3 citations · 4 across the 3 of their papers we have counts for
3 papers
A Survey on Deep Learning Approaches for Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, Diversity, and Beyond
Mihaela Cătălina Stoian, Eleonora Giunchiglia, Thomas Lukasiewicz
Generative modelling has become the standard approach for synthesising tabular data. However, different use cases demand synthetic data to comply with different requirements to be…
How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data
Mihaela Cătălina Stoian, Salijona Dyrmishi, Maxime Cordy +2
Deep Generative Models (DGMs) have been shown to be powerful tools for generating tabular data, as they have been increasingly able to capture the complex distributions that charac…
PiShield: A PyTorch Package for Learning with Requirements
Mihaela Cătălina Stoian, Alex Tatomir, Thomas Lukasiewicz +1
Deep learning models have shown their strengths in various application domains, however, they often struggle to meet safety requirements for their outputs. In this paper, we introd…