Recommended citation: Loyola, J. M., Errecalde, M. L., Escalante, H. J., & y Gomez, M. M. (2017, October). Learning When to Classify for Early Text Classification. In Argentine Congress of Computer Science (pp. 24-34). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-319-75214-3_3

Classification of RNA backbone conformation into rotamers using ¹³C′ chemical shifts How far we can go?

Published in PeerJ, 2019

Recommended citation: Icazatti, A. A., Loyola, J. M., Szleifer, I., Vila, J. A., & Martin, O. A. (2019). Classification of RNA backbone conformation into rotamers using ¹³C′ chemical shifts: exploring how far we can go. PeerJ 7:e7904 https://doi.org/10.7717/peerj.7904 https://peerj.com/articles/7904

UNSL at eRisk 2021: A Comparison of Three Early Alert Policies for Early Risk Detection

Published in Working Notes of CLEF, 2021

Recommended citation: Loyola, J. M., Burdisso, S., Thompson, H., Cagnina, L., & Errecalde, M. (2021). UNSL at eRisk 2021: A Comparison of Three Early Alert Policies for Early Risk Detection. Working Notes of CLEF, 21-24. http://ceur-ws.org/Vol-2936/paper-81.pdf

Solubility of meloxicam in ethylene glycol-water and propylene glycol-ethanol mixtures: Experimental determination and thermodynamic analysis

Published in Journal of Molecular Liquids, 2022

Recommended citation: Castro, G. T., Loyola, J. M., Gasull, E. I., & Almandoz, M. C. (2022). Solubility of meloxicam in ethylene glycol-water and propylene glycol-ethanol mixtures: Experimental determination and thermodynamic analysis. Journal of Molecular Liquids, 354, 118863. https://doi.org/10.1016/j.molliq.2022.118863

UNSL at eRisk 2022: Decision policies with history for early classification

Published in Working Notes of CLEF, 2022

Recommended citation: Loyola, J. M., Thompson, H., Burdisso, S. & Errecalde, M. (2022). UNSL at eRisk 2022: Decision policies with history for early classification. Working Notes of CLEF, 947-960. https://ceur-ws.org/Vol-3180/paper-75.pdf

Bayesian additive regression trees for probabilistic programming

Published in arXiv, 2022

Recommended citation: Quiroga, M., Garay, P. G., Alonso, J. M., Loyola, J. M., & Martin, O. A. (2022). Bayesian additive regression trees for probabilistic programming. arXiv preprint arXiv:2206.03619. https://arxiv.org/pdf/2206.03619.pdf