Alejandro Ribeiro
9 papers in the PaperMetrix corpus
Papers by this author
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Graphon Pooling in Graph Neural Networks
2020
Graph neural networks (GNNs) have been used effectively in different applications involving the processing of signals on irregular structures modeled by graphs. Relying on the use of shift-invariant graph filters, GNNs extend the operation of …
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Stochastic Graph Neural Networks
2020
Graph neural networks (GNNs) model nonlinear representations in graph data with applications in distributed agent coordination, control, and planning among others. However, current GNN implementations assume ideal distributed scenarios and ignore link fluctuations that occur …
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Stability of Manifold Neural Networks to Deformations.
2021 · arXiv (Cornell University)
Stability is an important property of graph neural networks (GNNs) which explains their success in many problems of practical interest. Existing GNN stability results depend on the size of the graph, restricting applicability to graphs …
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Actor-only Deterministic Policy Gradient via Zeroth-order Gradient Oracles in Action Space
2021
Deterministic policies demonstrate substantial empirical success over their stochastic counterparts as they remove a level of randomness in Policy Gradient (PG) methods when applied to stochastic search problems involving Markov decision processes. However, current implementations …
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Transferability Properties of Graph Neural Networks
2021 · arXiv (Cornell University)
Graph neural networks (GNNs) are composed of layers consisting of graph convolutions and pointwise nonlinearities. Due to their invariance and stability properties, GNNs are provably successful at learning representations from data supported on moderate-scale graphs. …
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On the Sample Complexity of Actor-Critic Method for Reinforcement Learning with Function Approximation
2019 · arXiv (Cornell University)
Reinforcement learning, mathematically described by Markov Decision Problems, may be approached either through dynamic programming or policy search. Actor-critic algorithms combine the merits of both approaches by alternating between steps to estimate the value function …
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Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs
2023 · arXiv (Cornell University)
We study the problem of computing an optimal policy of an infinite-horizon discounted constrained Markov decision process (constrained MDP). Despite the popularity of Lagrangian-based policy search methods used in practice, the oscillation of policy iterates …
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Multi-Target Tracking with Transferable Convolutional Neural Networks
2023
Multi-target tracking (MTT) is a classical signal processing task, where the goal is to estimate the states of an unknown number of moving targets from noisy sensor measurements. In this paper, we revisit MTT from …
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Unrolled Graph Neural Networks for Constrained Optimization
2026
In this paper, we unroll the dynamics of the dual ascent (DA) algorithm in two coupled graph neural networks (GNNs) to solve constrained optimization problems. The two networks interact with each other at the layer …