Marcello Sanguineti

IEEE Trans. Neural Networks Learn. Syst., April, 2024

2023

Approximation of classifiers by deep perceptron networks.

[BibT_eX]

[DOI]

Neural Networks, August, 2023

2022

A game-theoretic approach for reliability evaluation of public transportation transfers with stochastic features.

[BibT_eX]

[DOI]

Yuval Hadas

Durgesh Haribhau Salunkhe

EURO J. Transp. Logist., 2022

An efficient combined local and global search strategy for optimization of parallel kinematic mechanisms with joint limits and collision constraints.

[BibT_eX]

[DOI]

CoRR, 2022

Deeper Insights into Neural Nets with Random Weights.

[BibT_eX]

[DOI]

Ming Li

Proceedings of the AI 2021: Advances in Artificial Intelligence, 2022

2021

Special Issue Optimization for Machine Learning Guest Editorial.

[BibT_eX]

[DOI]

Elena Tànfani

Soft Comput., 2021

Correlations of random classifiers on large data sets.

[BibT_eX]

[DOI]

Soft Comput., 2021

Braess' paradox: A cooperative game-theoretic point of view.

[BibT_eX]

[DOI]

Networks, 2021

2020

Automated Analysis of the Origin of Movement: An Approach Based on Cooperative Games on Graphs.

[BibT_eX]

[DOI]

IEEE Trans. Hum. Mach. Syst., 2020

Transboundary pollution control and environmental absorption efficiency management.

[BibT_eX]

[DOI]

Ann. Oper. Res., 2020

A Computational Method to Automatically Detect the Perceived Origin of Full-Body Human Movement and its Propagation.

[BibT_eX]

[DOI]

Proceedings of the Companion Publication of the 2020 International Conference on Multimodal Interaction, 2020

2019

Classification by Sparse Neural Networks.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2019

Some properties of transportation network cooperative games.

[BibT_eX]

[DOI]

Yuval Hadas

Networks, 2019

Probabilistic Bounds for Binary Classification of Large Data Sets.

[BibT_eX]

[DOI]

Mohammad Mahdi Ghazaei Ardakani

Proceedings of the Recent Advances in Big Data and Deep Learning, 2019

On the Optimal Selection of Motors and Transmissions for a Back-Support Exoskeleton.

[BibT_eX]

[DOI]

Erfan Shojaei Barjuei

Darwin G. Caldwell

Jesús Ortiz

Proceedings of the 2019 IEEE International Conference on Cyborg and Bionic Systems, 2019

2018

Neural approximations in discounted infinite-horizon stochastic optimal control problems.

[BibT_eX]

[DOI]

Eng. Appl. Artif. Intell., 2018

Probabilistic Bounds on Complexity of Networks Computing Binary Classification Tasks.

[BibT_eX]

[DOI]

Proceedings of the 18th Conference Information Technologies, 2018

2017

Supervised and semi-supervised classifiers for the detection of flood-prone areas.

[BibT_eX]

[DOI]

Angela Celeste Taramasso

Soft Comput., 2017

A theoretical analysis of buffer occupancy for Intermittently-Connected Networks.

[BibT_eX]

[DOI]

Perform. Evaluation, 2017

Probabilistic lower bounds for approximation by shallow perceptron networks.

[BibT_eX]

[DOI]

Neural Networks, 2017

LQG Online Learning.

[BibT_eX]

[DOI]

Neural Comput., 2017

Graph-restricted game approach for investigating human movement qualities.

[BibT_eX]

[DOI]

Proceedings of the 4th International Conference on Movement Computing, 2017

2016

Model complexities of shallow networks representing highly varying functions.

[BibT_eX]

[DOI]

Neurocomputing, 2016

Learning with hard constraints as a limit case of learning with soft constraints.

[BibT_eX]

[DOI]

Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

2015

Learning With Mixed Hard/Soft Pointwise Constraints.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2015

Foundations of Support Constraint Machines.

[BibT_eX]

[DOI]

Neural Comput., 2015

Narrowing the Search for Optimal Call-Admission Policies Via a Nonlinear Stochastic Knapsack Model.

