3 papers
cs.AI2026
Regret-Based Federated Causal Discovery with Unknown Interventions
Federico Baldo, Charles K. Assaad
Most causal discovery methods recover a completed partially directed acyclic graph representing a Markov equivalence class from observational data. Recent work has extended these m…
cs.AI2026
Retrieving Classes of Causal Orders with Inconsistent Knowledge Bases
Federico Baldo, Simon Ferreira, Charles K. Assaad
Traditional causal discovery methods often depend on strong, untestable assumptions, making them unreliable in real-world applications. In this context, Large Language Models (LLMs…
cs.SE2025
Enhancing Cell Counting through MLOps: A Structured Approach for Automated Cell Analysis
Matteo Testi, Luca Clissa, Matteo Ballabio +5
Machine Learning (ML) models offer significant potential for advancing cell counting applications in neuroscience, medical research, pharmaceutical development, and environmental m…