Alejandro Ribeiro

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University of Pennsylvania ∙ Intel

Associate Professor Electrical and Systems Engineering (ESE) at University of Pennsylvania

Decentralized Control with Graph Neural Networks

Dynamical systems consisting of a set of autonomous agents face the chal...
12/29/2020 ∙ by Fernando Gama, et al. ∙ 16 ∙ share

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Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning

Prediction credibility measures, in the form of confidence intervals or ...
11/24/2020 ∙ by Luiz F. O. Chamon, et al. ∙ 0 ∙ share

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

We consider optimal resource allocation problems under asynchronous wire...
11/05/2020 ∙ by Zhiyang Wang, et al. ∙ 0 ∙ share

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Multi-Robot Coverage and Exploration using Spatial Graph Neural Networks

The multi-robot coverage problem is an essential building block for syst...
11/02/2020 ∙ by Ekaterina Tolstaya, et al. ∙ 0 ∙ share

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Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks

Graph convolutional neural networks (GCNNs) learn compositional represen...
10/27/2020 ∙ by Luana Ruiz, et al. ∙ 0 ∙ share

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Multi-task Supervised Learning via Cross-learning

In this paper we consider a problem known as multi-task learning, consis...
10/24/2020 ∙ by Juan Cervino, et al. ∙ 0 ∙ share

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Graph and graphon neural network stability

Graph neural networks (GNNs) are learning architectures that rely on kno...
10/23/2020 ∙ by Luana Ruiz, et al. ∙ 0 ∙ share

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Stability of Algebraic Neural Networks to Small Perturbations

Algebraic neural networks (AlgNNs) are composed of a cascade of layers e...
10/22/2020 ∙ by Alejandro Parada-Mayorga, et al. ∙ 0 ∙ share

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Quiver Signal Processing (QSP)

In this paper we state the basics for a signal processing framework on q...
10/22/2020 ∙ by Alejandro Parada-Mayorga, et al. ∙ 0 ∙ share

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Discriminability of Single-Layer Graph Neural Networks

Network data can be conveniently modeled as a graph signal, where data v...
10/17/2020 ∙ by Samuel Pfrommer, et al. ∙ 0 ∙ share

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

Reinforcement learning considers the problem of finding policies that ma...
10/16/2020 ∙ by Santiago Paternain, et al. ∙ 0 ∙ share

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

Spherical signals are useful mathematical models for data arising in man...
10/12/2020 ∙ by Zhan Gao, et al. ∙ 0 ∙ share

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Algebraic Neural Networks: Stability to Deformations

In this work we study the stability of algebraic neural networks (AlgNNs...
09/03/2020 ∙ by Alejandro Parada-Mayorga, et al. ∙ 0 ∙ share

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Graph Neural Networks: Architectures, Stability and Transferability

Graph Neural Networks (GNNs) are information processing architectures fo...
08/04/2020 ∙ by Luana Ruiz, et al. ∙ 0 ∙ share

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

This paper investigates the general problem of resource allocation for m...
07/27/2020 ∙ by Zhan Gao, et al. ∙ 0 ∙ share

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

Stochastic gradient descent is a canonical tool for addressing stochasti...
07/02/2020 ∙ by Zhan Gao, et al. ∙ 0 ∙ share

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

This paper investigates the optimal resource allocation in free space op...
06/26/2020 ∙ by Zhan Gao, et al. ∙ 0 ∙ share

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Zeroth-order Deterministic Policy Gradient

Deterministic Policy Gradient (DPG) removes a level of randomness from s...
06/12/2020 ∙ by Harshat Kumar, et al. ∙ 1 ∙ share

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Wide and Deep Graph Neural Networks with Distributed Online Learning

Graph neural networks (GNNs) learn representations from network data wit...
06/11/2020 ∙ by Zhan Gao, et al. ∙ 0 ∙ share

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Graph Neural Networks for Motion Planning

This paper investigates the feasibility of using Graph Neural Networks (...
06/11/2020 ∙ by Arbaaz Khan, et al. ∙ 6 ∙ share

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Probably Approximately Correct Constrained Learning

As learning solutions reach critical applications in social, industrial,...
06/09/2020 ∙ by Luiz F. O. Chamon, et al. ∙ 0 ∙ share

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Graphon Neural Networks and the Transferability of Graph Neural Networks

Graph neural networks (GNNs) rely on graph convolutions to extract local...
06/05/2020 ∙ by Luana Ruiz, et al. ∙ 0 ∙ share

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Stochastic Graph Neural Networks

Graph neural networks (GNNs) model nonlinear representations in graph da...
06/04/2020 ∙ by Zhan Gao, et al. ∙ 0 ∙ share

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Graph Neural Networks for Decentralized Controllers

Dynamical systems comprised of autonomous agents arise in many relevant ...
03/23/2020 ∙ by Fernando Gama, et al. ∙ 0 ∙ share

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Graphs, Convolutions, and Neural Networks

Network data can be conveniently modeled as a graph signal, where data v...
03/08/2020 ∙ by Fernando Gama, et al. ∙ 0 ∙ share

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Graphon Pooling in Graph Neural Networks

Graph neural networks (GNNs) have been used effectively in different app...
03/03/2020 ∙ by Alejandro Parada-Mayorga, et al. ∙ 0 ∙ share

