Alejandro Ribeiro

research

∙ 07/08/2022

Stability of Aggregation Graph Neural Networks

In this paper we study the stability properties of aggregation graph neu...

Alejandro Parada-Mayorga, et al. ∙

research

∙ 07/05/2022

State-Augmented Learnable Algorithms for Resource Management in Wireless Networks

We consider resource management problems in multi-user wireless networks...

Navid NaderiAlizadeh, et al. ∙

research

∙ 05/31/2022

coVariance Neural Networks

Graph neural networks (GNN) are an effective framework that exploit inte...

Saurabh Sihag, et al. ∙

research

∙ 05/19/2022

Graph Neural Networks Are More Powerful Than we Think

Graph Neural Networks (GNNs) are powerful convolutional architectures th...

Charilaos I. Kanatsoulis, et al. ∙

research

∙ 05/19/2022

Learning Graph Structure from Convolutional Mixtures

Machine learning frameworks such as graph neural networks typically rely...

Max Wasserman, et al. ∙

research

∙ 03/07/2022

Learning Resilient Radio Resource Management Policies with Graph Neural Networks

We consider the problems of downlink user selection and power control in...

Navid NaderiAlizadeh, et al. ∙

research

∙ 02/08/2022

A Lagrangian Duality Approach to Active Learning

We consider the batch active learning problem, where only a subset of th...

Juan Elenter, et al. ∙

research

∙ 12/14/2021

Learning Connectivity-Maximizing Network Configurations

In this work we propose a data-driven approach to optimizing the algebra...

Daniel Mox, et al. ∙

research

∙ 12/09/2021

Transferability Properties of Graph Neural Networks

Graph neural networks (GNNs) are deep convolutional architectures consis...

Luana Ruiz, et al. ∙

research

∙ 10/10/2021

Stability of Neural Networks on Manifolds to Relative Perturbations

Graph Neural Networks (GNNs) show impressive performance in many practic...

Zhiyang Wang, et al. ∙

research

∙ 10/07/2021

Training Stable Graph Neural Networks Through Constrained Learning

Graph Neural Networks (GNN) rely on graph convolutions to learn features...

Juan Cervino, et al. ∙

research

∙ 10/06/2021

Space-Time Graph Neural Networks

We introduce space-time graph neural network (ST-GNN), a novel GNN archi...

Samar Hadou, et al. ∙

research

∙ 09/30/2021

Coverage Control in Multi-Robot Systems via Graph Neural Networks

This paper develops a decentralized approach to mobile sensor coverage b...

Walker Gosrich, et al. ∙

research

∙ 08/23/2021

Convolutional Filtering and Neural Networks with Non Commutative Algebras

In this paper we provide stability results for algebraic neural networks...

Alejandro Parada-Mayorga, et al. ∙

research

∙ 07/19/2021

Wide and Deep Graph Neural Network with Distributed Online Learning

Graph neural networks (GNNs) are naturally distributed architectures for...

Zhan Gao, et al. ∙

research

∙ 06/24/2021

Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks

In this paper, we present a perception-action-communication loop design ...

Ting-Kuei Hu, et al. ∙

research

∙ 06/19/2021

Stability of Graph Convolutional Neural Networks to Stochastic Perturbations

Graph convolutional neural networks (GCNNs) are nonlinear processing too...

Zhan Gao, et al. ∙

research

∙ 06/07/2021

Stability of Manifold Neural Networks to Deformations

Stability is an important property of graph neural networks (GNNs) which...

Zhiyang Wang, et al. ∙

research

∙ 06/07/2021

Increase and Conquer: Training Graph Neural Networks on Growing Graphs

Graph neural networks (GNNs) use graph convolutions to exploit network i...

Juan Cervino, et al. ∙

research

∙ 06/05/2021

Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping

Graph neural networks (GNNs) are processing architectures that exploit g...

Zhan Gao, et al. ∙

research

∙ 05/18/2021

Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection

In this paper, we develop a learning-based approach for decentralized su...

Lifeng Zhou, et al. ∙

research

∙ 03/26/2021

Composable Learning with Sparse Kernel Representations

We present a reinforcement learning algorithm for learning sparse non-pa...

Ekaterina Tolstaya, et al. ∙

research

∙ 03/08/2021

Constrained Learning with Non-Convex Losses

Though learning has become a core technology of modern information proce...

Luiz F. O. Chamon, et al. ∙

research

∙ 03/08/2021

Learning Connectivity for Data Distribution in Robot Teams

Many algorithms for control of multi-robot teams operate under the assum...

Ekaterina Tolstaya, et al. ∙

research

∙ 02/23/2021

State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewards

Constrained reinforcement learning involves multiple rewards that must i...

Miguel Calvo-Fullana, et al. ∙

research

∙ 02/11/2021

Large Scale Distributed Collaborative Unlabeled Motion Planning with Graph Policy Gradients

In this paper, we present a learning method to solve the unlabelled moti...