[BibT_eX]

[DOI]

J. Optim. Theory Appl., 2015

A Multimodal Dataset for the Analysis of Movement Qualities in Karate Martial Art.

[BibT_eX]

[DOI]

Radoslaw Niewiadomski

EAI Endorsed Trans. Collab. Comput., 2015

Online learning as an LQG optimal control problem with random matrices.

[BibT_eX]

[DOI]

Proceedings of the 14th European Control Conference, 2015

2014

A theoretical framework for supervised learning from regions.

[BibT_eX]

[DOI]

Neurocomputing, 2014

On the detection of the level of attention in an orchestra through head movements.

[BibT_eX]

[DOI]

Int. J. Arts Technol., 2014

Evaluation of the Average Packet Delivery Delay in Highly-Disrupted Networks: The DTN and IP-like Protocol Cases.

[BibT_eX]

[DOI]

IEEE Commun. Lett., 2014

Approximate dynamic programming for stochastic N-stage optimization with application to optimal consumption under uncertainty.

[BibT_eX]

[DOI]

Mauro Gaggero

Comput. Optim. Appl., 2014

Complexity of Shallow Networks Representing Functions with Large Variations.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2014, 2014

2013

Approximating Multivariable Functions by Feedforward Neural Nets.

[BibT_eX]

[DOI]

Proceedings of the Handbook on Neural Information Processing, 2013

Optimality Conditions for Coordinate-Convex Policies in CAC With Nonlinear Feasibility Boundaries.

[BibT_eX]

[DOI]

IEEE/ACM Trans. Netw., 2013

Learning with Boundary Conditions.

[BibT_eX]

[DOI]

Marco Gori

Neural Comput., 2013

Dynamic Programming and Value-Function Approximation in Sequential Decision Problems: Error Analysis and Numerical Results.

[BibT_eX]

[DOI]

Mauro Gaggero

J. Optim. Theory Appl., 2013

Can Two Hidden Layers Make a Difference?

[BibT_eX]

[DOI]

Proceedings of the Adaptive and Natural Computing Algorithms, 2013

Learning with Hard Constraints.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2013, 2013

Towards Automated Analysis of Joint Music Performance in the Orchestra.

[BibT_eX]

[DOI]

Proceedings of the Arts and Technology, Third International Conference, 2013

2012

Dependence of Computational Models on Input Dimension: Tractability of Approximation and Optimization Tasks.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Theory, 2012

Suboptimal Solutions to Team Optimization Problems with Stochastic Information Structure.

[BibT_eX]

[DOI]

Mauro Gaggero

SIAM J. Optim., 2012

New insights into Witsenhausen's counterexample.

[BibT_eX]

[DOI]

Optim. Lett., 2012

A Model of Buffer Occupancy for ICNs.

[BibT_eX]

[DOI]

IEEE Commun. Lett., 2012

Accuracy of approximations of solutions to Fredholm equations by kernel methods.

[BibT_eX]

[DOI]

Appl. Math. Comput., 2012

An application to two-hop forwarding of a model of buffer occupancy in ICNs.

[BibT_eX]

[DOI]

Proceedings of the 7th International Conference on System of Systems Engineering, 2012

Approximation structures with moderate complexity in functional optimization and dynamic programming.

[BibT_eX]

[DOI]

Proceedings of the 51th IEEE Conference on Decision and Control, 2012

2011

On a Variational Norm Tailored to Variable-Basis Approximation Schemes.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Theory, 2011

Team optimization problems with Lipschitz continuous strategies.

[BibT_eX]

[DOI]

Optim. Lett., 2011

Can dictionary-based computational models outperform the best linear ones?

[BibT_eX]

[DOI]

Neural Networks, 2011

Some comparisons of complexity in dictionary-based and linear computational models.

[BibT_eX]

[DOI]

Neural Networks, 2011

CAC with Nonlinearly-Constrained Feasibility Regions.

[BibT_eX]

[DOI]

IEEE Commun. Lett., 2011

Structural properties of optimal coordinate-convex policies for CAC with nonlinearly-constrained feasibility regions.

