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ORCIDRoberto Esposito
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2020 – today
- 2024
[j15]Bruno Casella
, Roberto Esposito, Antonio Sciarappa, Carlo Cavazzoni, Marco Aldinucci:
Experimenting With Normalization Layers in Federated Learning on Non-IID Scenarios. IEEE Access 12: 47961-47971 (2024)- [j14]Mario Alviano, Francesco Bartoli, Marco Botta, Roberto Esposito, Laura Giordano, Daniele Theseider Dupré:
A preferential interpretation of MultiLayer Perceptrons in a conditional logic with typicality. Int. J. Approx. Reason. 164: 109065 (2024) - [j13]Roberto Esposito, Mattia Cerrato, Marco Locatelli:
Partitioned least squares. Mach. Learn. 113(9): 6839-6869 (2024) - [c46]Lorenzo Paletto, Valerio Basile, Roberto Esposito:
Label Augmentation for Zero-Shot Hierarchical Text Classification. ACL (1) 2024: 7697-7706 - [c45]Mario Alviano, Marco Botta, Roberto Esposito, Laura Giordano, Daniele Theseider Dupré:
Many-valued Temporal Weighted Knowledge Bases with Typicality for Explainability. CILC 2024 - [c44]Mario Alviano, Francesco Bartoli, Marco Botta, Roberto Esposito, Laura Giordano, Daniele Theseider Dupré:
Verifying Properties of a MultiLayer Network for the Recognition of Basic Emotions in a Conditional DL with Typicality (Extended Abstract). Description Logics 2024 - [i8]Lorenzo Sciandra, Roberto Esposito, Andrea Cesare Grosso, Laura Sacerdote, Cristina Zucca:
Graph Convolutional Branch and Bound. CoRR abs/2406.03099 (2024) - 2023
- [j12]Shuyi Yang, Mattia Cerrato, Dino Ienco, Ruggero G. Pensa, Roberto Esposito:
FairSwiRL: fair semi-supervised classification with representation learning. Mach. Learn. 112(9): 3051-3076 (2023) - [c43]Mattia Cerrato, Marius Köppel, Roberto Esposito, Stefan Kramer:
Invariant Representations with Stochastically Quantized Neural Networks. AAAI 2023: 6962-6970 - [c42]Gianluca Mittone, Nicolò Tonci, Robert Birke, Iacopo Colonnelli, Doriana Medic, Andrea Bartolini, Roberto Esposito, Emanuele Parisi, Francesco Beneventi, Mirko Polato, Massimo Torquati, Luca Benini, Marco Aldinucci:
Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning. CF 2023: 73-83 - [c41]Samuele Fonio, Lorenzo Paletto, Mattia Cerrato, Dino Ienco, Roberto Esposito:
Hierarchical priors for Hyperspherical Prototypical Networks. ESANN 2023 - [c40]Yasir Arfat, Gianluca Mittone, Iacopo Colonnelli, Fabrizio D'Ascenzo, Roberto Esposito, Marco Aldinucci:
Pooling critical datasets with Federated Learning. PDP 2023: 329-337 - [c39]Roberto Esposito, Mirko Polato, Marco Aldinucci:
Boosting Methods for Federated Learning. SEBD 2023: 439-448 - [c38]Shuyi Yang, Mattia Cerrato, Dino Ienco, Ruggero G. Pensa, Roberto Esposito:
Fair Semi-supervised Representation Learning for Tabular Data Classification. SEBD 2023: 488-496 - [c37]Mirko Polato, Roberto Esposito, Walter Riviera, Zenglin Xu, Irwin King:
1st Workshop on Federated Learning Technologies. WWW (Companion Volume) 2023: 1150 - [i7]Gianluca Mittone, Nicolò Tonci, Robert Birke, Iacopo Colonnelli, Doriana Medic, Andrea Bartolini, Roberto Esposito, Emanuele Parisi, Francesco Beneventi, Mirko Polato, Massimo Torquati, Luca Benini, Marco Aldinucci:
Experimenting with Emerging ARM and RISC-V Systems for Decentralised Machine Learning. CoRR abs/2302.07946 (2023) - [i6]Bruno Casella, Roberto Esposito, Antonio Sciarappa, Carlo Cavazzoni, Marco Aldinucci:
Experimenting with Normalization Layers in Federated Learning on non-IID scenarios. CoRR abs/2303.10630 (2023) - [i5]Bruno Casella, Roberto Esposito, Carlo Cavazzoni, Marco Aldinucci:
Benchmarking FedAvg and FedCurv for Image Classification Tasks. CoRR abs/2303.17942 (2023) - [i4]Mario Alviano, Francesco Bartoli, Marco Botta, Roberto Esposito, Laura Giordano, Daniele Theseider Dupré:
