39 citations · 150 across the 14 of their papers we have counts for
10 papers · 1 filter
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…
Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraints
Mihaela Cătălina Stoian, Eleonora Giunchiglia
Synthetic tabular data generation has traditionally been a challenging problem due to the high complexity of the underlying distributions that characterise this type of data. Despi…
Deep generative models as an adversarial attack strategy for tabular machine learning
Salijona Dyrmishi, Mihaela Cătălina Stoian, Eleonora Giunchiglia +1
Deep Generative Models (DGMs) have found application in computer vision for generating adversarial examples to test the robustness of machine learning (ML) systems. Extending these…
Exploiting T-norms for Deep Learning in Autonomous Driving
Mihaela Cătălina Stoian, Eleonora Giunchiglia, Thomas Lukasiewicz
Deep learning has been at the core of the autonomous driving field development, due to the neural networks' success in finding patterns in raw data and turning them into accurate p…
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…
Machine Learning with Requirements: a Manifesto
Eleonora Giunchiglia, Fergus Imrie, Mihaela van der Schaar +1
In the recent years, machine learning has made great advancements that have been at the root of many breakthroughs in different application domains. However, it is still an open is…
ROAD-R: The Autonomous Driving Dataset with Logical Requirements
Eleonora Giunchiglia, Mihaela Cătălina Stoian, Salman Khan +2
Neural networks have proven to be very powerful at computer vision tasks. However, they often exhibit unexpected behaviours, violating known requirements expressing background know…
Multi-Label Classification Neural Networks with Hard Logical Constraints
Eleonora Giunchiglia, Thomas Lukasiewicz
Multi-label classification (MC) is a standard machine learning problem in which a data point can be associated with a set of classes. A more challenging scenario is given by hierar…
Coherent Hierarchical Multi-Label Classification Networks
Eleonora Giunchiglia, Thomas Lukasiewicz
Hierarchical multi-label classification (HMC) is a challenging classification task extending standard multi-label classification problems by imposing a hierarchy constraint on the…