RECENT
PUBLICATIONS
The results of our research are submitted to highly reputed, peer-reviewed journals in different domains: Statistics, Physics, multidisciplinary, etc, or to top international (also peer-reviewed) conferences about Statistics and Machine Learning.
While we prioritize open-access journals, all these works are freely available at the arXiv repository, containing the latest published version of the papers.
Sparse Implicit Processes for Approximate Inference
Simon Rodriguez Santana, Bryan Zaldivar, Daniel Hernández-Lobato. Submitted to ICML 2022 Conference. https://arxiv.org/pdf/2110.07618.pdf
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Multiclass Gaussian Process Classification with Noisy Inputs
Carlos Villacampa-Calvo, Bryan Zaldivar, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato. Published at Journal of Machine Learning...
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Dark matter constraints from dwarf galaxies with data-driven J-factors
Alvarez, F. Calore, A. Genina, J. Read, P. D. Serpico and B. Zaldivar. Published at...
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A Deep Generative Artificial Intelligence system to decipher species coexistence patterns
Johannes Hirn et al. https://arxiv.org/abs/2107.06020 https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.13827
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Symmetry meets AI
Gabriela Barenboim, Johannes Hirn y Veronica Sanz. https://www.scipost.org/SciPostPhys.11.1.014
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Exploring the political pulse of a country using Data Science tools
Miguel G. Folgado y Veronica Sanz. Publicado en Journal of Computational Social Sciences. https://link.springer.com/article/10.1007/s42001-021-00157-1
Read MoreATLAS Collaboration (Aad et al.) 2021
Search for dark matter produced in association with a single top quark in sqrt{s}=13 TeV...
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