Boosting as a Monte Carlo Algorithm. - Roberto Esposito; Lorenza Saitta (AI*IA, 2001)

Is a Greedy Covering Strategy an Extreme Boosting? - Roberto Esposito; Lorenza Saitta (ISMIS, 2002)

Explaining Bagging with Monte Carlo Theory. - Roberto Esposito; Lorenza Saitta (AI*IA, 2003)

Monte Carlo Theory as an Explanation of Bagging and Boosting. - Roberto Esposito; Lorenza Saitta (IJCAI, 2003)

Analyzing ensemble learning in the framework of Monte Carlo theory. - Roberto Esposito (2003)

A Novel Incremental Approach to Association Rules Mining in Inductive Databases. - Rosa Meo; Marco Botta; Roberto Esposito; Arianna Gallo (Constraint-Based Mining and Inductive Databases, 2004)

Employing Inductive Databases in Concrete Applications. - Rosa Meo; Pier Luca Lanzi; Maristella Matera; Danilo Careggio; Roberto Esposito (Constraint-Based Mining and Inductive Databases, 2004)

Empirical Evaluation of the Effects of Concept Complexity on Generalization Error. - Roberto Esposito (ECAI, 2004)

Query Rewriting in Itemset Mining. - Rosa Meo; Marco Botta; Roberto Esposito (FQAS, 2004)

A Monte Carlo analysis of ensemble classification. - Roberto Esposito; Lorenza Saitta (ICML, 2004)

Integrating Web Conceptual Modeling and Web Usage Mining. - Rosa Meo; Pier Luca Lanzi; Maristella Matera; Roberto Esposito (WebKDD, 2004)

Optimization of Association Rules Extraction Through Exploitation of Context Dependent Constraints. - Arianna Gallo; Roberto Esposito; Rosa Meo; Marco Botta (AI*IA, 2005)

Experimental comparison between bagging and Monte Carlo ensemble classification. - Roberto Esposito; Lorenza Saitta (ICML, 2005)

A Conditional Model for Tonal Analysis. - Daniele Paolo Radicioni; Roberto Esposito (ISMIS, 2006)

Answering constraint-based mining queries on itemsets using previous materialized results. - Roberto Esposito; Rosa Meo; Marco Botta (J. Intell. Inf. Syst., 2006)

Trip Around the HMPerceptron Algorithm: Empirical Findings and Theoretical Tenets. - Roberto Esposito; Daniele Paolo Radicioni (AI*IA, 2007)

Tonal Harmony Analysis: A Supervised Sequential Learning Approach. - Daniele Paolo Radicioni; Roberto Esposito (AI*IA, 2007)

CarpeDiem: an algorithm for the fast evaluation of SSL classifiers. - Roberto Esposito; Daniele Paolo Radicioni (ICML, 2007)

Incremental Extraction of Association Rules in Applicative Domains. - Arianna Gallo; Roberto Esposito; Rosa Meo; Marco Botta (Appl. Artif. Intell., 2007)

Empirical Assessment of Two Strategies for Optimizing the Viterbi Algorithm. - Roberto Esposito; Daniele Paolo Radicioni (AI*IA, 2009)

OpenCDLig: a free web application for sharing resources about cyclodextrin/ligand complexes. - Roberto Esposito; Giuseppe Ermondi; Giulia Caron (J. Comput. Aided Mol. Des., 2009)

CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning. - Roberto Esposito; Daniele Paolo Radicioni (J. Mach. Learn. Res., 2009)

BREVE: An HMPerceptron-Based Chord Recognition System. - Daniele Paolo Radicioni; Roberto Esposito (Advances in Music Information Retrieval, 2010)

Tackling the DREAM Challenge for Gene Regulatory Networks Reverse Engineering. - Alessia Visconti; Roberto Esposito; Francesca Cordero (AI*IA, 2011)

Restructuring the Gene Ontology to emphasise regulative pathways and to improve gene similarity queries. - Alessia Visconti; Roberto Esposito; Francesca Cordero (Int. J. Comput. Biol. Drug Des., 2011)

CDoT: Optimizing MAP Queries on Trees. - Roberto Esposito; Daniele Paolo Radicioni; Alessia Visconti (AI*IA, 2013)

Bach Choral Harmony. - Daniele Paolo Radicioni; Roberto Esposito (UCI Machine Learning Repository, 2014)

Autonomous abnormal behaviour detection in intelligence surveillance and reconnaissance applications. - Rosa Meo; Roberto Esposito; Marco Botta; Sergio Viola; Chee Ming Choor; Valter Mellano; Franco Ciaramaglia (RTSI, 2015)