A preferential interpretation of MultiLayer Perceptrons in a conditional logic with typicality. CoRR abs/2305.00304 (2023) - 2022
- [j11]Amedeo Racanati, Roberto Esposito, Dino Ienco:
Dealing With Multipositive Unlabeled Learning Combining Metric Learning and Deep Clustering. IEEE Access 10: 51839-51849 (2022) - [j10]Giovanni Agosta, Marco Aldinucci, Carlos Álvarez, Roberto Ammendola, Yasir Arfat, Olivier Beaumont, Massimo Bernaschi, Andrea Biagioni, Tommaso Boccali, Bérenger Bramas, Carlo Brandolese, Barbara Cantalupo, Mauro Carrozzo, Daniele Cattaneo, Alessandro Celestini, Massimo Celino, Iacopo Colonnelli, Paolo Cretaro, Pasqua D'Ambra, Marco Danelutto, Roberto Esposito, Lionel Eyraud-Dubois, Antonio Filgueras, William Fornaciari, Ottorino Frezza, Andrea Galimberti, Francesco Giacomini, Brice Goglin, Daniele Gregori, Abdou Guermouche, Francesco Iannone, Michal Kulczewski, Francesca Lo Cicero, Alessandro Lonardo, Alberto Riccardo Martinelli, Michele Martinelli, Xavier Martorell, Giuseppe Massari, Simone Montangero, Gianluca Mittone, Raymond Namyst, Ariel Oleksiak, Paolo Palazzari, Pier Stanislao Paolucci, Federico Reghenzani, Cristian Rossi, Sergio Saponara, Francesco Simula, Federico Terraneo, Samuel Thibault, Massimo Torquati, Matteo Turisini, Piero Vicini, Miquel Vidal, Davide Zoni, Giuseppe Zummo:
Towards EXtreme scale technologies and accelerators for euROhpc hw/Sw supercomputing applications for exascale: The TEXTAROSSA approach. Microprocess. Microsystems 95: 104679 (2022) - [c36]Mario Alviano, Francesco Bartoli, Marco Botta, Roberto Esposito, Laura Giordano, Valentina Gliozzi, Daniele Theseider Dupré:
Towards a Conditional and Multi-preferential Approach to Explainability of Neural Network Models in Computational Logic (Extended Abstract). XAI.it@AI*IA 2022: 64-72 - [c35]Francesco Bartoli, Marco Botta, Roberto Esposito, Laura Giordano, Daniele Theseider Dupré:
Model Checking Verification of MultiLayer Perceptrons in Datalog: a Many-valued Approach with Typicality. Datalog 2022: 54-67 - [c34]Mirko Polato, Roberto Esposito, Marco Aldinucci:
Boosting the Federation: Cross-Silo Federated Learning without Gradient Descent. IJCNN 2022: 1-10 - [c33]Bruno Casella, Roberto Esposito, Carlo Cavazzoni, Marco Aldinucci:
Benchmarking FedAvg and FedCurv for Image Classification Tasks. itaDATA 2022: 99-110 - [c32]Francesco Bartoli, Marco Botta, Roberto Esposito, Laura Giordano, Valentina Gliozzi, Daniele Theseider Dupré:
From Common Sense Reasonig to Neural Network Models: a Conditional and Multi-preferential Approach for Explainability and Neuro-Symbolic Integration. FCR@KI 2022: 66-78 - [i3]Mattia Cerrato, Alesia Vallenas Coronel, Marius Köppel, Alexander Segner, Roberto Esposito, Stefan Kramer:
Fair Interpretable Representation Learning with Correction Vectors. CoRR abs/2202.03078 (2022) - [i2]Mattia Cerrato, Marius Köppel, Roberto Esposito, Stefan Kramer:
Invariant Representations with Stochastically Quantized Neural Networks. CoRR abs/2208.02656 (2022) - 2021
- [j9]Shuyi Yang, Dino Ienco, Roberto Esposito, Ruggero G. Pensa:
ESA☆: A generic framework for semi-supervised inductive learning. Neurocomputing 447: 102-117 (2021) - [j8]Alessandro Mazzei, Mattia Cerrato, Roberto Esposito, Valerio Basile:
Ranking Algorithms for Word Ordering in Surface Realization. Inf. 12(8): 337 (2021) - [j7]Marco Botta, Davide Cavagnino, Roberto Esposito:
NeuNAC: A novel fragile watermarking algorithm for integrity protection of neural networks. Inf. Sci. 576: 228-241 (2021) - [c31]Marco Aldinucci, Giovanni Agosta, Antonio Andreini, Claudio Agostino Ardagna, Andrea Bartolini, Alessandro Cilardo, Biagio Cosenza, Marco Danelutto, Roberto Esposito, William Fornaciari, Roberto Giorgi, Davide Lengani, Raffaele Montella, Mauro Olivieri, Sergio Saponara, Daniele Simoni, Massimo Torquati:
