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2020 – today
- 2024
[j31]Alvise De Biasio
, Nicolò Navarin, Dietmar Jannach:
Economic recommender systems - a systematic review. Electron. Commer. Res. Appl. 63: 101352 (2024)- [j30]Alvise De Biasio, Dietmar Jannach, Nicolò Navarin:
Model-based approaches to profit-aware recommendation. Expert Syst. Appl. 249: 123642 (2024) - [j29]Danilo Franco, Vincenzo Stefano D'Amato, Luca Pasa, Nicolò Navarin, Luca Oneto:
Fair graph representation learning: Empowering NIFTY via Biased Edge Dropout and Fair Attribute Preprocessing. Neurocomputing 563: 126948 (2024) - [j28]Nicolò Navarin, Dounia Mulders, Luca Oneto:
Advances in artificial neural networks, machine learning and computational intelligence. Neurocomputing 571: 127098 (2024) - [j27]Giovanni Donghi, Luca Pasa, Luca Oneto, Claudio Gallicchio, Alessio Micheli, Davide Anguita, Alessandro Sperduti, Nicolò Navarin:
Investigating over-parameterized randomized graph networks. Neurocomputing 606: 128281 (2024) - [j26]Luca Pasa, Nicolò Navarin, Wolfgang Erb, Alessandro Sperduti:
A unified framework for backpropagation-free soft and hard gated graph neural networks. Knowl. Inf. Syst. 66(4): 2393-2416 (2024) - [j25]Luca Pasa, Nicolò Navarin, Wolfgang Erb, Alessandro Sperduti:
Empowering Simple Graph Convolutional Networks. IEEE Trans. Neural Networks Learn. Syst. 35(4): 4385-4399 (2024) - [c52]Matteo Zavatteri, Davide Bresolin, Nicolò Navarin:
Automated Synthesis of Certified Neural Networks. ECAI 2024: 1341-1348 - [c51]Giovanni Donghi, Luca Pasa, Alberto Testolin, Marco Zorzi, Alessandro Sperduti, Nicolò Navarin:
Relative Local Signal Strength: The Impact of Normalization on the Analysis of Neuroimaging Data with Deep Learning. ICANN (8) 2024: 373-383 - [c50]Nicolò Navarin, Paolo Frazzetto, Luca Pasa, Pietro Verzelli, Filippo Visentin, Alessandro Sperduti, Cesare Alippi:
Physics-Informed Graph Neural Cellular Automata: an Application to Compartmental Modelling. IJCNN 2024: 1-9 - [c49]Paolo Frazzetto, Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
Beyond the Additive Nodes' Convolutions: a Study on High-Order Multiplicative Integration. SAC 2024: 474-481 - 2023
- [j24]Riccardo Galanti, Massimiliano de Leoni, Merylin Monaro, Nicolò Navarin, Alan Marazzi, Brigida Di Stasi, Stéphanie Maldera:
An explainable decision support system for predictive process analytics. Eng. Appl. Artif. Intell. 120: 105904 (2023) - [j23]Riccardo Galanti, Massimiliano de Leoni, Nicolò Navarin, Alan Marazzi:
Object-centric process predictive analytics. Expert Syst. Appl. 213(Part): 119173 (2023) - [j22]Alvise De Biasio, Andrea Montagna, Fabio Aiolli, Nicolò Navarin:
A systematic review of value-aware recommender systems. Expert Syst. Appl. 226: 120131 (2023) - [j21]Alvise De Biasio, Merylin Monaro, Luca Oneto, Lamberto Ballan, Nicolò Navarin:
On the problem of recommendation for sensitive users and influential items: Simultaneously maintaining interest and diversity. Knowl. Based Syst. 275: 110699 (2023) - [c48]Andrea Montagna, Alvise De Biasio, Nicolò Navarin, Fabio Aiolli:
Graph-based Explainable Recommendation Systems: Are We Rigorously Evaluating Explanations? HCAI4U@CHItaly 2023 - [c47]Davide Bacciu, Federico Errica, Alessio Micheli, Nicolò Navarin, Luca Pasa, Marco Podda, Daniele Zambon:
Graph Representation Learning. ESANN 2023 - [c46]Nicolò Navarin, Luca Pasa, Luca Oneto, Alessandro Sperduti:
An Empirical Study of Over-Parameterized Neural Models based on Graph Random Features. ESANN 2023 - [c45]Valentina Fietta, Nicolò Navarin, Merylin Monaro, Ombretta Gaggi:
