Danilo Macciò

IEEE Trans. Control. Syst. Technol., March, 2024

Simulation and Neural Models for Traffic Light Importance Analysis in Urban Networks.

[BibT_eX]

[DOI]

Proceedings of the 20th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, 2024

2023

An imitation learning approach for the control of a low-cost low-accuracy robotic arm for unstructured environments.

[BibT_eX]

[DOI]

Int. J. Intell. Robotics Appl., March, 2023

2022

Improving the variability of urban traffic microsimulation through the calibration of generative parameter models.

[BibT_eX]

[DOI]

J. Intell. Transp. Syst., 2022

Copula-based scenario generation for urban traffic models.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2022

Echo state network ensembles for surrogate models with an application to urban mobility.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2022

2021

Deep Learning and Low-discrepancy Sampling for Surrogate Modeling with an Application to Urban Traffic Simulation.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2021

Policy Optimization for Berth Allocation Problems.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2021

2020

Voronoi tree models for distribution-preserving sampling and generation.

[BibT_eX]

[DOI]

Pattern Recognit., 2020

Learning Robustly Stabilizing Explicit Model Predictive Controllers: A Non-Regular Sampling Approach.

[BibT_eX]

[DOI]

Thomas Parisini

IEEE Control. Syst. Lett., 2020

2019

An Improved Load Flow Method for MV Networks Based on LV Load Measurements and Estimations.

[BibT_eX]

[DOI]

IEEE Trans. Instrum. Meas., 2019

2018

Distribution-Preserving Stratified Sampling for Learning Problems.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2018

QuantTree: Histograms for Change Detection in Multivariate Data Streams.

[BibT_eX]

[DOI]

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

A Novel Approach for Sampling in Approximate Dynamic Programming Based on F-Discrepancy.

[BibT_eX]

[DOI]

IEEE Trans. Cybern., 2017

An Extreme Learning Machine Approach to Density Estimation Problems.

[BibT_eX]

[DOI]

IEEE Trans. Cybern., 2017

Uniform histograms for change detection in multivariate data.

[BibT_eX]

[DOI]

Giacomo Boracchi

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

2016

Low-Discrepancy Points for Deterministic Assignment of Hidden Weights in Extreme Learning Machines.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2016

F-Discrepancy for Efficient Sampling in Approximate Dynamic Programming.

[BibT_eX]

[DOI]

IEEE Trans. Cybern., 2016

Local linear regression for efficient data-driven control.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2016

2015

Efficient use of Nadaraya-Watson models and low-discrepancy sequences for approximate dynamic programming.

[BibT_eX]

[DOI]

Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

Lattice point sets for efficient kernel smoothing models.

[BibT_eX]

[DOI]

Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

2014

Local Linear Regression for Function Learning: An Analysis Based on Sample Discrepancy.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2014

Low-discrepancy sampling for approximate dynamic programming with local approximators.

[BibT_eX]

[DOI]

Comput. Oper. Res., 2014

Lattice sampling for efficient learning with Nadaraya-Watson local models.

[BibT_eX]

[DOI]

Proceedings of the 2014 International Joint Conference on Neural Networks, 2014

An approach to exploit non-optimized data for efficient control of unknown systems through neural and kernel models.

[BibT_eX]

[DOI]

Proceedings of the 2014 International Joint Conference on Neural Networks, 2014

An analysis based on F-discrepancy for sampling in regression tree learning.

[BibT_eX]

[DOI]

Proceedings of the 2014 International Joint Conference on Neural Networks, 2014

2013

Learning With Kernel Smoothing Models and Low-Discrepancy Sampling.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2013

Function learning with local linear regression models: An analysis based on discrepancy.

[BibT_eX]

[DOI]

Roberto Marcialis

Proceedings of the 2013 International Joint Conference on Neural Networks, 2013

Quasi-random sampling for approximate dynamic programming.

[BibT_eX]

[DOI]

Proceedings of the 2013 International Joint Conference on Neural Networks, 2013

2012

Efficient kernel models for learning and approximate minimization problems.

[BibT_eX]

[DOI]

Neurocomputing, 2012

Local Models for data-driven learning of control policies for complex systems.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2012

2011

A numerical method for minimum distance estimation problems.

[BibT_eX]

[DOI]

J. Multivar. Anal., 2011

A comparison of global and semi-local approximation in T-stage stochastic optimization.

[BibT_eX]

[DOI]

Eur. J. Oper. Res., 2011

2010

Efficient global maximum likelihood estimation through kernel methods.

[BibT_eX]

[DOI]

Marco Muselli

Neural Networks, 2010

Functional Optimization Through Semilocal Approximate Minimization.

[BibT_eX]

[DOI]

Marco Muselli

Oper. Res., 2010

2008

Deterministic Learning for Maximum-Likelihood Estimation Through Neural Networks.

[BibT_eX]

[DOI]