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Wireless Power Control via Counterfactual Optimization of Graph Neural Networks

We consider the problem of downlink power control in wireless networks, ...
02/17/2020 ∙ by Navid NaderiAlizadeh, et al. ∙ 0 ∙ share

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The empirical duality gap of constrained statistical learning

This paper is concerned with the study of constrained statistical learni...
02/12/2020 ∙ by Luiz F. O. Chamon, et al. ∙ 0 ∙ share

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Mobile Wireless Network Infrastructure on Demand

In this work, we introduce Mobile Wireless Infrastructure on Demand: a f...
02/07/2020 ∙ by Daniel Mox, et al. ∙ 0 ∙ share

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VGAI: A Vision-Based Decentralized Controller Learning Framework for Robot Swarms

Despite the popularity of decentralized controller learning, very few su...
02/06/2020 ∙ by Ting-Kuei Hu, et al. ∙ 0 ∙ share

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Gated Graph Recurrent Neural Networks

Graph processes exhibit a temporal structure determined by the sequence ...
02/03/2020 ∙ by Luana Ruiz, et al. ∙ 0 ∙ share

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EdgeNets:Edge Varying Graph Neural Networks

Driven by the outstanding performance of neural networks in the structur...
01/21/2020 ∙ by Elvin Isufi, et al. ∙ 29 ∙ share

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Graph Neural Networks for Decentralized Multi-Robot Path Planning

Efficient and collision-free navigation in multi-robot systems is fundam...
12/12/2019 ∙ by Qingbiao Li, et al. ∙ 0 ∙ share

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Risk-Aware MMSE Estimation

Despite the simplicity and intuitive interpretation of Minimum Mean Squa...
12/06/2019 ∙ by Dionysios S. Kalogerias, et al. ∙ 14 ∙ share

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Safe Policies for Reinforcement Learning via Primal-Dual Methods

In this paper, we study the learning of safe policies in the setting of ...
11/20/2019 ∙ by Santiago Paternain, et al. ∙ 0 ∙ share

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Model-Free Learning of Optimal Ergodic Policies in Wireless Systems

Learning optimal resource allocation policies in wireless systems can be...
11/10/2019 ∙ by Dionysios S. Kalogerias, et al. ∙ 15 ∙ share

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Scheduling Low Latency Traffic for Wireless Control Systems in 5G Networks

We consider the problem of allocating 5G radio resources over wireless c...
10/30/2019 ∙ by Mark Eisen, et al. ∙ 0 ∙ share

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Constrained Reinforcement Learning Has Zero Duality Gap

Autonomous agents must often deal with conflicting requirements, such as...
10/29/2019 ∙ by Santiago Paternain, et al. ∙ 0 ∙ share

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Stability of Graph Neural Networks to Relative Perturbations

Graph neural networks (GNNs), consisting of a cascade of layers applying...
10/21/2019 ∙ by Fernando Gama, et al. ∙ 24 ∙ share

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On the Sample Complexity of Actor-Critic Method for Reinforcement Learning with Function Approximation

Reinforcement learning, mathematically described by Markov Decision Prob...
10/18/2019 ∙ by Harshat Kumar, et al. ∙ 0 ∙ share

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Graph Policy Gradients for Large Scale Unlabeled Motion Planning with Constraints

In this paper, we present a learning method to solve the unlabelled moti...
09/24/2019 ∙ by Arbaaz Khan, et al. ∙ 0 ∙ share

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Source Seeking in Unknown Environments with Convex Obstacles

Navigation tasks often cannot be defined in terms of a target, either be...
09/16/2019 ∙ by Bruno A. Angelico, et al. ∙ 0 ∙ share

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Learning Safe Unlabeled Multi-Robot Planning with Motion Constraints

In this paper, we present a learning approach to goal assignment and tra...
07/11/2019 ∙ by Arbaaz Khan, et al. ∙ 0 ∙ share

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Graph Policy Gradients for Large Scale Robot Control

In this paper, we consider the problem of learning policies to control a...
07/08/2019 ∙ by Arbaaz Khan, et al. ∙ 0 ∙ share

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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...
06/21/2019 ∙ by Zhan Gao, et al. ∙ 0 ∙ share

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Stability of Graph Scattering Transforms

Scattering transforms are non-trainable deep convolutional architectures...
06/11/2019 ∙ by Fernando Gama, et al. ∙ 4 ∙ share

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Stability Properties of Graph Neural Networks

Data stemming from networks exhibit an irregular support, whereby each d...
05/11/2019 ∙ by Fernando Gama, et al. ∙ 22 ∙ share

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Sparse multiresolution representations with adaptive kernels

Reproducing kernel Hilbert spaces (RKHSs) are key elements of many non-p...
05/07/2019 ∙ by Maria Peifer, et al. ∙ 0 ∙ share

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Invariance-Preserving Localized Activation Functions for Graph Neural Networks

Graph signals are signals with an irregular structure that can be descri...
03/29/2019 ∙ by Luana Ruiz, et al. ∙ 0 ∙ share

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Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks

We consider the problem of finding distributed controllers for large net...
03/25/2019 ∙ by Ekaterina Tolstaya, et al. ∙ 0 ∙ share

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Alejandro Ribeiro

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