5 papers
RKHS Representation of Algebraic Convolutional Filters with Integral Operators
Alejandro Parada-Mayorga, Alejandro Ribeiro, Juan Bazerque
Integral operators play a central role in signal processing, underpinning classical convolution, and filtering on continuous network models such as graphons. While these operators…
Cross-Learning from Scarce Data via Multi-Task Constrained Optimization
Leopoldo Agorio, Juan Cerviño, Miguel Calvo-Fullana +2
A learning task, understood as the problem of fitting a parametric model from supervised data, fundamentally requires the dataset to be large enough to be representative of the und…
Convolutional Filtering with RKHS Algebras
Alejandro Parada-Mayorga, Leopoldo Agorio, Alejandro Ribeiro +1
In this paper, we develop a generalized theory of convolutional signal processing and neural networks for Reproducing Kernel Hilbert Spaces (RKHS). Leveraging the theory of algebra…
Cooperative Multi-Agent Assignment over Stochastic Graphs via Constrained Reinforcement Learning
Leopoldo Agorio, Sean Van Alen, Santiago Paternain +2
Constrained multi-agent reinforcement learning offers the framework to design scalable and almost surely feasible solutions for teams of agents operating in dynamic environments to…
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…