Generating Realistic Synthetic Relational Data through Graph Variational Autoencoders. - Ciro Antonio Mami; Andrea Coser; Eric Medvet; Alexander T. P. Boudewijn; Marco Volpe 004; Michael Whitworth; Borut Svara; Gabriele Sgroi; Daniele Panfilo; Sebastiano Saccani (CoRR, 2022)

A Deep Learning-Based Pipeline for the Generation of Synthetic Tabular Data. - Daniele Panfilo; Alexander T. P. Boudewijn; Sebastiano Saccani; Andrea Coser; Borut Svara; Carlo Rossi Chauvenet; Ciro Antonio Mami; Eric Medvet (IEEE Access, 2023)

Conditioning Score-Based Generative Models by Neuro-Symbolic Constraints. - Davide Scassola; Sebastiano Saccani; Ginevra Carbone; Luca Bortolussi (CoRR, 2023)

Zero-Shot Conditioning of Score-Based Diffusion Models by Neuro-Symbolic Constraints. - Davide Scassola; Sebastiano Saccani; Ginevra Carbone; Luca Bortolussi (AAAI, 2025)

Meta-learning approach for variational autoencoder hyperparameter tuning. - Michele Berti; Matheus Camilo da Silva; Sebastiano Saccani; Sylvio Barbon Junior (J. Univers. Comput. Sci., 2025)

Contrastive Learning-Based privacy metrics in Tabular Synthetic Datasets. - Milton Nicolás Plasencia Palacios; Sebastiano Saccani; Gabriele Sgroi; Alexander T. P. Boudewijn; Luca Bortolussi (CoRR, 2025)

Graph Conditional Flow Matching for Relational Data Generation. - Davide Scassola; Sebastiano Saccani; Luca Bortolussi (CoRR, 2025)