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Giorgio Gnecco
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2020 – today
- 2024
- [j67]Marco Gregnanin, Yanyi Zhang, Johannes De Smedt, Giorgio Gnecco, Maurizio Parton:
Signature-based portfolio allocation: a network approach. Appl. Netw. Sci. 9(1): 54 (2024) - [j66]Francesco Biancalani, Giorgio Gnecco
, Rodolfo Metulini, Massimo Riccaboni:
Prediction of annual CO2 emissions at the country and sector levels, based on a matrix completion optimization problem. Optim. Lett. 18(9): 2203-2219 (2024) - [j65]Emanuele Agrimi, Chiara Battaglini, Davide Bottari, Giorgio Gnecco
Game accessibility for visually impaired people: a review. Soft Comput. 28(17-18): 10475-10489 (2024) - [c31]Antonio Camurri
Iterative Design of Two Art-Inspired Experimental Scenarios for Collecting Expressive Movement Data of Individuals and Groups. AVI 2024: 92:1-92:3 - 2023
- [j64]Rodolfo Metulini
Measuring players' importance in basketball using the generalized Shapley value. Ann. Oper. Res. 325(1): 441-465 (2023) - [j63]Daniele Masti
Counter-Example Guided Inductive Synthesis of Control Lyapunov Functions for Uncertain Systems. IEEE Control. Syst. Lett. 7: 2047-2052 (2023) - [c30]Marco Gregnanin, Johannes De Smedt, Giorgio Gnecco, Maurizio Parton:
Signature-Based Community Detection for Time Series. COMPLEX NETWORKS (2) 2023: 146-158 - [c29]Giorgio Gnecco, Martina Fausto, Gabriele Romano, Gualtiero Volpe, Antonio Camurri:
Improving Output Visualization of an Algorithm for the Automated Detection of the Perceived Origin of Movement. INTETAIN 2023: 96-106 - [c28]Giorgio Gnecco
Increasing Accessibility of Online Board Games to Visually Impaired People via Machine Learning and Textual/Audio Feedback: The Case of "Quantik". INTETAIN 2023: 167-177 - [i10]Daniele Masti, Filippo Fabiani, Giorgio Gnecco, Alberto Bemporad:
Counter-example guided inductive synthesis of control Lyapunov functions for uncertain systems. CoRR abs/2303.10024 (2023) - [i9]Marco Gregnanin, Johannes De Smedt, Giorgio Gnecco, Maurizio Parton:
Signature-Based Community Detection for Time Series. CoRR abs/2311.05986 (2023) - 2022
- [j62]Giorgio Gnecco
Welfare and research and development incentive effects of uniform and differential pricing schemes. Comput. Manag. Sci. 19(2): 229-268 (2022) - [j61]Giorgio Gnecco, Yuval Hadas
A game-theoretic approach for reliability evaluation of public transportation transfers with stochastic features. EURO J. Transp. Logist. 11: 100090 (2022) - [j60]Francesco Biancalani
Price-volume agreements: A one principal/two agents model. Eur. J. Oper. Res. 300(1): 296-309 (2022) - [j59]Falco J. Bargagli Stoffi, Gustavo Cevolani
Simple Models in Complex Worlds: Occam's Razor and Statistical Learning Theory. Minds Mach. 32(1): 13-42 (2022) - [c27]Ming Li
Deeper Insights into Neural Nets with Random Weights. AI 2022: 129-140 - [c26]Francesco Biancalani, Giorgio Gnecco, Rodolfo Metulini
Matrix Completion for the Prediction of Yearly Country and Industry-Level CO2 Emissions. LOD (1) 2022: 14-19 - [i8]Rodolfo Metulini, Giorgio Gnecco, Francesco Biancalani, Massimo Riccaboni:
Hierarchical Clustering and Matrix Completion for the Reconstruction of World Input-Output Tables. CoRR abs/2203.08819 (2022) - 2021
- [j58]Giorgio Gnecco
Convex combination of data matrices: PCA perturbation bounds for multi-objective optimal design of mechanical metafilters. Math. Found. Comput. 4(4): 253 (2021) - [j57]Giorgio Gnecco
