Alejandro Ribeiro

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University of Pennsylvania ∙ Intel

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

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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Inverse Optimal Planning for Air Traffic Control

We envision a system that concisely describes the rules of air traffic c...
03/25/2019 ∙ by Ekaterina Tolstaya, et al. ∙ 0 ∙ share

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Gated Graph Convolutional Recurrent Neural Networks

Graph processes model a number of important problems such as identifying...
03/05/2019 ∙ by Luana Ruiz, et al. ∙ 0 ∙ share

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A Stochastic Trust Region Method for Non-convex Minimization

We target the problem of finding a local minimum in non-convex finite-su...
03/04/2019 ∙ by Zebang Shen, et al. ∙ 0 ∙ share

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Generalizing Graph Convolutional Neural Networks with Edge-Variant Recursions on Graphs

This paper reviews graph convolutional neural networks (GCNNs) through t...
03/04/2019 ∙ by Elvin Isufi, et al. ∙ 0 ∙ share

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Learning Task Agnostic Sufficiently Accurate Models

For complex real-world systems, designing controllers are a difficult ta...
02/19/2019 ∙ by Clark Zhang, et al. ∙ 10 ∙ share

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Functional Nonlinear Sparse Models

Signal processing in inherently continuous and often nonlinear applicati...
11/01/2018 ∙ by Luiz F. O. Chamon, et al. ∙ 0 ∙ share

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Median activation functions for graph neural networks

Graph neural networks (GNNs) have been shown to replicate convolutional ...
10/29/2018 ∙ by Luana Ruiz, et al. ∙ 0 ∙ share

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Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy

In this paper, we propose a Distributed Accumulated Newton Conjugate gra...
10/26/2018 ∙ by Majid Jahani, et al. ∙ 0 ∙ share

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Learning Optimal Resource Allocations in Wireless Systems

This paper considers the design of optimal resource allocation policies ...
07/21/2018 ∙ by Mark Eisen, et al. ∙ 0 ∙ share

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Diffusion Scattering Transforms on Graphs

Stability is a key aspect of data analysis. In many applications, the na...
06/22/2018 ∙ by Fernando Gama, et al. ∙ 0 ∙ share

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Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients

In this paper, we explore using deep reinforcement learning for problems...
05/22/2018 ∙ by Arbaaz Khan, et al. ∙ 0 ∙ share

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Convolutional Neural Networks Architectures for Signals Supported on Graphs

We describe two architectures that generalize convolutional neural netwo...
05/01/2018 ∙ by Fernando Gama, et al. ∙ 0 ∙ share

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Alejandro Ribeiro

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