Women and Gender Disparities in Computer Science: A Case Study at the University of Padua. GoodIT 2023: 82-91 - [c44]Nicolò Navarin, Luca Pasa, Claudio Gallicchio, Alessandro Sperduti:
An Untrained Neural Model for Fast and Accurate Graph Classification. ICANN (4) 2023: 278-290 - [c43]Paolo Frazzetto, Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
Topology preserving maps as aggregations for Graph Convolutional Neural Networks. SAC 2023: 536-543 - [i16]Davide Rigoni, Nicolò Navarin, Alessandro Sperduti:
RGCVAE: Relational Graph Conditioned Variational Autoencoder for Molecule Design. CoRR abs/2305.11699 (2023) - [i15]Alvise De Biasio, Nicolò Navarin, Dietmar Jannach:
Economic Recommender Systems - A Systematic Review. CoRR abs/2308.11998 (2023) - 2022
- [j20]Merylin Monaro, Stéphanie Maldera, Cristina Scarpazza, Giuseppe Sartori, Nicolò Navarin:
Detecting deception through facial expressions in a dataset of videotaped interviews: A comparison between human judges and machine learning models. Comput. Hum. Behav. 127: 107063 (2022) - [j19]Luca Oneto, Kerstin Bunte, Nicolò Navarin:
Advances in artificial neural networks, machine learning and computational intelligence. Neurocomputing 470: 300-303 (2022) - [j18]Danilo Franco, Nicolò Navarin, Michele Donini, Davide Anguita, Luca Oneto:
Deep fair models for complex data: Graphs labeling and explainable face recognition. Neurocomputing 470: 318-334 (2022) - [j17]Luca Oneto, Nicolò Navarin, Battista Biggio, Federico Errica, Alessio Micheli, Franco Scarselli, Monica Bianchini, Luca Demetrio, Pietro Bongini, Armando Tacchella, Alessandro Sperduti:
Towards learning trustworthily, automatically, and with guarantees on graphs: An overview. Neurocomputing 493: 217-243 (2022) - [j16]Luca Oneto, Nicolò Navarin, Frank-Michael Schleif:
Advances in artificial neural networks, machine learning and computational intelligence. Neurocomputing 507: 311-314 (2022) - [j15]Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
Polynomial-based graph convolutional neural networks for graph classification. Mach. Learn. 111(4): 1205-1237 (2022) - [j14]Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
SOM-based aggregation for graph convolutional neural networks. Neural Comput. Appl. 34(1): 5-24 (2022) - [j13]Merylin Monaro, Emilia I. Barakova, Nicolò Navarin:
Editorial Special Issue Interaction With Artificial Intelligence Systems: New Human-Centered Perspectives and Challenges. IEEE Trans. Hum. Mach. Syst. 52(3): 326-331 (2022) - [j12]Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
Multiresolution Reservoir Graph Neural Network. IEEE Trans. Neural Networks Learn. Syst. 33(6): 2642-2653 (2022) - [c42]Davide Bacciu, Federico Errica, Nicolò Navarin, Luca Pasa, Daniele Zambon:
Deep Learning for Graphs. ESANN 2022 - [c41]Federico Caldart, Luca Pasa, Luca Oneto, Alessandro Sperduti, Nicolò Navarin:
Biased Edge Dropout in NIFTY for Fair Graph Representation Learning. ESANN 2022 - [c40]Luca Pasa, Nicolò Navarin, Wolfgang Erb, Alessandro Sperduti:
Backpropagation-free Graph Neural Networks. ICDM 2022: 388-397 - [c39]Matteo Cardaioli, Alessio Miolla, Mauro Conti, Giuseppe Sartori, Merylin Monaro, Cristina Scarpazza, Nicolò Navarin:
Face the Truth: Interpretable Emotion Genuineness Detection. IJCNN 2022: 1-8 - [c38]Merylin Monaro, Valentina Fietta, Valentina Curró, Giulia Lusetti, Giuseppe Sartori, Nicolò Navarin:
Forged handwriting verification: a public domain dataset for training machine learning models. IJCNN 2022: 1-8 - [c37]Matteo Munari, Luca Pasa, Daniele Zambon, Cesare Alippi, Nicolò Navarin:
Understanding Catastrophic Forgetting of Gated Linear Networks in Continual Learning. IJCNN 2022: 1-8 - [c36]Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
Compact graph neural network models for node classification. SAC 2022: 592-599 - [i14]Riccardo Galanti, Massimiliano de Leoni, Nicolò Navarin, Alan Marazzi:
Object-centric Process Predictive Analytics. CoRR abs/2203.02801 (2022) - [i13]Riccardo Galanti, Massimiliano de Leoni, Merylin Monaro, Nicolò Navarin, Alan Marazzi, Brigida Di Stasi, Stéphanie Maldera:
An Explainable Decision Support System for Predictive Process Analytics. CoRR abs/2207.12782 (2022) - 2021
- [j11]Danilo Franco, Luca Oneto, Nicolò Navarin, Davide Anguita:
Toward Learning Trustworthily from Data Combining Privacy, Fairness, and Explainability: An Application to Face Recognition. Entropy 23(8): 1047 (2021) - [c35]Luca Oneto, Nicolò Navarin, Battista Biggio, Federico Errica, Alessio Micheli, Franco Scarselli, Monica Bianchini, Alessandro Sperduti:
Complex Data: Learning Trustworthily, Automatically, and with Guarantees. ESANN 2021 - [c34]Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
Tangent Graph Convolutional Network. ESANN 2021 - [c33]Danilo Franco, Luca Oneto, Nicolò Navarin, Davide Anguita:
Learn and Visually Explain Deep Fair Models: an Application to Face Recognition. IJCNN 2021: 1-10 - [c32]Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
Simple Multi-resolution Gated GNN. SSCI 2021: 1-7 - [c31]Mirko Polato, Denys Demchenko, Almat Kuanyshkereyev, Nicolò Navarin:
Efficient Multilingual Deep Learning Model for Keyword Categorization. SSCI 2021: 1-8 - [i12]Luca Pasa, Nicolò Navarin, Wolfgang Erb, Alessandro Sperduti:
Simple Graph Convolutional Networks. CoRR abs/2106.05809 (2021) - 2020
- [j10]Nicolò Navarin, Dinh Tran-Van, Alessandro Sperduti:
A framework for the definition of complex structured feature spaces. Neurocomputing 416: 190-201 (2020) - [j9]Manuel Dorado-Moreno, Nicolò Navarin, Pedro Antonio Gutiérrez, Luis Prieto, Alessandro Sperduti, Sancho Salcedo-Sanz, César Hervás-Martínez:
Multi-task learning for the prediction of wind power ramp events with deep neural networks. Neural Networks 123: 401-411 (2020) - [c30]Nicolò Navarin, Dinh Van Tran, Alessandro Sperduti:
Learning Kernel-Based Embeddings in Graph Neural Networks. ECAI 2020: 1387-1394 - [c29]Luca Oneto, Nicolò Navarin, Michele Donini:
Learning Deep Fair Graph Neural Networks. ESANN 2020: 31-36 - [c28]Nicolò Navarin, Wolfgang Erb, Luca Pasa, Alessandro Sperduti:
Linear Graph Convolutional Networks. ESANN 2020: 151-156 - [c27]Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
Deep Recurrent Graph Neural Networks. ESANN 2020: 157-162 - [c26]Davide Rigoni, Nicolò Navarin, Alessandro Sperduti:
A Systematic Assessment of Deep Learning Models for Molecule Generation. ESANN 2020: 547-552 - [c25]Riccardo Galanti, Bernat Coma-Puig, Massimiliano de Leoni, Josep Carmona, Nicolò Navarin:
Explainable Predictive Process Monitoring. ICPM 2020: 1-8 - [c24]Nicolò Navarin, Matteo Cambiaso, Andrea Burattin, Fabrizio Maria Maggi, Luca Oneto, Alessandro Sperduti:
Towards Online Discovery of Data-Aware Declarative Process Models from Event Streams. IJCNN 2020: 1-8 - [c23]Giorgio Nicola, Luca Tagliapietra, Elisa Tosello, Nicolò Navarin, Stefano Ghidoni, Emanuele Menegatti:
Robotic Object Sorting via Deep Reinforcement Learning: a generalized approach. RO-MAN 2020: 1266-1273 - [c22]Davide Rigoni, Nicolò Navarin, Alessandro Sperduti:
Conditional Constrained Graph Variational Autoencoders for Molecule Design. SSCI 2020: 729-736 - [e2]Luca Oneto, Nicolò Navarin, Alessandro Sperduti, Davide Anguita:
Recent Advances in Big Data and Deep Learning, Proceedings of the INNS Big Data and Deep Learning Conference INNSBDDL 2019, held at Sestri Levante, Genova, Italy 16-18 April 2019. Springer 2020, ISBN 978-3-030-16840-7 [contents] - [e1]Luca Oneto, Nicolò Navarin, Alessandro Sperduti, Davide Anguita:
Recent Trends in Learning From Data - Tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL 2019). Studies in Computational Intelligence 896, Springer 2020, ISBN 978-3-030-43882-1 [contents] - [i11]Riccardo Galanti, Bernat Coma-Puig, Massimiliano de Leoni, Josep Carmona, Nicolò Navarin:
Explainable Predictive Process Monitoring. CoRR abs/2008.01807 (2020) - [i10]Davide Rigoni, Nicolò Navarin, Alessandro Sperduti:
A Systematic Assessment of Deep Learning Models for Molecule Generation. CoRR abs/2008.09168 (2020) - [i9]Davide Rigoni, Nicolò Navarin, Alessandro Sperduti:
Conditional Constrained Graph Variational Autoencoders for Molecule Design. CoRR abs/2009.00725 (2020)
2010 – 2019
- 2019
- [c21]Nicolò Navarin, Dinh Van Tran, Alessandro Sperduti:
On the definition of complex structured feature spaces. ESANN 2019 - [c20]Nicolò Navarin, Dinh Van Tran, Alessandro Sperduti:
Universal Readout for Graph Convolutional Neural Networks. IJCNN 2019: 1-7 - [c19]Luca Oneto, Nicolò Navarin, Alessandro Sperduti, Davide Anguita:
Introduction. INNSBDDL (Tutorials) 2019: 1-4 - 2018
- [j8]Guido Zampieri, Dinh Tran-Van, Michele Donini, Nicolò Navarin, Fabio Aiolli, Alessandro Sperduti, Giorgio Valle:
Scuba: scalable kernel-based gene prioritization. BMC Bioinform. 19(1): 23:1-23:12 (2018) - [j7]Luca Oneto, Nicolò Navarin, Alessandro Sperduti, Davide Anguita:
Multilayer Graph Node Kernels: Stacking While Maintaining Convexity. Neural Process. Lett. 48(2): 649-667 (2018) - [j6]Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti:
Tree-Based Kernel for Graphs With Continuous Attributes. IEEE Trans. Neural Networks Learn. Syst. 29(7): 3270-3276 (2018) - [j5]Luca Oneto, Nicolò Navarin, Michele Donini, Sandro Ridella, Alessandro Sperduti, Fabio Aiolli, Davide Anguita:
Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels. IEEE Trans. Neural Networks Learn. Syst. 29(10): 4660-4671 (2018) - [c18]Luca Oneto, Nicolò Navarin, Michele Donini, Davide Anguita:
Emerging trends in machine learning: beyond conventional methods and data. ESANN 2018 - [c17]Dinh Tran-Van, Nicolò Navarin, Alessandro Sperduti:
DEEP: decomposition feature enhancement procedure for graphs. ESANN 2018 - [c16]Nicolò Navarin, Giovanni Da San Martino, Alessandro Sperduti:
Extreme Graph Kernels for Online Learning on a Memory Budget. IJCNN 2018: 1-8 - [c15]Dinh Van Tran, Nicolò Navarin, Alessandro Sperduti:
On Filter Size in Graph Convolutional Networks. SSCI 2018: 1534-1541 - [i8]Nicolò Navarin, Dinh Van Tran, Alessandro Sperduti:
Pre-training Graph Neural Networks with Kernels. CoRR abs/1811.06930 (2018) - [i7]Dinh Van Tran, Nicolò Navarin, Alessandro Sperduti:
On Filter Size in Graph Convolutional Networks. CoRR abs/1811.10435 (2018) - 2017
- [j4]Nicolò Navarin, Fabrizio Costa:
An efficient graph kernel method for non-coding RNA functional prediction. Bioinform. 33(17): 2642-2650 (2017) - [j3]Luca Oneto, Nicolò Navarin, Michele Donini, Alessandro Sperduti, Fabio Aiolli, Davide Anguita:
Measuring the expressivity of graph kernels through Statistical Learning Theory. Neurocomputing 268: 4-16 (2017) - [c14]Michele Donini, Nicolò Navarin, Ivano Lauriola, Fabio Aiolli, Fabrizio Costa:
Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning. ESANN 2017 - [c13]Nicolò Navarin, Alessandro Sperduti:
Approximated Neighbours MinHash Graph Node Kernel. ESANN 2017 - [c12]Luca Oneto, Nicolò Navarin, Alessandro Sperduti, Davide Anguita:
Deep graph node kernels: A convex approach. IJCNN 2017: 316-323 - [c11]Nicolò Navarin, Beatrice Vincenzi, Mirko Polato, Alessandro Sperduti:
LSTM networks for data-aware remaining time prediction of business process instances. SSCI 2017: 1-7 - [i6]Nicolò Navarin, Beatrice Vincenzi, Mirko Polato, Alessandro Sperduti:
LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances. CoRR abs/1711.03822 (2017) - 2016
- [j2]Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti:
Ordered Decompositional DAG kernels enhancements. Neurocomputing 192: 92-103 (2016) - [j1]Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti:
An empirical study on budget-aware online kernel algorithms for streams of graphs. Neurocomputing 216: 163-182 (2016) - [c10]Luca Oneto, Nicolò Navarin, Michele Donini, Fabio Aiolli, Davide Anguita:
Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints. ESANN 2016 - [c9]Luca Oneto, Nicolò Navarin, Michele Donini, Alessandro Sperduti, Fabio Aiolli, Davide Anguita:
Measuring the Expressivity of Graph Kernels through the Rademacher Complexity. ESANN 2016 - [c8]Carlo M. Massimo, Nicolò Navarin, Alessandro Sperduti:
Hyper-Parameter Tuning for Graph Kernels via Multiple Kernel Learning. ICONIP (2) 2016: 214-223 - 2015
- [c7]Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti:
Exploiting the ODD framework to define a novel effective graph kernel. ESANN 2015 - [c6]Nicolò Navarin, Alessandro Sperduti, Riccardo Tesselli:
Extending Local Features with Contextual Information in Graph Kernels. ICONIP (4) 2015: 271-279 - [c5]Fabio Aiolli, Michele Donini, Nicolò Navarin, Alessandro Sperduti:
Multiple Graph-Kernel Learning. SSCI 2015: 1607-1614 - [i5]Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti:
An Empirical Study on Budget-Aware Online Kernel Algorithms for Streams of Graphs. CoRR abs/1507.02158 (2015) - [i4]Nicolò Navarin, Alessandro Sperduti, Riccardo Tesselli:
Extending local features with contextual information in graph kernels. CoRR abs/1507.02186 (2015) - [i3]Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti:
Ordered Decompositional DAG Kernels Enhancements. CoRR abs/1507.03372 (2015) - [i2]Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti:
A tree-based kernel for graphs with continuous attributes. CoRR abs/1509.01116 (2015) - [i1]Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti:
Graph Kernels exploiting Weisfeiler-Lehman Graph Isomorphism Test Extensions. CoRR abs/1509.06589 (2015) - 2014
- [b1]Nicolò Navarin:
Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction. University of Bologna, Italy, 2014 - [c4]Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti:
Graph Kernels Exploiting Weisfeiler-Lehman Graph Isomorphism Test Extensions. ICONIP (2) 2014: 93-100 - 2013
- [c3]Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti:
A Lossy Counting Based Approach for Learning on Streams of Graphs on a Budget. IJCAI 2013: 1294-1301 - 2012
- [c2]Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti:
A memory efficient graph kernel. IJCNN 2012: 1-7 - [c1]Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti:
A Tree-Based Kernel for Graphs. SDM 2012: 975-986
Coauthor Index
[j27] [j26] [j25] [c51] [c50] [c49] [c46] [c44] [c43] [i16] [j17] [j15] [j14] [j12] [c41] [c40] [c36] [c35] [c34] [c32] [i12] [j10] [j9] [c30] [c28] [c27] [c26] [c24] [c22] [e2] [e1] [i10] [i9] [c21] [c20] [c19] [j8] [j7] [j6] [j5] [c17] [c16] [c15] [i8] [i7] [j3] [c13] [c12] [c11] [i6] [j2] [j1] [c9] [c8] [c7] [c6] [c5] [i5] [i4] [i3] [i2] [i1] [c4] [c3] [c2] [c1]
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