Optimal data collection design in machine learning: the case of the fixed effects generalized least squares panel data model. Mach. Learn. 110(7): 1549-1584 (2021) - [j56]Pietro Lenarda
Parameter estimation in a 3-parameter p-star random graph model. Networks 77(3): 403-420 (2021) - [j55]Mauro Passacantando
Braess' paradox: A cooperative game-theoretic point of view. Networks 78(3): 264-283 (2021) - [j54]Giorgio Gnecco
On the trade-off between number of examples and precision of supervision in machine learning problems. Optim. Lett. 15(5): 1711-1733 (2021) - [c25]Giorgio Gnecco, Andrea Bacigalupo:
On Principal Component Analysis of the Convex Combination of Two Data Matrices and Its Application to Acoustic Metamaterial Filters. LOD 2021: 119-123 - [i7]Giorgio Gnecco, Andrea Bacigalupo, Francesca Fantoni, Daniela Selvi:
Principal Component Analysis Applied to Gradient Fields in Band Gap Optimization Problems for Metamaterials. CoRR abs/2104.02588 (2021) - [i6]Giorgio Gnecco, Andrea Bacigalupo:
On principal component analysis of the convex combination of two data matrices and its application to acoustic metamaterial filters. CoRR abs/2104.07054 (2021) - [i5]Giorgio Gnecco, Federico Nutarelli, Massimo Riccaboni:
Matrix Completion of World Trade. CoRR abs/2109.03930 (2021) - 2020
- [j53]Fouad El Ouardighi
Transboundary pollution control and environmental absorption efficiency management. Ann. Oper. Res. 287(2): 653-681 (2020) - [j52]Falco J. Bargagli Stoffi
Causal tree with instrumental variable: an extension of the causal tree framework to irregular assignment mechanisms. Int. J. Data Sci. Anal. 9(3): 315-337 (2020) - [j51]Andrea Bacigalupo, Giorgio Gnecco
Machine-Learning Techniques for the Optimal Design of Acoustic Metamaterials. J. Optim. Theory Appl. 187(3): 630-653 (2020) - [j50]Giorgio Gnecco
Optimal trade-off between sample size, precision of supervision, and selection probabilities for the unbalanced fixed effects panel data model. Soft Comput. 24(21): 15937-15949 (2020) - [j49]Ksenia Kolykhalova, Giorgio Gnecco
Automated Analysis of the Origin of Movement: An Approach Based on Cooperative Games on Graphs. IEEE Trans. Hum. Mach. Syst. 50(6): 550-560 (2020) - [c24]Olga Matthiopoulou, Benoît G. Bardy, Giorgio Gnecco
A Computational Method to Automatically Detect the Perceived Origin of Full-Body Human Movement and its Propagation. ICMI Companion 2020: 449-453 - [c23]Giorgio Gnecco
Machine Learning Application to Family Business Status Classification. LOD (1) 2020: 25-36 - [c22]Falco J. Bargagli Stoffi, Gustavo Cevolani, Giorgio Gnecco
Should Simplicity Be Always Preferred to Complexity in Supervised Machine Learning? LOD (1) 2020: 55-59
2010 – 2019
- 2019
- [j48]Giorgio Gnecco
Some properties of transportation network cooperative games. Networks 74(2): 161-173 (2019) - [c21]Giorgio Gnecco, Federico Nutarelli:
On the Trade-Off Between Number of Examples and Precision of Supervision in Regression. INNSBDDL 2019: 1-6 - [c20]Giorgio Gnecco
Optimal Trade-Off Between Sample Size and Precision of Supervision for the Fixed Effects Panel Data Model. LOD 2019: 531-542 - [i4]Falco J. Bargagli Stoffi, Kristof De Witte, Giorgio Gnecco:
Heterogeneous causal effects with imperfect compliance: a novel Bayesian machine learning approach. CoRR abs/1905.12707 (2019) - [i3]Andrea Bacigalupo, Giorgio Gnecco, Marco Lepidi, Luigi Gambarotta:
