
Composable Learning with Sparse Kernel Representations
We present a reinforcement learning algorithm for learning sparse nonpa...
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Constrained Learning with NonConvex Losses
Though learning has become a core technology of modern information proce...
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Learning Connectivity for Data Distribution in Robot Teams
Many algorithms for control of multirobot teams operate under the assum...
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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...
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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...
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Sufficiently Accurate Model Learning for Planning
Data driven models of dynamical systems help planners and controllers to...
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ROSNetSim: A Framework for the Integration of Robotic and Network Simulators
Multiagent systems play an important role in modern robotics. Due to th...
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Decentralized Control with Graph Neural Networks
Dynamical systems consisting of a set of autonomous agents face the chal...
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Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning
Prediction credibility measures, in the form of confidence intervals or ...
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Unsupervised Learning for Asynchronous Resource Allocation in Adhoc Wireless Networks
We consider optimal resource allocation problems under asynchronous wire...
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MultiRobot Coverage and Exploration using Spatial Graph Neural Networks
The multirobot coverage problem is an essential building block for syst...
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Nonlinear StateSpace Generalizations of Graph Convolutional Neural Networks
Graph convolutional neural networks (GCNNs) learn compositional represen...
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Multitask Supervised Learning via Crosslearning
In this paper we consider a problem known as multitask learning, consis...
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Graph and graphon neural network stability
Graph neural networks (GNNs) are learning architectures that rely on kno...
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Stability of Algebraic Neural Networks to Small Perturbations
Algebraic neural networks (AlgNNs) are composed of a cascade of layers e...
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Quiver Signal Processing (QSP)
In this paper we state the basics for a signal processing framework on q...
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Discriminability of SingleLayer Graph Neural Networks
Network data can be conveniently modeled as a graph signal, where data v...
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Policy Gradient for Continuing Tasks in Nonstationary Markov Decision Processes
Reinforcement learning considers the problem of finding policies that ma...
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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...
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Algebraic Neural Networks: Stability to Deformations
In this work we study the stability of algebraic neural networks (AlgNNs...
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Graph Neural Networks: Architectures, Stability and Transferability
Graph Neural Networks (GNNs) are information processing architectures fo...
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Resource Allocation via ModelFree Deep Learning in Free Space Optical Networks
This paper investigates the general problem of resource allocation for m...
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Balancing Rates and Variance via Adaptive BatchSize for Stochastic Optimization Problems
Stochastic gradient descent is a canonical tool for addressing stochasti...
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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...
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Zerothorder Deterministic Policy Gradient
Deterministic Policy Gradient (DPG) removes a level of randomness from s...
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Wide and Deep Graph Neural Networks with Distributed Online Learning
Graph neural networks (GNNs) learn representations from network data wit...
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Graph Neural Networks for Motion Planning
This paper investigates the feasibility of using Graph Neural Networks (...
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Probably Approximately Correct Constrained Learning
As learning solutions reach critical applications in social, industrial,...
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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...
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Stochastic Graph Neural Networks
Graph neural networks (GNNs) model nonlinear representations in graph da...
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Graph Neural Networks for Decentralized Controllers
Dynamical systems comprised of autonomous agents arise in many relevant ...
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Graphs, Convolutions, and Neural Networks
Network data can be conveniently modeled as a graph signal, where data v...
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Graphon Pooling in Graph Neural Networks
Graph neural networks (GNNs) have been used effectively in different app...
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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, ...
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The empirical duality gap of constrained statistical learning
This paper is concerned with the study of constrained statistical learni...
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Mobile Wireless Network Infrastructure on Demand
In this work, we introduce Mobile Wireless Infrastructure on Demand: a f...
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VGAI: A VisionBased Decentralized Controller Learning Framework for Robot Swarms
Despite the popularity of decentralized controller learning, very few su...
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Gated Graph Recurrent Neural Networks
Graph processes exhibit a temporal structure determined by the sequence ...
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EdgeNets:Edge Varying Graph Neural Networks
Driven by the outstanding performance of neural networks in the structur...
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Graph Neural Networks for Decentralized MultiRobot Path Planning
Efficient and collisionfree navigation in multirobot systems is fundam...
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RiskAware MMSE Estimation
Despite the simplicity and intuitive interpretation of Minimum Mean Squa...
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Safe Policies for Reinforcement Learning via PrimalDual Methods
In this paper, we study the learning of safe policies in the setting of ...
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ModelFree Learning of Optimal Ergodic Policies in Wireless Systems
Learning optimal resource allocation policies in wireless systems can be...
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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...
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Constrained Reinforcement Learning Has Zero Duality Gap
Autonomous agents must often deal with conflicting requirements, such as...
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Stability of Graph Neural Networks to Relative Perturbations
Graph neural networks (GNNs), consisting of a cascade of layers applying...
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On the Sample Complexity of ActorCritic Method for Reinforcement Learning with Function Approximation
Reinforcement learning, mathematically described by Markov Decision Prob...
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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...
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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...
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Learning Safe Unlabeled MultiRobot Planning with Motion Constraints
In this paper, we present a learning approach to goal assignment and tra...
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Alejandro Ribeiro
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Associate Professor Electrical and Systems Engineering (ESE) at University of Pennsylvania