[BibT_eX]

[DOI]

Proceedings of the INFOCOM 2011. 30th IEEE International Conference on Computer Communications, 2011

Bounds for Approximate Solutions of Fredholm Integral Equations Using Kernel Networks.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2011, 2011

A Generalized Stochastic Knapsack Problem with Application in Call Admission Control.

[BibT_eX]

[DOI]

Proceedings of the 10th Cologne-Twente Workshop on graphs and combinatorial optimization. Extended Abstracts, 2011

2010

Error bounds for suboptimal solutions to kernel principal component analysis.

[BibT_eX]

[DOI]

Optim. Lett., 2010

Regularization Techniques and Suboptimal Solutions to Optimization Problems in Learning from Data.

[BibT_eX]

[DOI]

Neural Comput., 2010

Minimizing Sequences for a Family of Functional Optimal Estimation Problems.

[BibT_eX]

[DOI]

J. Optim. Theory Appl., 2010

On spectral windows in supervised learning from data.

[BibT_eX]

[DOI]

Inf. Process. Lett., 2010

Guest editorial.

[BibT_eX]

[DOI]

Enza Messina

Comput. Oper. Res., 2010

Management of water resource systems in the presence of uncertainties by nonlinear approximation techniques and deterministic sampling.

[BibT_eX]

[DOI]

Comput. Optim. Appl., 2010

Smooth Optimal Decision Strategies for Static Team Optimization Problems and Their Approximations.

[BibT_eX]

[DOI]

Proceedings of the SOFSEM 2010: Theory and Practice of Computer Science, 2010

Some Comparisons of Model Complexity in Linear and Neural-Network Approximation.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks - ICANN 2010, 2010

2009

Regularization and Suboptimal Solutions in Learning from Data.

[BibT_eX]

[DOI]

Proceedings of the Innovations in Neural Information Paradigms and Applications, 2009

The extended Ritz method for functional optimization: overview and applications to single-person and team optimal decision problems.

[BibT_eX]

[DOI]

Marco Baglietto

Riccardo Zoppoli

Optim. Methods Softw., 2009

Complexity of Gaussian-radial-basis networks approximating smooth functions.

[BibT_eX]

[DOI]

J. Complex., 2009

Accuracy of suboptimal solutions to kernel principal component analysis.

[BibT_eX]

[DOI]

Comput. Optim. Appl., 2009

Guest Editorial.

[BibT_eX]

[DOI]

O. Erhun Kundakcioglu

Theodore B. Trafalis

Comput. Manag. Sci., 2009

The weight-decay technique in learning from data: an optimization point of view.

[BibT_eX]

[DOI]

Comput. Manag. Sci., 2009

On Tractability of Neural-Network Approximation.

[BibT_eX]

[DOI]

Proceedings of the Adaptive and Natural Computing Algorithms, 9th International Conference, 2009

2008

Geometric Upper Bounds on Rates of Variable-Basis Approximation.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Theory, 2008

Approximate Minimization of the Regularized Expected Error over Kernel Models.

[BibT_eX]

[DOI]

Math. Oper. Res., 2008

Geometric Rates of Approximation by Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the SOFSEM 2008: Theory and Practice of Computer Science, 2008

2007

Design of Asymptotic Estimators: An Approach Based on Neural Networks and Nonlinear Programming.

[BibT_eX]

[DOI]

Cristiano Cervellera

IEEE Trans. Neural Networks, 2007

Estimates of covering numbers of convex sets with slowly decaying orthogonal subsets.

[BibT_eX]

[DOI]

Discret. Appl. Math., 2007

A recursive algorithm for nonlinear least-squares problems.

[BibT_eX]

[DOI]

Comput. Optim. Appl., 2007

Estimates of Approximation Rates by Gaussian Radial-Basis Functions.

[BibT_eX]

[DOI]

Proceedings of the Adaptive and Natural Computing Algorithms, 8th International Conference, 2007

2006

Design of Parameterized State Observers and Controllers for a Class of Nonlinear Continuous-Time Systems.

