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Cristiano Cervellera
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2020 – today
- 2024
- [j39]Cristiano Cervellera
, Danilo Macciò
Model Predictive Control of Port-City Traffic Interactions Over Shared Urban Infrastructure. IEEE Trans. Control. Syst. Technol. 32(2): 688-695 (2024) - [c21]Cristiano Cervellera, Danilo Macciò, Francesco Rebora:
Simulation and Neural Models for Traffic Light Importance Analysis in Urban Networks. MESA 2024: 1-8 - 2023
- [j38]Cristiano Cervellera
Optimized ensemble value function approximation for dynamic programming. Eur. J. Oper. Res. 309(2): 719-730 (2023) - [j37]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
- [j36]Cristiano Cervellera
Copula-based scenario generation for urban traffic models. Expert Syst. Appl. 210: 118389 (2022) - [j35]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) - [c20]Cristiano Cervellera, Danilo Macciò, Francesco Rebora:
Echo state network ensembles for surrogate models with an application to urban mobility. IJCNN 2022: 1-8 - 2021
- [c19]Cristiano Cervellera, Mauro Gaggero, Danilo Macciò:
Policy Optimization for Berth Allocation Problems. IJCNN 2021: 1-6 - [c18]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
- [j34]Cristiano Cervellera
Learning Robustly Stabilizing Explicit Model Predictive Controllers: A Non-Regular Sampling Approach. IEEE Control. Syst. Lett. 4(3): 737-742 (2020) - [j33]Cristiano Cervellera
Voronoi tree models for distribution-preserving sampling and generation. Pattern Recognit. 97 (2020)
2010 – 2019
- 2019
- [j32]Antonio Cataliotti
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
- [j31]Cristiano Cervellera, Danilo Macciò
Distribution-Preserving Stratified Sampling for Learning Problems. IEEE Trans. Neural Networks Learn. Syst. 29(7): 2886-2895 (2018) - [c17]Giacomo Boracchi, Diego Carrera, Cristiano Cervellera, Danilo Macciò:
QuantTree: Histograms for Change Detection in Multivariate Data Streams. ICML 2018: 638-647 - 2017
- [j30]Cristiano Cervellera
An Extreme Learning Machine Approach to Density Estimation Problems. IEEE Trans. Cybern. 47(10): 3254-3265 (2017) - [j29]Cristiano Cervellera
A Novel Approach for Sampling in Approximate Dynamic Programming Based on F-Discrepancy. IEEE Trans. Cybern. 47(10): 3355-3366 (2017) - [c16]Giacomo Boracchi
Uniform histograms for change detection in multivariate data. IJCNN 2017: 1732-1739 - 2016
- [j28]Cristiano Cervellera, Danilo Macciò
F-Discrepancy for Efficient Sampling in Approximate Dynamic Programming. IEEE Trans. Cybern. 46(7): 1628-1639 (2016) - [j27]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
- [c15]Cristiano Cervellera, Mauro Gaggero
Lattice point sets for efficient kernel smoothing models. IJCNN 2015: 1-8 - [c14]Cristiano Cervellera, Mauro Gaggero
Efficient use of Nadaraya-Watson models and low-discrepancy sequences for approximate dynamic programming. IJCNN 2015: 1-8 - 2014
- [j26]Cristiano Cervellera, Mauro Gaggero
Low-discrepancy sampling for approximate dynamic programming with local approximators. Comput. Oper. Res. 43: 108-115 (2014) - [j25]Diana Flaccadoro, Cristiano Cervellera, Giorgio Bosia, Eva Riccomagno
Modelling of Fault Detection and Diagnostics for Hybrid Bus Using Chain Graph Models. Qual. Reliab. Eng. Int. 30(7): 975-983 (2014) - [j24]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) - [c13]Cristiano Cervellera, Mauro Gaggero
An analysis based on F-discrepancy for sampling in regression tree learning. IJCNN 2014: 1115-1121 - [c12]Cristiano Cervellera, Mauro Gaggero
An approach to exploit non-optimized data for efficient control of unknown systems through neural and kernel models. IJCNN 2014: 1856-1863 - [c11]Cristiano Cervellera, Mauro Gaggero
Lattice sampling for efficient learning with Nadaraya-Watson local models. IJCNN 2014: 1915-1922 - 2013
- [j23]Angelo Alessandri
Predictive Control of Container Flows in Maritime Intermodal Terminals. IEEE Trans. Control. Syst. Technol. 21(4): 1423-1431 (2013) - [j22]Derong Liu
Editorial A Successful Change From TNN to TNNLS and a Very Successful Year. IEEE Trans. Neural Networks Learn. Syst. 24(1): 1-7 (2013) - [j21]Cristiano Cervellera, Danilo Macciò
Learning With Kernel Smoothing Models and Low-Discrepancy Sampling. IEEE Trans. Neural Networks Learn. Syst. 24(3): 504-509 (2013) - [c10]Cristiano Cervellera, Mauro Gaggero
Quasi-random sampling for approximate dynamic programming. IJCNN 2013: 1-8 - [c9]Cristiano Cervellera, Danilo Macciò
Function learning with local linear regression models: An analysis based on discrepancy. IJCNN 2013: 1-8 - 2012