Machine-learning techniques for the optimal design of acoustic metamaterials. CoRR abs/1908.10645 (2019) - 2018
- [j47]Giorgio Gnecco
Strong Convexity and Smoothness of Solutions to Geometric Optics Problems via Dynamic Programming. IEEE Control. Syst. Lett. 2(3): 549-554 (2018) - [j46]Giorgio Gnecco
Neural approximations in discounted infinite-horizon stochastic optimal control problems. Eng. Appl. Artif. Intell. 74: 294-302 (2018) - [j45]Giorgio Gnecco
A Comparison of Game-Theoretic Models for Parallel Trade. IGTR 20(3): 1850003:1-1850003:57 (2018) - [j44]Giorgio Gnecco
Symmetric and antisymmetric properties of solutions to kernel-based machine learning problems. Neurocomputing 306: 141-159 (2018) - [j43]Stefano Sebastio
A green policy to schedule tasks in a distributed cloud. Optim. Lett. 12(7): 1535-1551 (2018) - [c19]Falco Joannes Bargagli Stoffi, Giorgio Gnecco
Estimating Heterogeneous Causal Effects in the Presence of Irregular Assignment Mechanisms. DSAA 2018: 1-10 - [i2]Falco J. Bargagli Stoffi, Giorgio Gnecco:
Estimating Heterogeneous Causal Effects in the Presence of Irregular Assignment Mechanisms. CoRR abs/1808.04281 (2018) - 2017
- [j42]Stefano Sebastio, Giorgio Gnecco
Optimal distributed task scheduling in volunteer clouds. Comput. Oper. Res. 81: 231-246 (2017) - [j41]Giorgio Gnecco
LQG Online Learning. Neural Comput. 29(8): 2203-2291 (2017) - [j40]Giorgio Gnecco
Symmetry and antisymmetry properties of optimal solutions to regression problems. Optim. Lett. 11(7): 1427-1442 (2017) - [j39]Luca Boero
A theoretical analysis of buffer occupancy for Intermittently-Connected Networks. Perform. Evaluation 115: 108-131 (2017) - [j38]Mohammed M. Abdelsamea
A SOM-based Chan-Vese model for unsupervised image segmentation. Soft Comput. 21(8): 2047-2067 (2017) - [j37]Giorgio Gnecco
Supervised and semi-supervised classifiers for the detection of flood-prone areas. Soft Comput. 21(13): 3673-3685 (2017) - [c18]Ksenia Kolykhalova, Giorgio Gnecco
Graph-restricted game approach for investigating human movement qualities. MOCO 2017: 30:1-30:4 - 2016
- [j36]Rita Morisi, Giorgio Gnecco
A hierarchical consensus method for the approximation of the consensus state, based on clustering and spectral graph theory. Eng. Appl. Artif. Intell. 56: 157-174 (2016) - [j35]Giorgio Gnecco
On the Curse of Dimensionality in the Ritz Method. J. Optim. Theory Appl. 168(2): 488-509 (2016) - [j34]Floriane Dardard, Giorgio Gnecco
Automatic Classification of Leading Interactions in a String Quartet. ACM Trans. Interact. Intell. Syst. 6(1): 5:1-5:27 (2016) - [c17]Giorgio Gnecco, Marco Gori, Stefano Melacci, Marcello Sanguineti:
Learning with hard constraints as a limit case of learning with soft constraints. ESANN 2016 - [c16]Andrea Bacigalupo, Giorgio Gnecco
Design of Acoustic Metamaterials Through Nonlinear Programming. MOD 2016: 170-181 - [i1]Giorgio Gnecco:
Symmetric and antisymmetric properties of solutions to kernel-based machine learning problems. CoRR abs/1606.08501 (2016) - 2015
- [j33]Mohammed M. Abdelsamea, Giorgio Gnecco, Mohamed Medhat Gaber, Eyad Elyan:
On the Relationship between Variational Level Set-Based and SOM-Based Active Contours. Comput. Intell. Neurosci. 2015: 109029:1-109029:19 (2015) - [j32]Mohammed M. Abdelsamea
An efficient Self-Organizing Active Contour model for image segmentation. Neurocomputing 149: 820-835 (2015) - [j31]Donald Glowinski
