Papers

Articles in Refereed Journals

  • Gerli, S., Cigna, T., Ascari, R., Migliorati, S., and Borrotti, M. (2024) Beyond human labelling: an automatic topic identification framework for big web data, Electronic Journal of Applied Statistical Analysis, accepted.

  • Zangirolami, V., and Borrotti, M. (2024). Dealing with uncertainty: balancing exploration and exploitation in deep recurrent reinforcement learning. Knowledge-Based Systems, Vol. 293, p. 1-11, 10.1016/j.knosys.2024.111663.

  • Borrotti, M. (2024). Quantifying Uncertainty with Conformal Prediction for Heating and Cooling Load Forecasting in Building Performance Simulation. Energies, Vol. 17, p. 1-13, 10.3390/en17174348.

  • Borrotti, M., Sambo, F., and Mylona, K. (2023). Multi-objective optimisation of split-plot designs. Econometrics and Statistics, Vol. 28, p. 163-172, 10.1016/j.ecosta.2022.04.001.

  • Borrotti, M., Rabasco, M., Santoro, A. (2023). Using accounting information to predict aggressive tax location decisions by European groups, Economic Systems, Vol. 43, 101090, 10.1016/j.ecosys.2023.101090.

  • Riboni, A., Ghioldi, N., Candelieri, A., and Borrotti, M. (2022) Bayesian optimization and deep learning for steering wheel angle prediction, Scientific Reports, Vol. 12, pp. 1–13, 10.1038/s41598-022-12509-6.

  • Lucarelli, G., and Borrotti, M. (2020) A deep Q-learning portfolio management framework for the cryptocurrency market, Neural Computing and Applications, Vol.32, pp. 17229—17244, 10.1007/s00521-020-05359-8.

  • Borrotti, M., Sambo, F., Mylona, K., and Gilmour, S. (2017) A multi-objective coordinate-exchange two-phase local search algorithm for multi-stratum experiments, Statistics and Computing, Vol. 27, pp. 469–481, 10.1007/s11222-016-9633-6.

  • Borrotti, M., Lanzarone, E., Manganini, F., Ortelli, S., Pievatolo, A., and Tonetti, C. (2017) Defect minimization and feature control in electrospinning through design of experiments, Journal of Applied Polymer, Vol. 134, 10.1002/app.44740.

  • Piccolomini, A. A., Fiabon, A., Borrotti, M., and De Lucrezia, D. (2017) Optimization of thermophilic trans-isoprenyl diphosphate synthase expression in Escherichia coly by response surface methodology, Biotechnology and Applied Biochemistry, Vol. 64, pp. 70-78, 10.1002/bab.1459.

  • Borrotti, M., Minervini, G., De Lucrezia, P., and Poli, I. (2016) Naïve Bayes ant colony optimization for designing high dimensional experiments, Applied Soft Computing, Vol. 49, pp. 459–468, 10.1016/j.asoc.2016.08.018.

  • Borrotti, M., Pievatolo, A., Degiorgi, A., Critelli, I., and Colledani, M. (2016) A computer-aided methodology for the optimization of electrostatic separation processes in recycling, Applied Stochastic Models in Business and Industry, Vol. 32, pp. 133–148, 10.1002/asmb.2128.

  • Coccon, F., Bossi, G., Borrotti, M., P. Torricelli, and P. Franzoi (2015) A land-use perspective for wildlife strike risk assessment at airports: the Attraction Risk Index, PlosOne, Vol. 10, 10.1371/journal.pone.0128363.

  • Zennaro, P., Kehrwald, N.M., McConnel, J.R., Schüpbach, S., Maselli, O., Marlon, J., Vallelonga, P., Leuenberger, D., Zangrando, R., Spolaor, M., Borrotti, M., Barbaro, E., Gambaro, A., and Barbante, C. (2014) Fire in ice: two millennia of northern hemisphere fire history from the Greenland NEEM ice core, Climate of the Past, Vol. 10, pp. 1905–1925, 10.5194/cpd-10-809-2014.

  • Borrotti, M., De March, M., Slanzi, D., Poli, I. (2014) Designing lead optimisation of MMP-12 Inhibitors, Computational and Mathematical Methods in Medicine, Vol. 2014, pp. 1–8, 10.1155/2014/258627.

  • Sambo, F., Borrotti, M., and Mylona, K. (2014) Coordinate-exchange two-phase local search: an optimal algorithm for D-efficient and I-efficient second-order split-plot designs, Computational Statistics and Data Analysis, Vol. 71, pp. 1193–1207, 10.1016/j.csda.2013.03.015.

