sliced-Wasserstein Embedding
Learning representations from sets has become increasingly important with many applications in point cloud processing, graph learning, image/video recognition, and object detection.
Wireless Link Scheduling via Graph Representation Learning: A Comparative Study of Different Supervision Levels
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.
Set Representation Learning with Generalized Sliced-Wasserstein Embeddings
An increasing number of machine learning tasks deal with learning representations from set-structured data.
Contrastive Self-Supervised Learning for Wireless Power Control
We propose a new approach for power control in wireless networks using self-supervised learning.
Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning
We propose a novel framework for value function factorization in multi-agent deep reinforcement learning (MARL) using graph neural networks (GNNs).
Wasserstein Embedding for Graph Learning
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
Wireless Power Control via Counterfactual Optimization of Graph Neural Networks
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.
Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning
We propose a mechanism for distributed resource management and interference mitigation in wireless networks using multi-agent deep reinforcement learning (RL).
On the Communication Latency of Wireless Decentralized Learning
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.
Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach
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.
Learning to Code: Coded Caching via Deep Reinforcement Learning
We consider a system comprising a file library and a network with a server and multiple users equipped with cache memories.
When Multiple Agents Learn to Schedule: A Distributed Radio Resource Management Framework
Interference among concurrent transmissions in a wireless network is a key factor limiting the system performance.
