Luis Carlos Padierna

TL;DR: A novel performance index to guide the optimization process, that improves the generalization of the solutions while maintaining their effectiveness, is presented.

TL;DR: A novel formulation of orthogonal polynomial kernels that includes and improves previous proposals (Legendre, Chebyshev and Hermite) is presented and shows that the Gegenbauer family competes with the RBF kernel in accuracy while requiring fewer support vectors and overcomes other classical and Orthogonal kernels.

TL;DR: In this article, the main areas within Condensed Matter Physics (CMP) which have successfully applied ML techniques to further research, such as the description and use of ML schemes for potential energy surfaces, the characterization of topological phases of matter in lattice systems, the prediction of phase transitions in off-lattice and atomistic simulations, the interpretation of ML theories with physics-inspired frameworks and the enhancement of simulation methods with ML algorithms.

TL;DR: This chapter analyzes two Estimation Distribution Algorithms, the Univariate Marginal Distribution Algorithm and the Boltzmann Univariatemarginal distribution Algorithm, to verify if these algorithms preserve the effectiveness of Random Search and at the same time make more efficient the process of finding the optimal hyper-parameters without increasing the complexity of Random search.

TL;DR: This review aims to explore the main areas within CMP, which have successfully applied ML techniques to further research, such as the description and use of ML schemes for potential energy surfaces, the characterization of topological phases of matter in lattice systems, the prediction of phase transitions in off-lattice and atomistic simulations, and the interpretation of ML theories with physics-inspired frameworks.