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2020 – today
- 2024
[j23]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): 688-695 (2024)- [c14]Cristiano Cervellera, Danilo Macciò, Francesco Rebora:
Simulation and Neural Models for Traffic Light Importance Analysis in Urban Networks. MESA 2024: 1-8 - 2023
- [j22]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): 13-30 (2023) - 2022
- [j21]Cristiano Cervellera, Danilo Macciò, Francesco Rebora:
Copula-based scenario generation for urban traffic models. Expert Syst. Appl. 210: 118389 (2022) - [j20]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): 544-556 (2022) - [c13]Cristiano Cervellera, Danilo Macciò, Francesco Rebora:
Echo state network ensembles for surrogate models with an application to urban mobility. IJCNN 2022: 1-8 - 2021
- [c12]Cristiano Cervellera, Mauro Gaggero, Danilo Macciò:
Policy Optimization for Berth Allocation Problems. IJCNN 2021: 1-6 - [c11]Cristiano Cervellera, Danilo Macciò, Francesco Rebora:
Deep Learning and Low-discrepancy Sampling for Surrogate Modeling with an Application to Urban Traffic Simulation. IJCNN 2021: 1-8 - 2020
- [j19]Cristiano Cervellera, Danilo Macciò, Thomas Parisini:
Learning Robustly Stabilizing Explicit Model Predictive Controllers: A Non-Regular Sampling Approach. IEEE Control. Syst. Lett. 4(3): 737-742 (2020) - [j18]Cristiano Cervellera, Danilo Macciò:
Voronoi tree models for distribution-preserving sampling and generation. Pattern Recognit. 97 (2020)
2010 – 2019
- 2019
- [j17]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): 430-438 (2019) - 2018
- [j16]Cristiano Cervellera, Danilo Macciò:
Distribution-Preserving Stratified Sampling for Learning Problems. IEEE Trans. Neural Networks Learn. Syst. 29(7): 2886-2895 (2018) - [c10]Giacomo Boracchi, Diego Carrera, Cristiano Cervellera, Danilo Macciò:
QuantTree: Histograms for Change Detection in Multivariate Data Streams. ICML 2018: 638-647 - 2017
- [j15]Cristiano Cervellera, Danilo Macciò:
An Extreme Learning Machine Approach to Density Estimation Problems. IEEE Trans. Cybern. 47(10): 3254-3265 (2017) - [j14]Cristiano Cervellera, Danilo Macciò:
A Novel Approach for Sampling in Approximate Dynamic Programming Based on F-Discrepancy. IEEE Trans. Cybern. 47(10): 3355-3366 (2017) - [c9]Giacomo Boracchi, Cristiano Cervellera, Danilo Macciò:
Uniform histograms for change detection in multivariate data. IJCNN 2017: 1732-1739 - 2016
- [j13]Danilo Macciò:
Local linear regression for efficient data-driven control. Knowl. Based Syst. 98: 55-67 (2016) - [j12]Cristiano Cervellera, Danilo Macciò:
F-Discrepancy for Efficient Sampling in Approximate Dynamic Programming. IEEE Trans. Cybern. 46(7): 1628-1639 (2016) - [j11]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): 891-896 (2016) - 2015
- [c8]Cristiano Cervellera, Mauro Gaggero, Danilo Macciò, Roberto Marcialis:
Lattice point sets for efficient kernel smoothing models. IJCNN 2015: 1-8 - [c7]Cristiano Cervellera, Mauro Gaggero, Danilo Macciò, Roberto Marcialis:
Efficient use of Nadaraya-Watson models and low-discrepancy sequences for approximate dynamic programming. IJCNN 2015: 1-8 - 2014
- [j10]Cristiano Cervellera, Mauro Gaggero, Danilo Macciò:
Low-discrepancy sampling for approximate dynamic programming with local approximators. Comput. Oper. Res. 43: 108-115 (2014) - [j9]Cristiano Cervellera, Danilo Macciò:
Local Linear Regression for Function Learning: An Analysis Based on Sample Discrepancy. IEEE Trans. Neural Networks Learn. Syst. 25(11): 2086-2098 (2014) - [c6]Cristiano Cervellera, Mauro Gaggero, Danilo Macciò:
An analysis based on F-discrepancy for sampling in regression tree learning. IJCNN 2014: 1115-1121 - [c5]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: 1856-1863 - [c4]Cristiano Cervellera, Mauro Gaggero, Danilo Macciò, Roberto Marcialis:
Lattice sampling for efficient learning with Nadaraya-Watson local models. IJCNN 2014: 1915-1922 - 2013
- [j8]Cristiano Cervellera, Danilo Macciò:
Learning With Kernel Smoothing Models and Low-Discrepancy Sampling. IEEE Trans. Neural Networks Learn. Syst. 24(3): 504-509 (2013) - [c3]Cristiano Cervellera, Mauro Gaggero, Danilo Macciò, Roberto Marcialis:
Quasi-random sampling for approximate dynamic programming. IJCNN 2013: 1-8 - [c2]Cristiano Cervellera, Danilo Macciò, Roberto Marcialis:
Function learning with local linear regression models: An analysis based on discrepancy. IJCNN 2013: 1-8 - 2012
- [j7]Danilo Macciò, Cristiano Cervellera:
Local Models for data-driven learning of control policies for complex systems. Expert Syst. Appl. 39(18): 13399-13408 (2012) - [j6]Cristiano Cervellera, Mauro Gaggero, Danilo Macciò:
Efficient kernel models for learning and approximate minimization problems. Neurocomputing 97: 74-85 (2012) - 2011
- [j5]Cristiano Cervellera, Danilo Macciò:
A comparison of global and semi-local approximation in T-stage stochastic optimization. Eur. J. Oper. Res. 208(2): 109-118 (2011) - [j4]Cristiano Cervellera, Danilo Macciò:
A numerical method for minimum distance estimation problems. J. Multivar. Anal. 102(4): 789-800 (2011) - 2010
- [j3]Cristiano Cervellera, Danilo Macciò, Marco Muselli:
Functional Optimization Through Semilocal Approximate Minimization. Oper. Res. 58(5): 1491-1504 (2010) - [j2]Cristiano Cervellera, Danilo Macciò, Marco Muselli:
Efficient global maximum likelihood estimation through kernel methods. Neural Networks 23(7): 917-925 (2010)
2000 – 2009
- 2008
- [j1]Cristiano Cervellera, Danilo Macciò, Marco Muselli:
Deterministic Learning for Maximum-Likelihood Estimation Through Neural Networks. IEEE Trans. Neural Networks 19(8): 1456-1467 (2008) - 2006
- [c1]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: 5388-5393
Coauthor Index
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last updated on 2024-10-25 21:14 CEST by the dblp team
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