Prediction and interpretation of the lipophilicity of small peptides. - Alessia Visconti; Giuseppe Ermondi; Giulia Caron; Roberto Esposito (J. Comput. Aided Mol. Des., 2015)

A Neural Network Model for Taxonomic Responding with Realistic Visual Inputs. - Giorgia Fenoglio; Roberto Esposito; Valentina Gliozzi (CogSci, 2017)

Taxonomic and Whole Object Constraints: A Deep Architecture. - Mattia Cerrato; Edoardo Arnaudo; Valentina Gliozzi; Roberto Esposito (CogSci, 2019)

Using Eye Tracking Data to Understand Visitors' Behaviour. - Marco Grazioso; Roberto Esposito; Emma Maayan-Fanar; Tsvi Kuflik; Francesco Cutugno (AVI²CH@AVI, 2020)

Fair pairwise learning to rank. - Mattia Cerrato; Marius Köppel; Alexander Segner; Roberto Esposito; Stefan Kramer 001 (DSAA, 2020)

Constraining deep representations with a noise module for fair classification. - Mattia Cerrato; Roberto Esposito; Laura Li Puma (SAC, 2020)

The Italian research on HPC key technologies across EuroHPC. - 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; et al. (CF, 2021)

TEXTAROSSA: Towards EXtreme scale Technologies and Accelerators for euROhpc hw/Sw Supercomputing Applications for exascale. - Giovanni Agosta; Daniele Cattaneo 002; William Fornaciari; Andrea Galimberti; Giuseppe Massari; Federico Reghenzani; Federico Terraneo; Davide Zoni; Carlo Brandolese; Massimo Celino; Francesco Iannone; Paolo Palazzari; et al. (DSD, 2021)

HPC Application Cloudification: The StreamFlow Toolkit (Invited Paper). - Iacopo Colonnelli; Barbara Cantalupo; Roberto Esposito; Matteo Pennisi; Concetto Spampinato; Marco Aldinucci (PARMA-DITAM@HiPEAC, 2021)

Fairness and Neural Networks. - Roberto Esposito (SEBD, 2021)

An Inductive Framework for Semi-supervised Learning (Discussion Paper). - Shuyi Yang; Dino Ienco; Roberto Esposito; Ruggero G. Pensa (SEBD, 2021)

ESA☆: A generic framework for semi-supervised inductive learning. - Shuyi Yang; Dino Ienco; Roberto Esposito; Ruggero G. Pensa (Neurocomputing, 2021)

Ranking Algorithms for Word Ordering in Surface Realization. - Alessandro Mazzei; Mattia Cerrato; Roberto Esposito; Valerio Basile (Inf., 2021)

NeuNAC: A novel fragile watermarking algorithm for integrity protection of neural networks. - Marco Botta; Davide Cavagnino; Roberto Esposito (Inf. Sci., 2021)

Towards a Conditional and Multi-preferential Approach to Explainability of Neural Network Models in Computational Logic (Extended Abstract). - Mario Alviano; Francesco Bartoli; Marco Botta; Roberto Esposito; Laura Giordano 001; Valentina Gliozzi; Daniele Theseider Dupré (XAI.it@AI*IA, 2022)

Model Checking Verification of MultiLayer Perceptrons in Datalog: a Many-valued Approach with Typicality. - Francesco Bartoli; Marco Botta; Roberto Esposito; Laura Giordano 001; Daniele Theseider Dupré (Datalog, 2022)

Boosting the Federation: Cross-Silo Federated Learning without Gradient Descent. - Mirko Polato; Roberto Esposito; Marco Aldinucci (IJCNN, 2022)

Benchmarking FedAvg and FedCurv for Image Classification Tasks. - Bruno Casella; Roberto Esposito; Carlo Cavazzoni; Marco Aldinucci (itaDATA, 2022)

From Common Sense Reasonig to Neural Network Models: a Conditional and Multi-preferential Approach for Explainability and Neuro-Symbolic Integration. - Francesco Bartoli; Marco Botta; Roberto Esposito; Laura Giordano 001; Valentina Gliozzi; Daniele Theseider Dupré (FCR@KI, 2022)

Dealing With Multipositive Unlabeled Learning Combining Metric Learning and Deep Clustering. - Amedeo Racanati; Roberto Esposito; Dino Ienco (IEEE Access, 2022)

Fair Interpretable Representation Learning with Correction Vectors. - Mattia Cerrato; Alesia Vallenas Coronel; Marius Köppel; Alexander Segner; Roberto Esposito; Stefan Kramer 001 (CoRR, 2022)

