
Algebraic Neural Networks: Stability to Deformations
In this work we study the stability of algebraic neural networks (AlgNNs...
read it

Graph Neural Networks: Architectures, Stability and Transferability
Graph Neural Networks (GNNs) are information processing architectures fo...
read it

Resource Allocation via ModelFree Deep Learning in Free Space Optical Networks
This paper investigates the general problem of resource allocation for m...
read it

Balancing Rates and Variance via Adaptive BatchSize for Stochastic Optimization Problems
Stochastic gradient descent is a canonical tool for addressing stochasti...
read it

Resource Allocation via Graph Neural Networks in Free Space Optical Fronthaul Networks
This paper investigates the optimal resource allocation in free space op...
read it

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

Wide and Deep Graph Neural Networks with Distributed Online Learning
Graph neural networks (GNNs) learn representations from network data wit...
read it

Graph Neural Networks for Motion Planning
This paper investigates the feasibility of using Graph Neural Networks (...
read it

Probably Approximately Correct Constrained Learning
As learning solutions reach critical applications in social, industrial,...
read it

Graphon Neural Networks and the Transferability of Graph Neural Networks
Graph neural networks (GNNs) rely on graph convolutions to extract local...
read it

Stochastic Graph Neural Networks
Graph neural networks (GNNs) model nonlinear representations in graph da...
read it

Graph Neural Networks for Decentralized Controllers
Dynamical systems comprised of autonomous agents arise in many relevant ...
read it

Graphs, Convolutions, and Neural Networks
Network data can be conveniently modeled as a graph signal, where data v...
read it

Graphon Pooling in Graph Neural Networks
Graph neural networks (GNNs) have been used effectively in different app...
read it

Wireless Power Control via Counterfactual Optimization of Graph Neural Networks
We consider the problem of downlink power control in wireless networks, ...
read it

The empirical duality gap of constrained statistical learning
This paper is concerned with the study of constrained statistical learni...
read it

Mobile Wireless Network Infrastructure on Demand
In this work, we introduce Mobile Wireless Infrastructure on Demand: a f...
read it

VGAI: A VisionBased Decentralized Controller Learning Framework for Robot Swarms
Despite the popularity of decentralized controller learning, very few su...
read it

Gated Graph Recurrent Neural Networks
Graph processes exhibit a temporal structure determined by the sequence ...
read it

EdgeNets:Edge Varying Graph Neural Networks
Driven by the outstanding performance of neural networks in the structur...
read it

Graph Neural Networks for Decentralized MultiRobot Path Planning
Efficient and collisionfree navigation in multirobot systems is fundam...
read it

RiskAware MMSE Estimation
Despite the simplicity and intuitive interpretation of Minimum Mean Squa...
read it

Safe Policies for Reinforcement Learning via PrimalDual Methods
In this paper, we study the learning of safe policies in the setting of ...
read it

ModelFree Learning of Optimal Ergodic Policies in Wireless Systems
Learning optimal resource allocation policies in wireless systems can be...
read it

Scheduling Low Latency Traffic for Wireless Control Systems in 5G Networks
We consider the problem of allocating 5G radio resources over wireless c...
read it

Constrained Reinforcement Learning Has Zero Duality Gap
Autonomous agents must often deal with conflicting requirements, such as...
read it

Stability of Graph Neural Networks to Relative Perturbations
Graph neural networks (GNNs), consisting of a cascade of layers applying...
read it

On the Sample Complexity of ActorCritic Method for Reinforcement Learning with Function Approximation
Reinforcement learning, mathematically described by Markov Decision Prob...
read it

Graph Policy Gradients for Large Scale Unlabeled Motion Planning with Constraints
In this paper, we present a learning method to solve the unlabelled moti...
read it

Source Seeking in Unknown Environments with Convex Obstacles
Navigation tasks often cannot be defined in terms of a target, either be...
read it

Learning Safe Unlabeled MultiRobot Planning with Motion Constraints
In this paper, we present a learning approach to goal assignment and tra...
read it

Graph Policy Gradients for Large Scale Robot Control
In this paper, we consider the problem of learning policies to control a...
read it

Optimal WDM Power Allocation via Deep Learning for Radio on Free Space Optics Systems
Radio on Free Space Optics (RoFSO), as a universal platform for heteroge...
read it

Stability of Graph Scattering Transforms
Scattering transforms are nontrainable deep convolutional architectures...
read it

Stability Properties of Graph Neural Networks
Data stemming from networks exhibit an irregular support, whereby each d...
read it

Sparse multiresolution representations with adaptive kernels
Reproducing kernel Hilbert spaces (RKHSs) are key elements of many nonp...
read it

InvariancePreserving Localized Activation Functions for Graph Neural Networks
Graph signals are signals with an irregular structure that can be descri...
read it

Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks
We consider the problem of finding distributed controllers for large net...
read it

Inverse Optimal Planning for Air Traffic Control
We envision a system that concisely describes the rules of air traffic c...
read it

Gated Graph Convolutional Recurrent Neural Networks
Graph processes model a number of important problems such as identifying...
read it

A Stochastic Trust Region Method for Nonconvex Minimization
We target the problem of finding a local minimum in nonconvex finitesu...
read it

Generalizing Graph Convolutional Neural Networks with EdgeVariant Recursions on Graphs
This paper reviews graph convolutional neural networks (GCNNs) through t...
read it

Learning Task Agnostic Sufficiently Accurate Models
For complex realworld systems, designing controllers are a difficult ta...
read it

Functional Nonlinear Sparse Models
Signal processing in inherently continuous and often nonlinear applicati...
read it

Median activation functions for graph neural networks
Graph neural networks (GNNs) have been shown to replicate convolutional ...
read it

Efficient Distributed Hessian Free Algorithm for Largescale Empirical Risk Minimization via Accumulating Sample Strategy
In this paper, we propose a Distributed Accumulated Newton Conjugate gra...
read it

Learning Optimal Resource Allocations in Wireless Systems
This paper considers the design of optimal resource allocation policies ...
read it

Diffusion Scattering Transforms on Graphs
Stability is a key aspect of data analysis. In many applications, the na...
read it

Scalable Centralized Deep MultiAgent Reinforcement Learning via Policy Gradients
In this paper, we explore using deep reinforcement learning for problems...
read it

Convolutional Neural Networks Architectures for Signals Supported on Graphs
We describe two architectures that generalize convolutional neural netwo...
read it
Alejandro Ribeiro
is this you? claim profile
Associate Professor Electrical and Systems Engineering (ESE) at University of Pennsylvania