4 citations · 4 across the 15 of their papers we have counts for
6 papers · 1 filter
Graph-Aware Diffusion for Signal Generation
Sergio Rozada, Vimal K. B., Andrea Cavallo +3
We study the problem of generating graph signals from unknown distributions defined over given graphs, relevant to domains such as recommender systems or sensor networks. Our appro…
Graph Signal Generative Diffusion Models
Yigit Berkay Uslu, Samar Hadou, Sergio Rozada +2
We introduce U-shaped encoder-decoder graph neural networks (U-GNNs) for stochastic graph signal generation using denoising diffusion processes. The architecture learns node featur…
Unrolling Dynamic Programming via Graph Filters
Sergio Rozada, Samuel Rey, Gonzalo Mateos +1
Dynamic programming (DP) is a fundamental tool used across many engineering fields. The main goal of DP is to solve Bellman's optimality equations for a given Markov decision proce…
A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning
Sergio Rozada, Santiago Paternain, Juan Andres Bazerque +1
In pursuit of reinforcement learning systems that could train in physical environments, we investigate multi-task approaches as a means to alleviate the need for massive data acqui…
Multilinear Tensor Low-Rank Approximation for Policy-Gradient Methods in Reinforcement Learning
Sergio Rozada, Hoi-To Wai, Antonio G. Marques
Reinforcement learning (RL) aims to estimate the action to take given a (time-varying) state, with the goal of maximizing a cumulative reward function. Predominantly, there are two…
Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation
Sergio Rozada, Jose Luis Orejuela, Antonio G. Marques
We study the problem of learning optimal policies in finite-horizon Markov Decision Processes (MDPs) using low-rank reinforcement learning (RL) methods. In finite-horizon MDPs, the…