Towards EXtreme scale technologies and accelerators for euROhpc hw/Sw supercomputing applications for exascale: The TEXTAROSSA approach. - Giovanni Agosta; Marco Aldinucci; Carlos Álvarez 001; Roberto Ammendola; Yasir Arfat; Olivier Beaumont; Massimo Bernaschi; Andrea Biagioni; Tommaso Boccali; Bérenger Bramas; Carlo Brandolese; Barbara Cantalupo; et al. (Microprocess. Microsystems, 2022)

Invariant Representations with Stochastically Quantized Neural Networks. - Mattia Cerrato; Marius Köppel; Roberto Esposito; Stefan Kramer 001 (AAAI, 2023)

Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning. - Gianluca Mittone; Nicolò Tonci; Robert Birke; Iacopo Colonnelli; Doriana Medic; Andrea Bartolini; Roberto Esposito; Emanuele Parisi; Francesco Beneventi; Mirko Polato; Massimo Torquati; Luca Benini; et al. (CF, 2023)

Hierarchical priors for Hyperspherical Prototypical Networks. - Samuele Fonio; Lorenzo Paletto; Mattia Cerrato; Dino Ienco; Roberto Esposito (ESANN, 2023)

Pooling critical datasets with Federated Learning. - Yasir Arfat; Gianluca Mittone; Iacopo Colonnelli; Fabrizio D'Ascenzo; Roberto Esposito; Marco Aldinucci (PDP, 2023)

Boosting Methods for Federated Learning. - Roberto Esposito; Mirko Polato; Marco Aldinucci (SEBD, 2023)

Fair Semi-supervised Representation Learning for Tabular Data Classification. - Shuyi Yang; Mattia Cerrato; Dino Ienco; Ruggero G. Pensa; Roberto Esposito (SEBD, 2023)

1st Workshop on Federated Learning Technologies. - Mirko Polato; Roberto Esposito; Walter Riviera; Zenglin Xu; Irwin King (WWW (Companion Volume), 2023)

Experimenting with Emerging ARM and RISC-V Systems for Decentralised Machine Learning. - Gianluca Mittone; Nicolò Tonci; Robert Birke; Iacopo Colonnelli; Doriana Medic; Andrea Bartolini; Roberto Esposito; Emanuele Parisi; Francesco Beneventi; Mirko Polato; Massimo Torquati; Luca Benini; et al. (CoRR, 2023)

FairSwiRL: fair semi-supervised classification with representation learning. - Shuyi Yang; Mattia Cerrato; Dino Ienco; Ruggero G. Pensa; Roberto Esposito (Mach. Learn., 2023)

Label Augmentation for Zero-Shot Hierarchical Text Classification. - Lorenzo Paletto; Valerio Basile; Roberto Esposito (ACL (1), 2024)

Many-valued Temporal Weighted Knowledge Bases with Typicality for Explainability. - Mario Alviano; Marco Botta; Roberto Esposito; Laura Giordano 001; Daniele Theseider Dupré (CILC, 2024)

Verifying Properties of a MultiLayer Network for the Recognition of Basic Emotions in a Conditional DL with Typicality (Extended Abstract). - Mario Alviano; Francesco Bartoli; Marco Botta; Roberto Esposito; Laura Giordano 001; Daniele Theseider Dupré (Description Logics, 2024)

FedHP: Federated Learning with Hyperspherical Prototypical Regularization. - Samuele Fonio; Mirko Polato; Roberto Esposito (ESANN, 2024)

Many-valued Temporal Description Logics with Typicality: an Abridged Report (short paper). - Mario Alviano; Marco Botta; Roberto Esposito; Laura Giordano 001; Daniele Theseider Dupré (AI4CC-IPS-RCRA-SPIRIT@AI*IA, 2024)

Experimenting With Normalization Layers in Federated Learning on Non-IID Scenarios. - Bruno Casella; Roberto Esposito; Antonio Sciarappa; Carlo Cavazzoni; Marco Aldinucci (IEEE Access, 2024)

Graph Convolutional Branch and Bound. - Lorenzo Sciandra; Roberto Esposito; Andrea Cesare Grosso; Laura Sacerdote; Cristina Zucca (CoRR, 2024)

A preferential interpretation of MultiLayer Perceptrons in a conditional logic with typicality. - Mario Alviano; Francesco Bartoli; Marco Botta; Roberto Esposito; Laura Giordano 001; Daniele Theseider Dupré (Int. J. Approx. Reason., 2024)

Partitioned least squares. - Roberto Esposito; Mattia Cerrato; Marco Locatelli 001 (Mach. Learn., 2024)

Hyperbolic Prototypical Entailment Cones for Image Classification. - Samuele Fonio; Roberto Esposito; Marco Aldinucci (AISTATS, 2025)

SVF: Support Vector Federation. - Mirko Polato; Roberto Esposito; Lorenzo Sciandra (IEEE Access, 2025)