- [j20]Danilo Macciò
Local Models for data-driven learning of control policies for complex systems. Expert Syst. Appl. 39(18): 13399-13408 (2012) - [j19]Cristiano Cervellera, Mauro Gaggero
Efficient kernel models for learning and approximate minimization problems. Neurocomputing 97: 74-85 (2012) - 2011
- [j18]Luca Caviglione
Design, optimization and performance evaluation of a content distribution overlay for streaming. Comput. Commun. 34(12): 1497-1509 (2011) - [j17]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) - [j16]Luca Caviglione
An Optimized Content Replication and Distribution Framework for Vehicular Networks. J. Intell. Transp. Syst. 15(4): 179-192 (2011) - [j15]Cristiano Cervellera, Danilo Macciò
A numerical method for minimum distance estimation problems. J. Multivar. Anal. 102(4): 789-800 (2011) - 2010
- [j14]Marco Baglietto
Management of water resource systems in the presence of uncertainties by nonlinear approximation techniques and deterministic sampling. Comput. Optim. Appl. 47(2): 349-376 (2010) - [j13]Cristiano Cervellera, Danilo Macciò
Functional Optimization Through Semilocal Approximate Minimization. Oper. Res. 58(5): 1491-1504 (2010) - [j12]Cristiano Cervellera, Danilo Macciò
Efficient global maximum likelihood estimation through kernel methods. Neural Networks 23(7): 917-925 (2010) - [j11]Cristiano Cervellera:
Lattice point sets for deterministic learning and approximate optimization problems. IEEE Trans. Neural Networks 21(4): 687-692 (2010)
2000 – 2009
- 2009
- [j10]Cristiano Cervellera, Luca Caviglione
Optimization of a peer-to-peer system for efficient content replication. Eur. J. Oper. Res. 196(2): 423-433 (2009) - 2008
- [j9]Luca Caviglione
Optimization of an eMule-like modifier strategy. Comput. Commun. 31(16): 3876-3882 (2008) - [j8]Angelo Alessandri
Modeling and Feedback Control for Resource Allocation and Performance Analysis in Container Terminals. IEEE Trans. Intell. Transp. Syst. 9(4): 601-614 (2008) - [j7]Cristiano Cervellera, Danilo Macciò
Deterministic Learning for Maximum-Likelihood Estimation Through Neural Networks. IEEE Trans. Neural Networks 19(8): 1456-1467 (2008) - [c8]Angelo Alessandri
Nonlinear predictive control for the management of container flows in maritime intermodal terminals. CDC 2008: 2800-2805 - 2007
- [j6]Cristiano Cervellera, Marco Muselli:
Efficient sampling in approximate dynamic programming algorithms. Comput. Optim. Appl. 38(3): 417-443 (2007) - [j5]Luca Caviglione
Design of a peer-to-peer system for optimized content replication. Comput. Commun. 30(16): 3107-3116 (2007) - [j4]Cristiano Cervellera, Aihong Wen, Victoria C. P. Chen:
Neural network and regression spline value function approximations for stochastic dynamic programming. Comput. Oper. Res. 34(1): 70-90 (2007) - [j3]Angelo Alessandri
Design of Asymptotic Estimators: An Approach Based on Neural Networks and Nonlinear Programming. IEEE Trans. Neural Networks 18(1): 86-96 (2007) - 2006
- [j2]Cristiano Cervellera, Victoria C. P. Chen, Aihong Wen:
Optimization of a large-scale water reservoir network by stochastic dynamic programming with efficient state space discretization. Eur. J. Oper. Res. 171(3): 1139-1151 (2006) - [c7]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 - 2005
- [c6]Angelo Alessandri, Cristiano Cervellera, Filippo Aldo Grassia, Marcello Sanguineti:
An approximate solution to optimal Lp state estimation problems. ACC 2005: 4204-4209 - 2004
- [j1]Cristiano Cervellera, Marco Muselli:
Deterministic design for neural network learning: an approach based on discrepancy. IEEE Trans. Neural Networks 15(3): 533-544 (2004) - [c5]Angelo Alessandri, Cristiano Cervellera, Filippo Aldo Grassia, Marcello Sanguineti
Design of observers for continuous-time nonlinear systems using neural networks. ACC 2004: 2433-2438 - 2003
- [c4]Angelo Alessandri
Application of neural control to economic growth problems. CIFEr 2003: 151-157 - [c3]Cristiano Cervellera, Marco Muselli:
A Deterministic Learning Approch Based on Discrepancy. WIRN 2003: 53-60 - 2002
- [c2]Angelo Alessandri, Cristiano Cervellera, Filippo Aldo Grassia:
Optimal neural feedback control applied to a problem of economic growth in freight transport market. CDC 2002: 1141-1146 - 2000
- [c1]Marco Baglietto
Approximating networks, dynamic programming and stochastic approximation. ACC 2000: 3304-3308
Coauthor Index
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last updated on 2024-10-25 20:09 CEST by the dblp team
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