  • Ferrari, D., Borrotti, M., and De March, D. (2014) Response improvement in complex experiments by co-information composite likelihood optimization, Statistics and Computing, Vol. 24, pp. 351–363, 10.1007/s11222-013-9374-8.

  • Zemella, G., De March, D., Borrotti, M., and Poli, I. (2011) Optimised design of energy efficient building façades via evolutionary neural networks, Energy and Buildings, vol. 43, pp. 3297– 3302, 10.1016/j.enbuild.2011.10.006.

  • Pizza, F., Contardi, S., Baldi Antognini, A., Zagoraiou, M., Borrotti, M., Mostacci, B., Mondini, and S., Cirignotta, F. (2010) Sleep quality and car accidents in adolescents, Journal of Clinical Sleep Medicine, Vol. 6, No. 1, 10.5664/jcsm.27708.

Books and Chapters in Books

  • Riboni, A., Candelieri, A., and Borrotti, M. (2022) Deep autonomous agents comparison for self-driving cars, In: G. Nicosia et al. (Eds.) Machine Learning, Optimization, and Data Science. LOD 2021. Lecture Notes in Computer Science, Vol. 13163, pp. 201–213, Springer-Verlag.

  • Lucarelli, G., and Borrotti, M. (2019) A deep reinforcement learning approach for automated cryptocurrency trading, In J. MacIntyre et al. (Eds.), Artificial Intelligence Applications and Innovations, Vol. 559, pp. 247–258, Springer-Verlag.

  • Borrotti, M., and Pievatolo, A. (2016) A multi-objective Bayesian sequential design based on Pareto optimality, Advances in Model-Oriented Design and Analysis, Contributions to Statistics, pp. 47-54, Springer-Verlag, Springer-Verlag.

  • De March, D., Borrotti, M., Sartore, L., Slanzi, D., Podestà, L., and Poli, I. (2015) A predictive approach based on neural network models for building automation systems, In S. Bassis et al. (Eds.), Advances in Neural Networks: Computational and Theoretical Issues, Smart Innovation Systems and Technology, Vol. 37, pp. 253-262, Springer-Verlag.

  • Slanzi, D., Borrotti, M., Orlando, D., De March, D.s, Giove, S., and Poli, I. (2014) Qualitative Particle Swarm Optimization (Q-PSO) for energy efficient building designs, In C. Pizzuti et al. (Eds.), Advances in Artificial Life and Evolutionary Computation, Communications in Computer and Information Science, Vol. 445, pp. 13–25, Springer-Verlag.

  • Ferrari, D., and Borrotti, M. (2013) Maximum entropy design in high dimensions by composite likelihood modeling, In D. Ucínski et al. (Eds.), Advances in Model-Oriented Design and Analysis, Contributions to Statistics, pp. 73–80, Springer-Verlag.

  • Borrotti, M., and Poli, I. (2013) Naïve Bayes ant colony optimization for experimental design, In R. Kruse et al. (Eds.), Synergies of Soft Computing and Statistics for Intelligent Data Analysis, Advances in Intelligent Systems and Computing, Vol. 190, pp. 489–497, Springer-Verlag.

  • Borrotti, M., De Lucrezia, D., Minervini, G., and Poli, I. (2010) A model based ant colony design for the protein engineering problems, In M. Dorigo et al. (Eds.), ANTS 2010, Lecture Notes in Computer Science, Vol. 6263, pp. 352–359, Springer-Verlag, 2010.

Conference Proceedings and Technical Reports

  • Bacino, V., Zoccarato, A., Liberati, C., and Borrotti, M. (2021) Statistical learning for credit risk modelling. In Book of Short Papers SIS 2021, Vol. 2021, pp. 1624–1629.

  • Bogni, S., Slanzi, D., and Borrotti, M. (2021) Q-learning estimation techniques for Dynamic Treatment Regime . In Book of Short Papers SIS 2021, Vol. 2021, pp. 578–583.

  • Hassan Elbedawi Omar, M., and Borrotti, M. (2018) Customer churn prediction based on eXtreme gradient boosting classifier. In Book of Short Papers SIS 2018, Vol. 2018, pp. 775–780.

  • Borrotti, M. (2009) A model based algorithm for evolutionary design of experiments, In F. Hutter et al. (Eds.), SLS-DS 2009, Technical Report N. TR/IRIDIA/2009-024, IRIDIA.