Alejandro Ribeiro

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University of Pennsylvania ∙ Intel

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

Wide and Deep Graph Neural Network with Distributed Online Learning

Graph neural networks (GNNs) are naturally distributed architectures for...
07/19/2021 ∙ by Zhan Gao, et al. ∙ 0 ∙ share

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

In this paper, we present a perception-action-communication loop design ...
06/24/2021 ∙ by Ting-Kuei Hu, et al. ∙ 0 ∙ share

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Stability of Graph Convolutional Neural Networks to Stochastic Perturbations

Graph convolutional neural networks (GCNNs) are nonlinear processing too...
06/19/2021 ∙ by Zhan Gao, et al. ∙ 0 ∙ share

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Stability of Manifold Neural Networks to Deformations

Stability is an important property of graph neural networks (GNNs) which...
06/07/2021 ∙ by Zhiyang Wang, et al. ∙ 0 ∙ share

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Increase and Conquer: Training Graph Neural Networks on Growing Graphs

Graph neural networks (GNNs) use graph convolutions to exploit network i...
06/07/2021 ∙ by Juan Cervino, et al. ∙ 0 ∙ share

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Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping

Graph neural networks (GNNs) are processing architectures that exploit g...
06/05/2021 ∙ by Zhan Gao, et al. ∙ 0 ∙ share

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Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection

In this paper, we develop a learning-based approach for decentralized su...
05/18/2021 ∙ by Lifeng Zhou, et al. ∙ 8 ∙ share

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Composable Learning with Sparse Kernel Representations

We present a reinforcement learning algorithm for learning sparse non-pa...
03/26/2021 ∙ by Ekaterina Tolstaya, et al. ∙ 0 ∙ share

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Constrained Learning with Non-Convex Losses

Though learning has become a core technology of modern information proce...
03/08/2021 ∙ by Luiz F. O. Chamon, et al. ∙ 0 ∙ share

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Learning Connectivity for Data Distribution in Robot Teams

Many algorithms for control of multi-robot teams operate under the assum...
03/08/2021 ∙ by Ekaterina Tolstaya, et al. ∙ 4 ∙ share

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

Constrained reinforcement learning involves multiple rewards that must i...
02/23/2021 ∙ by Miguel Calvo-Fullana, et al. ∙ 0 ∙ share

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Large Scale Distributed Collaborative Unlabeled Motion Planning with Graph Policy Gradients

In this paper, we present a learning method to solve the unlabelled moti...
02/11/2021 ∙ by Arbaaz Khan, et al. ∙ 5 ∙ share

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Sufficiently Accurate Model Learning for Planning

Data driven models of dynamical systems help planners and controllers to...
02/11/2021 ∙ by Clark Zhang, et al. ∙ 0 ∙ share

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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...
01/25/2021 ∙ by Miguel Calvo-Fullana, et al. ∙ 0 ∙ share

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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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Alejandro Ribeiro

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