Pubblicazioni

Pubblicazioni recenti

Pubblicazioni per anno

2021
2020
  • Ascari, Roberto and Sonia Migliorati (2020a). “A new regression model for overdispersed binomial data accounting for outliers and an excess of zeros”. Submitted.
    — (2020b). “The flexible beta-binomial regression model”. In: Proceedings of the 6th Stochastic Modeling Techniques and Data Analysis International Conference with Demographics Workshop. SMTDA 2020. ISAST: International Society for the Advancement of Science and Technology, pp. 31–42.
  • Battiston, Pietro, Simona Gamba, and Alessandro Santoro (2020). Optimizing Tax Administration with Machine Learning. Working Paper 436. University of Milan-Bicocca Department of Economics, Management and Statistics Working Papers.
  • Brisco, Agnese Maria Di et al. (2020). “Simulation studies for a special mixture regression model with multivariate responses on the simplex”. In: Proceedings of the 6th Stochastic Modeling Techniques and Data Analysis International Conference with Demographics Workshop. SMTDA 2020. ISAST: International Society for the Advancement of Science and Technology, pp. 149–162.
  • Candelieri, Antonio and Francesco Archetti (2020a). “MISO-wiLDCosts: Multi Information Source Optimization with Location Dependent Costs”. submitted - AISTATS 2021.
    — (2020b). “Sparsifying to optimize over multiple information sources: an Augmented Gaussian Process based algorithm”. under review - Structural And Multidisciplinary Optimization.
  • Candelieri, Antonio, Francesco Archetti, et al. (2020). “Energy Efficient Hyperparameters tuning through Augmented Gaussian Processes and Multi-information Source Optimization”. ahead of printing - Proceedings of the 7th International Conference on Soft Computing and Machine Learning (ISCMI 2020).
  • Candelieri, Antonio, Bruno Galuzzi, et al. (2020). “Learning Optimal Control of Water Distribution Networks Through Sequential Model-Based Optimization”. In: Learning and Intelligent Optimization: 14th International Conference, LION 14, Athens, Greece, May 24–28, 2020. Vol. 12096. Springer, p. 303.
  • Candelieri, Antonio, Ilaria Giordani, et al. (2020). “Composition of Kernel and Acquisition Functions for High Dimensional Bayesian Optimization”. In: Learning and Intelligent Optimization: 14th International Conference, LION 14, Athens, Greece, May 24–28, 2020. Vol. 12096. Springer Nature, p. 316.
  • Candelieri, Antonio, Riccardo Perego, and Francesco Archetti (2020). “Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization”. under review - Soft Computing.
  • Colciago, Andrea, Stefano Fasani, and Lorenza Rossi (2020). “Unemployment, Firm Dy- namics, and the Business Cycle”. submitted.
  • Colombo, Emilio and Matteo Pelagatti (2020). “Statistical learning and exchange rate forecasting”. In: International Journal of Forecasting.
  • Corradin, Riccardo, Antonio Canale, and Bernardo Nipoti (n.d.). “BNPmix: an R pack- age for Bayesian nonparametric modelling via Pitman-Yor mixtures”. In: Journal of Statistical Software ().
  • Corradin, Riccardo, Luis Enrique Nieto-Barajas, and Bernardo Nipoti (2020). “Optimal stratification of survival data via Bayesian nonparametric mixtures”. submitted.
  • Lucarelli, Giorgio and Matteo Borrotti (2020). “A deep Q-learning portfolio management framework for the cryptocurrency market”. In: Neural Computing and Applications 2020, pp. 1–16.
  • Monti, Gianna S and Peter Filzmoser (2020). “Sparse least trimmed squares regression with compositional covariates for high dimensional data”. submitted.
  • Pelagatti, Matteo and Paolo Maranzano (forthcoming). “Nonparametric tests for event studies under cross-sectional dependence”. In: Quarterly Journal of Finance and Ac- counting.
  • Pelagatti, Matteo M. and Giacomo Sbrana (Jan. 2020). Estimating High Dimensional Multivariate Stochastic Volatility Models. Tech. rep. 428. University of Milano-Bicocca, Department of Economics, Management and Statistics.
  • Perego, Riccardo et al. (2020a). “AutoTinyML for microcontrollers: dealing with black- box deployability”. under review - IEEE TNNLS.
    — (2020b). “Tuning Deep Neural Network’s Hyperparameters Constrained to Deploy- ability on Tiny Systems”. In: International Conference on Artificial Neural Networks. Springer, pp. 92–103.