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Roberto Esposito
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2020 – today
- 2024
- [j15]Bruno Casella
, Roberto Esposito
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
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
Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning. CF 2023: 73-83 - [c41]Samuele Fonio, Lorenzo Paletto
Hierarchical priors for Hyperspherical Prototypical Networks. ESANN 2023 - [c40]Yasir Arfat, Gianluca Mittone, Iacopo Colonnelli
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
1st Workshop on Federated Learning Technologies. WWW (Companion Volume) 2023: 1150 - [i7]Gianluca Mittone, Nicolò Tonci, Robert Birke, Iacopo Colonnelli
Experimenting with Emerging ARM and RISC-V Systems for Decentralised Machine Learning. CoRR abs/2302.07946 (2023) - [i6]Bruno Casella, Roberto Esposito
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
A preferential interpretation of MultiLayer Perceptrons in a conditional logic with typicality. CoRR abs/2305.00304 (2023) - 2022
- [j11]Amedeo Racanati
Dealing With Multipositive Unlabeled Learning Combining Metric Learning and Deep Clustering. IEEE Access 10: 51839-51849 (2022) - [j10]Giovanni Agosta, Marco Aldinucci, Carlos Álvarez
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
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
Invariant Representations with Stochastically Quantized Neural Networks. CoRR abs/2208.02656 (2022) - 2021
- [j9]Shuyi Yang, Dino Ienco, Roberto Esposito
ESA☆: A generic framework for semi-supervised inductive learning. Neurocomputing 447: 102-117 (2021) - [j8]Alessandro Mazzei, Mattia Cerrato, Roberto Esposito
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
The Italian research on HPC key technologies across EuroHPC. CF 2021: 178-184 - [c30]Giovanni Agosta, Daniele Cattaneo, William Fornaciari
TEXTAROSSA: Towards EXtreme scale Technologies and Accelerators for euROhpc hw/Sw Supercomputing Applications for exascale. DSD 2021: 286-294 - [c29]Iacopo Colonnelli
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
Fair pairwise learning to rank. DSAA 2020: 729-738 - [c24]Mattia Cerrato, Roberto Esposito
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
Partitioned Least Squares. AI*IA 2019: 180-192 - [c22]Mattia Cerrato, Edoardo Arnaudo
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
Prediction and interpretation of the lipophilicity of small peptides. J. Comput. Aided Mol. Des. 29(4): 361-370 (2015) - [c20]Rosa Meo
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
CDoT: Optimizing MAP Queries on Trees. AI*IA 2013: 481-492 - 2011
- [j5]Alessia Visconti
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
Tackling the DREAM Challenge for Gene Regulatory Networks Reverse Engineering. AI*IA 2011: 372-382 - 2010
- [p1]Daniele Paolo Radicioni
BREVE: An HMPerceptron-Based Chord Recognition System. Advances in Music Information Retrieval 2010: 143-164
2000 – 2009
- 2009
- [j4]Roberto Esposito
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
Empirical Assessment of Two Strategies for Optimizing the Viterbi Algorithm. AI*IA 2009: 141-150 - 2007
- [j2]Arianna Gallo, Roberto Esposito
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
Tonal Harmony Analysis: A Supervised Sequential Learning Approach. AI*IA 2007: 638-649 - [c14]Roberto Esposito
CarpeDiem: an algorithm for the fast evaluation of SSL classifiers. ICML 2007: 257-264 - 2006
- [j1]Roberto Esposito
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
Optimization of Association Rules Extraction Through Exploitation of Context Dependent Constraints. AI*IA 2005: 258-269 - [c11]Roberto Esposito
Experimental comparison between bagging and Monte Carlo ensemble classification. ICML 2005: 209-216 - 2004
- [c10]Rosa Meo
A Novel Incremental Approach to Association Rules Mining in Inductive Databases. Constraint-Based Mining and Inductive Databases 2004: 267-294 - [c9]Rosa Meo
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
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
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 21:18 CEST by the dblp team
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