Expressive non-verbal interaction in a string quartet: an analysis through head movements. J. Multimodal User Interfaces 9(1): 55-68 (2015) - [j30]Marco Cello, Giorgio Gnecco
Narrowing the Search for Optimal Call-Admission Policies Via a Nonlinear Stochastic Knapsack Model. J. Optim. Theory Appl. 164(3): 819-841 (2015) - [j29]Giorgio Gnecco
Foundations of Support Constraint Machines. Neural Comput. 27(2): 388-480 (2015) - [j28]Giorgio Gnecco
Learning With Mixed Hard/Soft Pointwise Constraints. IEEE Trans. Neural Networks Learn. Syst. 26(9): 2019-2032 (2015) - [j27]Giorgio Gnecco
Sparse Solutions to the Average Consensus Problem via Various Regularizations of the Fastest Mixing Markov-Chain Problem. IEEE Trans. Netw. Sci. Eng. 2(3): 97-111 (2015) - [c15]Giorgio Gnecco
Online learning as an LQG optimal control problem with random matrices. ECC 2015: 2482-2489 - [c14]Rita Morisi, Giorgio Gnecco
Binary and Multi-class Parkinsonian Disorders Classification Using Support Vector Machines. IbPRIA 2015: 379-386 - 2014
- [j26]Mauro Gaggero
Approximate dynamic programming for stochastic N-stage optimization with application to optimal consumption under uncertainty. Comput. Optim. Appl. 58(1): 31-85 (2014) - [j25]Marco Cello, Giorgio Gnecco
Evaluation of the Average Packet Delivery Delay in Highly-Disrupted Networks: The DTN and IP-like Protocol Cases. IEEE Commun. Lett. 18(3): 519-522 (2014) - [j24]Giorgio Gnecco
On the detection of the level of attention in an orchestra through head movements. Int. J. Arts Technol. 7(4): 316-338 (2014) - [j23]Giorgio Gnecco
A theoretical framework for supervised learning from regions. Neurocomputing 129: 25-32 (2014) - [j22]Giorgio Gnecco
Approximation and Estimation Bounds for Subsets of Reproducing Kernel Kreǐn Spaces. Neural Process. Lett. 39(2): 137-153 (2014) - [c13]Giorgio Gnecco
Sparse solutions to the average consensus problem via l1-norm regularization of the fastest mixing Markov-chain problem. CDC 2014: 2228-2233 - [c12]Mohammed M. Abdelsamea
A Concurrent SOM-Based Chan-Vese Model for Image Segmentation. WSOM 2014: 199-208 - [c11]Mohammed M. Abdelsamea
A Survey of SOM-Based Active Contour Models for Image Segmentation. WSOM 2014: 293-302 - 2013
- [j21]Mauro Gaggero
Dynamic Programming and Value-Function Approximation in Sequential Decision Problems: Error Analysis and Numerical Results. J. Optim. Theory Appl. 156(2): 380-416 (2013) - [j20]Giorgio Gnecco
Learning with Boundary Conditions. Neural Comput. 25(4): 1029-1106 (2013) - [j19]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) - [j18]Marco Cello, Giorgio Gnecco
Optimality Conditions for Coordinate-Convex Policies in CAC With Nonlinear Feasibility Boundaries. IEEE/ACM Trans. Netw. 21(5): 1363-1377 (2013) - [c10]Donald Glowinski, Giorgio Gnecco
Expressive Non-verbal Interaction in String Quartet. ACII 2013: 233-238 - [c9]Giorgio Gnecco
Towards Automated Analysis of Joint Music Performance in the Orchestra. ArtsIT 2013: 120-127 - [c8]Giorgio Gnecco
Learning with Hard Constraints. ICANN 2013: 146-153 - 2012
- [j17]Giorgio Gnecco
Accuracy of approximations of solutions to Fredholm equations by kernel methods. Appl. Math. Comput. 218(14): 7481-7497 (2012) - [j16]Marco Cello, Giorgio Gnecco
A Model of Buffer Occupancy for ICNs. IEEE Commun. Lett. 16(6): 862-865 (2012) - [j15]Giorgio Gnecco
