3 citations · 3 across the 6 of their papers we have counts for
6 papers
Stay Within Your Bounds: Distance-Guided Decoding for Guaranteed Context-Free Grammar Compliance
Vincenzo Collura, Karim Tit, Eleonora Giunchiglia +2
Grammar-constrained decoding helps large language models produce syntactically valid structured outputs, such as code, JSON, and SQL. For context-free grammars, many practical deco…
Privacy Vulnerabilities of Attention Layers in Tabular Foundation Models and Protection of High-Risk Queries
Tânia Carvalho, Maxime Cordy
Tabular foundation models are commonly assumed to present limited privacy concerns as they are often pre-trained on large collections of synthetic data. However, these models lever…
Measuring Cross-Modal Synergy: A Benchmark for VLM Explainability
Joël Roman Ky, Salah Ghamizi, Maxime Cordy
Vision-Language Models (VLMs) map complex visual inputs to semantic spaces, but interpreting the cross-modal reasoning of VLMs currently relies on post-hoc explainers evaluated via…
ABS: Enforcing Constraint Satisfaction On Generated Sequences Via Automata-Guided Beam Search
Vincenzo Collura, Karim Tit, Laura Bussi +2
Sequence generation and prediction form a cornerstone of modern machine learning, with applications spanning natural language processing, program synthesis, and time-series forecas…
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…
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…