6 papers
Evaluation of Clinically Steerable Retinal Image Generation from Foundation Model Latent Spaces
Zuzanna A. Wakefield-Skórniewska, Bartłomiej W. Papież
Medical foundation models learn latent representations of clinically meaningful phenotypes, yet their ability to support controllable image generation remains largely unexplored. W…
Operationalising Relative Causal Knowledge: Backbone Identifiability from Private Reports on a Shared Outcome
Fabrizio Russo, Mark Somers
The Relativity of Causal Knowledge (RCK) explains how a network of agents with different structural causal models can exchange causal knowledge through a shared interventionally co…
Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach
Zihao Li, Fabrizio Russo
Causal discovery seeks to uncover causal relations from data, typically represented as causal graphs, and is essential for predicting the effects of interventions. While expert kno…
Heterogeneous Graph Neural Networks for Assumption-Based Argumentation
Preesha Gehlot, Anna Rapberger, Fabrizio Russo +1
Assumption-Based Argumentation (ABA) is a powerful structured argumentation formalism, but exact computation of extensions under stable semantics is intractable for large framework…
On Gradual Semantics for Assumption-Based Argumentation
Anna Rapberger, Fabrizio Russo, Antonio Rago +1
In computational argumentation, gradual semantics are fine-grained alternatives to extension-based and labelling-based semantics . They ascribe a dialectical strength to (component…
Shapley-PC: Constraint-based Causal Structure Learning with a Shapley Inspired Framework
Fabrizio Russo, Francesca Toni
Causal Structure Learning (CSL), also referred to as causal discovery, amounts to extracting causal relations among variables in data. CSL enables the estimation of causal effects…