A Comparison between Fixed-Basis and Variable-Basis Schemes for Function Approximation and Functional Optimization. J. Appl. Math. 2012: 806945:1-806945:17 (2012) - [j14]Giorgio Gnecco
New insights into Witsenhausen's counterexample. Optim. Lett. 6(7): 1425-1446 (2012) - [j13]Giorgio Gnecco
Suboptimal Solutions to Team Optimization Problems with Stochastic Information Structure. SIAM J. Optim. 22(1): 212-243 (2012) - [c7]Mauro Gaggero
Approximation structures with moderate complexity in functional optimization and dynamic programming. CDC 2012: 1902-1908 - [c6]Marco Cello, Giorgio Gnecco
An application to two-hop forwarding of a model of buffer occupancy in ICNs. SoSE 2012: 491-496 - 2011
- [j12]Giorgio Gnecco
Functional optimization by variable-basis approximation schemes. 4OR 9(1): 103-106 (2011) - [j11]Marco Cello, Giorgio Gnecco
CAC with Nonlinearly-Constrained Feasibility Regions. IEEE Commun. Lett. 15(4): 467-469 (2011) - [j10]Giorgio Gnecco
Some comparisons of complexity in dictionary-based and linear computational models. Neural Networks 24(2): 171-182 (2011) - [j9]Giorgio Gnecco
Can dictionary-based computational models outperform the best linear ones? Neural Networks 24(8): 881-887 (2011) - [j8]Giorgio Gnecco
Team optimization problems with Lipschitz continuous strategies. Optim. Lett. 5(2): 333-346 (2011) - [j7]Giorgio Gnecco
On a Variational Norm Tailored to Variable-Basis Approximation Schemes. IEEE Trans. Inf. Theory 57(1): 549-558 (2011) - [c5]Marco Cello, Giorgio Gnecco, Mario Marchese, Marcello Sanguineti:
A Generalized Stochastic Knapsack Problem with Application in Call Admission Control. CTW 2011: 105-108 - [c4]Giorgio Gnecco
Bounds for Approximate Solutions of Fredholm Integral Equations Using Kernel Networks. ICANN (1) 2011: 126-133 - [c3]Mario Marchese
Structural properties of optimal coordinate-convex policies for CAC with nonlinearly-constrained feasibility regions. INFOCOM 2011: 466-470 - 2010
- [j6]Giorgio Gnecco
On spectral windows in supervised learning from data. Inf. Process. Lett. 110(23): 1031-1036 (2010) - [j5]Angelo Alessandri
Minimizing Sequences for a Family of Functional Optimal Estimation Problems. J. Optim. Theory Appl. 147(2): 243-262 (2010) - [j4]Giorgio Gnecco
Regularization Techniques and Suboptimal Solutions to Optimization Problems in Learning from Data. Neural Comput. 22(3): 793-829 (2010) - [j3]Giorgio Gnecco
Error bounds for suboptimal solutions to kernel principal component analysis. Optim. Lett. 4(2): 197-210 (2010) - [c2]Giorgio Gnecco
Some Comparisons of Model Complexity in Linear and Neural-Network Approximation. ICANN (3) 2010: 358-367 - [c1]Giorgio Gnecco
Smooth Optimal Decision Strategies for Static Team Optimization Problems and Their Approximations. SOFSEM 2010: 440-451
2000 – 2009
- 2009
- [j2]Giorgio Gnecco
The weight-decay technique in learning from data: an optimization point of view. Comput. Manag. Sci. 6(1): 53-79 (2009) - [j1]Giorgio Gnecco
Accuracy of suboptimal solutions to kernel principal component analysis. Comput. Optim. Appl. 42(2): 265-287 (2009) - [p1]Giorgio Gnecco
Regularization and Suboptimal Solutions in Learning from Data. Innovations in Neural Information Paradigms and Applications 2009: 113-154
Coauthor Index
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last updated on 2024-11-07 20:28 CET by the dblp team
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