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cs.LG2026
A Contraction Framework for Stochastic Operators with Bootstrapping: Application to TD Learning
Ids van der Werf, Sergio Rozada, Antonio G. Marques
Many iterative algorithms rely on bootstrapping. A variable is updated using a second, frozen copy as a target, which is periodically replaced with the updated variable. Majorize-m…
cs.LG2026
Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion
Shervin Khalafi, Igor Krawczuk, Sergio Rozada +3
Denoising graphs is a fundamental problem in graph learning and the core operation of graph diffusion models. Attention-based architectures such as graph transformers have shown pr…