[BibT_eX]

[DOI]

Proceedings of the 45th IEEE Conference on Decision and Control, 2006

2005

Error Estimates for Approximate Optimization by the Extended Ritz Method.

[BibT_eX]

[DOI]

SIAM J. Optim., 2005

Optimization of approximating networks for optimal fault diagnosis.

[BibT_eX]

[DOI]

Optim. Methods Softw., 2005

Rates of Minimization of Error Functionals over Boolean Variable-Basis Functions.

[BibT_eX]

[DOI]

J. Math. Model. Algorithms, 2005

Learning with generalization capability by kernel methods of bounded complexity.

[BibT_eX]

[DOI]

J. Complex., 2005

An approximate solution to optimal Lp state estimation problems.

[BibT_eX]

[DOI]

Proceedings of the American Control Conference, 2005

2004

Minimization of Error Functionals over Variable-Basis Functions.

[BibT_eX]

[DOI]

SIAM J. Optim., 2004

Design of observers for continuous-time nonlinear systems using neural networks.

[BibT_eX]

[DOI]

Proceedings of the 2004 American Control Conference, 2004

2003

Neural network learning as approximate optimization.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Nets and Genetic Algorithms, 2003

EKF learning for feedforward neural networks.

[BibT_eX]

[DOI]

Proceedings of the 7th European Control Conference, 2003

On the convergence of EKF-based parameters optimization for neural networks.

[BibT_eX]

[DOI]

Proceedings of the 42nd IEEE Conference on Decision and Control, 2003

2002

Optimization-based learning with bounded error for feedforward neural networks.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks, 2002

Comparison of worst case errors in linear and neural network approximation.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Theory, 2002

Optimized feedforward neural networks for on-line identification of nonlinear models.

[BibT_eX]

[DOI]

Proceedings of the 41st IEEE Conference on Decision and Control, 2002

Batch-mode identification of black-box models using feedforward neural networks.

[BibT_eX]

[DOI]

Proceedings of the American Control Conference, 2002

2001

Bounds on rates of variable-basis and neural-network approximation.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Theory, 2001

Tight Bounds on Rates of Neural-Network Approximation.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks, 2001

PF-stable estimators for nonlinear systems.

[BibT_eX]

[DOI]

Proceedings of the 6th European Control Conference, 2001

Can we cope with the curse of dimensionality in optimal control by using neural approximators?

[BibT_eX]

[DOI]

Riccardo Zoppoli

Thomas Parisini

Proceedings of the 40th IEEE Conference on Decision and Control, 2001

On the design of approximate state estimators for nonlinear systems.

[BibT_eX]

[DOI]

Proceedings of the 40th IEEE Conference on Decision and Control, 2001

Lp-stable and asymptotic estimators for nonlinear dynamic systems.

[BibT_eX]

[DOI]

Proceedings of the American Control Conference, 2001

2000

Comparison of Rates of Linear and Neural Network Approximation.

[BibT_eX]

[DOI]

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000

On Dimension-Independent Approximation by Neural Networks and Linear Approximators.

[BibT_eX]

[DOI]

Saverio Giulini

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000

On estimators for nonlinear systems in Lp spaces.

[BibT_eX]

[DOI]

Proceedings of the 39th IEEE Conference on Decision and Control, 2000

Approximating networks, dynamic programming and stochastic approximation.

[BibT_eX]

[DOI]

Proceedings of the American Control Conference, 2000

1999

Some Comparisons Between Linear Approximation and Approximation by Neural Networks.

[BibT_eX]

[DOI]

Katerina Hlavácková-Schindler

Proceedings of the International Conference on Artificial Neural Nets and Genetic Algorithms, 1999

1998

Algorithm of Incremental Approximation Using Variation of a Function with Respect to a Subset.

[BibT_eX]

Katerina Hlavácková

Proceedings of the International ICSC / IFAC Symposium on Neural Computation (NC 1998), 1998

Parameter-estimation-based learning for feedforward neural networks: convergence and robustness analysis.

[BibT_eX]

[DOI]