The Italian research on HPC key technologies across EuroHPC. CF 2021: 178-184 - [c30]Giovanni Agosta, Daniele Cattaneo, William Fornaciari, Andrea Galimberti, Giuseppe Massari, Federico Reghenzani, Federico Terraneo, Davide Zoni, Carlo Brandolese, Massimo Celino, Francesco Iannone, Paolo Palazzari, Giuseppe Zummo, Massimo Bernaschi, Pasqua D'Ambra, Sergio Saponara, Marco Danelutto, Massimo Torquati, Marco Aldinucci, Yasir Arfat, Barbara Cantalupo, Iacopo Colonnelli, Roberto Esposito, Alberto Riccardo Martinelli, Gianluca Mittone, Olivier Beaumont, Bérenger Bramas, Lionel Eyraud-Dubois, Brice Goglin, Abdou Guermouche, Raymond Namyst, Samuel Thibault, Antonio Filgueras, Miquel Vidal, Carlos Álvarez, Xavier Martorell, Ariel Oleksiak, Michal Kulczewski, Alessandro Lonardo, Piero Vicini, Francesca Lo Cicero, Francesco Simula, Andrea Biagioni, Paolo Cretaro, Ottorino Frezza, Pier Stanislao Paolucci, Matteo Turisini, Francesco Giacomini, Tommaso Boccali, Simone Montangero, Roberto Ammendola:
TEXTAROSSA: Towards EXtreme scale Technologies and Accelerators for euROhpc hw/Sw Supercomputing Applications for exascale. DSD 2021: 286-294 - [c29]Iacopo Colonnelli, Barbara Cantalupo, Roberto Esposito, Matteo Pennisi, Concetto Spampinato, Marco Aldinucci:
HPC Application Cloudification: The StreamFlow Toolkit (Invited Paper). PARMA-DITAM@HiPEAC 2021: 5:1-5:13 - [c28]Shuyi Yang, Dino Ienco, Roberto Esposito, Ruggero G. Pensa:
An Inductive Framework for Semi-supervised Learning (Discussion Paper). SEBD 2021: 371-378 - [c27]Roberto Esposito:
Fairness and Neural Networks. SEBD 2021: v - 2020
- [c26]Marco Grazioso, Roberto Esposito, Emma Maayan-Fanar, Tsvi Kuflik, Francesco Cutugno:
Using Eye Tracking Data to Understand Visitors' Behaviour. AVI²CH@AVI 2020 - [c25]Mattia Cerrato, Marius Köppel, Alexander Segner, Roberto Esposito, Stefan Kramer:
Fair pairwise learning to rank. DSAA 2020: 729-738 - [c24]Mattia Cerrato, Roberto Esposito, Laura Li Puma:
Constraining deep representations with a noise module for fair classification. SAC 2020: 470-472 - [i1]Roberto Esposito, Mattia Cerrato, Marco Locatelli:
Partitioned Least Squares. CoRR abs/2006.16202 (2020)
2010 – 2019
- 2019
- [c23]Roberto Esposito, Mattia Cerrato, Marco Locatelli:
Partitioned Least Squares. AI*IA 2019: 180-192 - [c22]Mattia Cerrato, Edoardo Arnaudo, Valentina Gliozzi, Roberto Esposito:
Taxonomic and Whole Object Constraints: A Deep Architecture. CogSci 2019: 1465-1471 - 2017
- [c21]Giorgia Fenoglio, Roberto Esposito, Valentina Gliozzi:
A Neural Network Model for Taxonomic Responding with Realistic Visual Inputs. CogSci 2017 - 2015
- [j6]Alessia Visconti, Giuseppe Ermondi, Giulia Caron, Roberto Esposito:
Prediction and interpretation of the lipophilicity of small peptides. J. Comput. Aided Mol. Des. 29(4): 361-370 (2015) - [c20]Rosa Meo, Roberto Esposito, Marco Botta, Sergio Viola, Chee Ming Choor, Valter Mellano, Franco Ciaramaglia:
Autonomous abnormal behaviour detection in intelligence surveillance and reconnaissance applications. RTSI 2015: 334-340 - 2014
- [d1]Daniele Paolo Radicioni, Roberto Esposito:
Bach Choral Harmony. UCI Machine Learning Repository, 2014
- 2013
- [c19]Roberto Esposito, Daniele Paolo Radicioni, Alessia Visconti:
CDoT: Optimizing MAP Queries on Trees. AI*IA 2013: 481-492 - 2011
- [j5]Alessia Visconti, Roberto Esposito, Francesca Cordero:
Restructuring the Gene Ontology to emphasise regulative pathways and to improve gene similarity queries. Int. J. Comput. Biol. Drug Des. 4(3): 220-238 (2011) - [c18]Alessia Visconti, Roberto Esposito, Francesca Cordero:
Tackling the DREAM Challenge for Gene Regulatory Networks Reverse Engineering. AI*IA 2011: 372-382 - 2010
- [p1]Daniele Paolo Radicioni, Roberto Esposito:
BREVE: An HMPerceptron-Based Chord Recognition System. Advances in Music Information Retrieval 2010: 143-164
2000 – 2009
- 2009
- [j4]Roberto Esposito, Giuseppe Ermondi, Giulia Caron:
OpenCDLig: a free web application for sharing resources about cyclodextrin/ligand complexes. J. Comput. Aided Mol. Des. 23(9): 669-675 (2009) - [j3]Roberto Esposito, Daniele Paolo Radicioni:
CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning. J. Mach. Learn. Res. 10: 1851-1880 (2009) - [c17]Roberto Esposito, Daniele Paolo Radicioni:
Empirical Assessment of Two Strategies for Optimizing the Viterbi Algorithm. AI*IA 2009: 141-150 - 2007
- [j2]Arianna Gallo, Roberto Esposito, Rosa Meo, Marco Botta:
Incremental Extraction of Association Rules in Applicative Domains. Appl. Artif. Intell. 21(4&5): 297-315 (2007) - [c16]Roberto Esposito, Daniele Paolo Radicioni:
Trip Around the HMPerceptron Algorithm: Empirical Findings and Theoretical Tenets. AI*IA 2007: 242-253 - [c15]Daniele Paolo Radicioni, Roberto Esposito:
Tonal Harmony Analysis: A Supervised Sequential Learning Approach. AI*IA 2007: 638-649 - [c14]Roberto Esposito, Daniele Paolo Radicioni:
CarpeDiem: an algorithm for the fast evaluation of SSL classifiers. ICML 2007: 257-264 - 2006
- [j1]Roberto Esposito, Rosa Meo, Marco Botta:
Answering constraint-based mining queries on itemsets using previous materialized results. J. Intell. Inf. Syst. 26(1): 95-111 (2006) - [c13]Daniele Paolo Radicioni, Roberto Esposito:
A Conditional Model for Tonal Analysis. ISMIS 2006: 652-661 - 2005
- [c12]Arianna Gallo, Roberto Esposito, Rosa Meo, Marco Botta:
Optimization of Association Rules Extraction Through Exploitation of Context Dependent Constraints. AI*IA 2005: 258-269 - [c11]Roberto Esposito, Lorenza Saitta:
Experimental comparison between bagging and Monte Carlo ensemble classification. ICML 2005: 209-216 - 2004
- [c10]Rosa Meo, Marco Botta, Roberto Esposito, Arianna Gallo:
A Novel Incremental Approach to Association Rules Mining in Inductive Databases. Constraint-Based Mining and Inductive Databases 2004: 267-294 - [c9]Rosa Meo, Pier Luca Lanzi, Maristella Matera, Danilo Careggio, Roberto Esposito:
Employing Inductive Databases in Concrete Applications. Constraint-Based Mining and Inductive Databases 2004: 295-327 - [c8]Roberto Esposito:
Empirical Evaluation of the Effects of Concept Complexity on Generalization Error. ECAI 2004: 1009-1010 - [c7]Rosa Meo, Marco Botta, Roberto Esposito:
Query Rewriting in Itemset Mining. FQAS 2004: 111-124 - [c6]Roberto Esposito, Lorenza Saitta:
A Monte Carlo analysis of ensemble classification. ICML 2004 - [c5]Rosa Meo, Pier Luca Lanzi, Maristella Matera, Roberto Esposito:
Integrating Web Conceptual Modeling and Web Usage Mining. WebKDD 2004: 135-148 - 2003
- [b1]Roberto Esposito:
Analyzing ensemble learning in the framework of Monte Carlo theory. University of Turin, Italy, 2003 - [c4]Roberto Esposito, Lorenza Saitta:
Explaining Bagging with Monte Carlo Theory. AI*IA 2003: 189-200 - [c3]Roberto Esposito, Lorenza Saitta:
Monte Carlo Theory as an Explanation of Bagging and Boosting. IJCAI 2003: 499-504 - 2002
- [c2]Roberto Esposito, Lorenza Saitta:
Is a Greedy Covering Strategy an Extreme Boosting? ISMIS 2002: 94-102 - 2001
- [c1]Roberto Esposito, Lorenza Saitta:
Boosting as a Monte Carlo Algorithm. AI*IA 2001: 11-19
Coauthor Index
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last updated on 2024-10-07 22:10 CEST by the dblp team
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