Danilo Macciò

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 18

Top Topics

Approximate Dynamic Programming

Predictive Control

Multivariate Data

Top Venues

IEEE Trans. Cybern.

IEEE Trans. Neural Networks Learn. Syst.

IEEE Control. Syst. Lett.

Publications

Cristiano Cervellera, Danilo Macciò, Francesco Rebora
Model Predictive Control of Port-City Traffic Interactions Over Shared Urban Infrastructure. IEEE Trans. Control. Syst. Technol. 32 (2) (2024)
Fabio Bonsignorio, Cristiano Cervellera, Danilo Macciò, Enrica Zereik
An imitation learning approach for the control of a low-cost low-accuracy robotic arm for unstructured environments. Int. J. Intell. Robotics Appl. 7 (1) (2023)
Cristiano Cervellera, Danilo Macciò, Francesco Rebora
Copula-based scenario generation for urban traffic models. Expert Syst. Appl. 210 (2022)
Cristiano Cervellera, Danilo Macciò, Francesco Rebora
Echo state network ensembles for surrogate models with an application to urban mobility. IJCNN (2022)
Cristiano Cervellera, Danilo Macciò, Francesco Rebora
Improving the variability of urban traffic microsimulation through the calibration of generative parameter models. J. Intell. Transp. Syst. 26 (5) (2022)
Cristiano Cervellera, Danilo Macciò, Francesco Rebora
Deep Learning and Low-discrepancy Sampling for Surrogate Modeling with an Application to Urban Traffic Simulation. IJCNN (2021)
Cristiano Cervellera, Mauro Gaggero, Danilo Macciò
Policy Optimization for Berth Allocation Problems. IJCNN (2021)
Cristiano Cervellera, Danilo Macciò
Voronoi tree models for distribution-preserving sampling and generation. Pattern Recognit. 97 (2020)
Cristiano Cervellera, Danilo Macciò, Thomas Parisini
Learning Robustly Stabilizing Explicit Model Predictive Controllers: A Non-Regular Sampling Approach. IEEE Control. Syst. Lett. 4 (3) (2020)
Antonio Cataliotti, Cristiano Cervellera, Valentina Cosentino, Dario Di Cara, Mauro Gaggero, Danilo Macciò, Giuseppe Marsala, Antonella Ragusa, Giovanni Tinè
An Improved Load Flow Method for MV Networks Based on LV Load Measurements and Estimations. IEEE Trans. Instrum. Meas. 68 (2) (2019)
Giacomo Boracchi, Diego Carrera, Cristiano Cervellera, Danilo Macciò
QuantTree: Histograms for Change Detection in Multivariate Data Streams. ICML (2018)
Cristiano Cervellera, Danilo Macciò
Distribution-Preserving Stratified Sampling for Learning Problems. IEEE Trans. Neural Networks Learn. Syst. 29 (7) (2018)
Cristiano Cervellera, Danilo Macciò
An Extreme Learning Machine Approach to Density Estimation Problems. IEEE Trans. Cybern. 47 (10) (2017)
Cristiano Cervellera, Danilo Macciò
A Novel Approach for Sampling in Approximate Dynamic Programming Based on F-Discrepancy. IEEE Trans. Cybern. 47 (10) (2017)
Giacomo Boracchi, Cristiano Cervellera, Danilo Macciò
Uniform histograms for change detection in multivariate data. IJCNN (2017)
Cristiano Cervellera, Danilo Macciò
F-Discrepancy for Efficient Sampling in Approximate Dynamic Programming. IEEE Trans. Cybern. 46 (7) (2016)
Danilo Macciò
Local linear regression for efficient data-driven control. Knowl. Based Syst. 98 (2016)
Cristiano Cervellera, Danilo Macciò
Low-Discrepancy Points for Deterministic Assignment of Hidden Weights in Extreme Learning Machines. IEEE Trans. Neural Networks Learn. Syst. 27 (4) (2016)
Cristiano Cervellera, Mauro Gaggero, Danilo Macciò, Roberto Marcialis
Lattice point sets for efficient kernel smoothing models. IJCNN (2015)
Cristiano Cervellera, Mauro Gaggero, Danilo Macciò, Roberto Marcialis
Efficient use of Nadaraya-Watson models and low-discrepancy sequences for approximate dynamic programming. IJCNN (2015)
Cristiano Cervellera, Mauro Gaggero, Danilo Macciò
Low-discrepancy sampling for approximate dynamic programming with local approximators. Comput. Oper. Res. 43 (2014)
Cristiano Cervellera, Mauro Gaggero, Danilo Macciò, Roberto Marcialis
Lattice sampling for efficient learning with Nadaraya-Watson local models. IJCNN (2014)
Cristiano Cervellera, Danilo Macciò
Local Linear Regression for Function Learning: An Analysis Based on Sample Discrepancy. IEEE Trans. Neural Networks Learn. Syst. 25 (11) (2014)
Cristiano Cervellera, Mauro Gaggero, Danilo Macciò
An analysis based on F-discrepancy for sampling in regression tree learning. IJCNN (2014)
Cristiano Cervellera, Mauro Gaggero, Danilo Macciò, Roberto Marcialis
An approach to exploit non-optimized data for efficient control of unknown systems through neural and kernel models. IJCNN (2014)
Cristiano Cervellera, Danilo Macciò
Learning With Kernel Smoothing Models and Low-Discrepancy Sampling. IEEE Trans. Neural Networks Learn. Syst. 24 (3) (2013)
Cristiano Cervellera, Mauro Gaggero, Danilo Macciò, Roberto Marcialis
Quasi-random sampling for approximate dynamic programming. IJCNN (2013)
Cristiano Cervellera, Danilo Macciò, Roberto Marcialis
Function learning with local linear regression models: An analysis based on discrepancy. IJCNN (2013)
Danilo Macciò, Cristiano Cervellera
Local Models for data-driven learning of control policies for complex systems. Expert Syst. Appl. 39 (18) (2012)
Cristiano Cervellera, Mauro Gaggero, Danilo Macciò
Efficient kernel models for learning and approximate minimization problems. Neurocomputing 97 (2012)
Cristiano Cervellera, Danilo Macciò
A comparison of global and semi-local approximation in T-stage stochastic optimization. Eur. J. Oper. Res. 208 (2) (2011)
Cristiano Cervellera, Danilo Macciò
A numerical method for minimum distance estimation problems. J. Multivar. Anal. 102 (4) (2011)
Cristiano Cervellera, Danilo Macciò, Marco Muselli
Functional Optimization Through Semilocal Approximate Minimization. Oper. Res. 58 (5) (2010)
Cristiano Cervellera, Danilo Macciò, Marco Muselli
Efficient global maximum likelihood estimation through kernel methods. Neural Networks 23 (7) (2010)
Cristiano Cervellera, Danilo Macciò, Marco Muselli
Deterministic Learning for Maximum-Likelihood Estimation Through Neural Networks. IEEE Trans. Neural Networks 19 (8) (2008)
Angelo Alessandri, Cristiano Cervellera, Danilo Macciò, Marcello Sanguineti
Design of Parameterized State Observers and Controllers for a Class of Nonlinear Continuous-Time Systems. CDC (2006)