Arbaaz Khan, et al. ∙

research

∙ 02/11/2021

Sufficiently Accurate Model Learning for Planning

Data driven models of dynamical systems help planners and controllers to...

Clark Zhang, et al. ∙

research

∙ 01/25/2021

ROS-NetSim: A Framework for the Integration of Robotic and Network Simulators

Multi-agent systems play an important role in modern robotics. Due to th...

Miguel Calvo-Fullana, et al. ∙

research

∙ 12/29/2020

Decentralized Control with Graph Neural Networks

Dynamical systems consisting of a set of autonomous agents face the chal...

Fernando Gama, et al. ∙

research

∙ 11/24/2020

Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning

Prediction credibility measures, in the form of confidence intervals or ...

Luiz F. O. Chamon, et al. ∙

research

∙ 11/05/2020

Unsupervised Learning for Asynchronous Resource Allocation in Ad-hoc Wireless Networks

We consider optimal resource allocation problems under asynchronous wire...

Zhiyang Wang, et al. ∙

research

∙ 11/02/2020

Multi-Robot Coverage and Exploration using Spatial Graph Neural Networks

The multi-robot coverage problem is an essential building block for syst...

Ekaterina Tolstaya, et al. ∙

research

∙ 10/27/2020

Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks

Graph convolutional neural networks (GCNNs) learn compositional represen...

Luana Ruiz, et al. ∙

research

∙ 10/24/2020

Multi-task Supervised Learning via Cross-learning

In this paper we consider a problem known as multi-task learning, consis...

Juan Cervino, et al. ∙

research

∙ 10/23/2020

Graph and graphon neural network stability

Graph neural networks (GNNs) are learning architectures that rely on kno...

Luana Ruiz, et al. ∙

research

∙ 10/22/2020

Stability of Algebraic Neural Networks to Small Perturbations

Algebraic neural networks (AlgNNs) are composed of a cascade of layers e...

Alejandro Parada-Mayorga, et al. ∙

research

∙ 10/22/2020

Quiver Signal Processing (QSP)

In this paper we state the basics for a signal processing framework on q...

Alejandro Parada-Mayorga, et al. ∙

research

∙ 10/17/2020

Discriminability of Single-Layer Graph Neural Networks

Network data can be conveniently modeled as a graph signal, where data v...

Samuel Pfrommer, et al. ∙

research

∙ 10/16/2020

Policy Gradient for Continuing Tasks in Non-stationary Markov Decision Processes

Reinforcement learning considers the problem of finding policies that ma...

Santiago Paternain, et al. ∙

research

∙ 10/12/2020

Spherical Convolutional Neural Networks: Stability to Perturbations in SO(3)

Spherical signals are useful mathematical models for data arising in man...

Zhan Gao, et al. ∙

research

∙ 09/03/2020

Algebraic Neural Networks: Stability to Deformations

In this work we study the stability of algebraic neural networks (AlgNNs...

Alejandro Parada-Mayorga, et al. ∙

research

∙ 08/04/2020

Graph Neural Networks: Architectures, Stability and Transferability

Graph Neural Networks (GNNs) are information processing architectures fo...

Luana Ruiz, et al. ∙

research

∙ 07/27/2020

Resource Allocation via Model-Free Deep Learning in Free Space Optical Networks

This paper investigates the general problem of resource allocation for m...

Zhan Gao, et al. ∙

research

∙ 07/02/2020

Balancing Rates and Variance via Adaptive Batch-Size for Stochastic Optimization Problems

Stochastic gradient descent is a canonical tool for addressing stochasti...

Zhan Gao, et al. ∙

research

∙ 06/26/2020

Resource Allocation via Graph Neural Networks in Free Space Optical Fronthaul Networks

This paper investigates the optimal resource allocation in free space op...

Zhan Gao, et al. ∙

research

∙ 06/12/2020

Zeroth-order Deterministic Policy Gradient

Deterministic Policy Gradient (DPG) removes a level of randomness from s...

Harshat Kumar, et al. ∙

research

∙ 06/11/2020

Wide and Deep Graph Neural Networks with Distributed Online Learning

Graph neural networks (GNNs) learn representations from network data wit...

Zhan Gao, et al. ∙

research

∙ 06/11/2020

Graph Neural Networks for Motion Planning

This paper investigates the feasibility of using Graph Neural Networks (...

Arbaaz Khan, et al. ∙

research

∙ 06/09/2020

Probably Approximately Correct Constrained Learning

As learning solutions reach critical applications in social, industrial,...

Luiz F. O. Chamon, et al. ∙

research

∙ 06/05/2020

Graphon Neural Networks and the Transferability of Graph Neural Networks

Graph neural networks (GNNs) rely on graph convolutions to extract local...

Luana Ruiz, et al. ∙

Alejandro Ribeiro

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