3 papers
cs.LG2026
Data-Driven Energy-Based Learning via Gibbs Measures on Hierarchical Structures
L. U. Abdullaev, F. Herrera, U. A. Rozikov +1
We introduce a data-driven probabilistic framework for learning systems based on Gibbs measures on hierarchical structures. Unlike standard empirical risk minimization, where a dat…
cs.LG2026
Featured Reproducing Kernel Banach Spaces for Learning and Neural Networks
Isabel de la Higuera, Francisco Herrera, M. Victoria Velasco
Reproducing kernel Hilbert spaces provide a foundational framework for kernel-based learning, where regularization and interpolation problems admit finite-dimensional solutions thr…
cs.LG2025
Ising Models with Hidden Markov Structure: Applications to Probabilistic Inference in Machine Learning
F. Herrera, U. A. Rozikov, M. V. Velasco
In this paper, we investigate tree-indexed Markov chains (Gibbs measures) defined by a Hamiltonian that couples two Ising layers: hidden spins \(s(x) \in \{\pm 1\}\) and observed s…