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Riccardo Massidda

3 accepted papers

2025

Deferring Concept Bottleneck Models: Learning to Defer Interventions to Inaccurate Experts

NeurIPS 2025poster

Concept Bottleneck Models (CBMs) are interpretable machine learning models that ground their predictions on human-understandable concepts, allowing for targeted interventions in their decision-making process. However, when intervened on, CBMs assume the availability of humans that can identify the n…

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2024

Constraint-Free Structure Learning with Smooth Acyclic Orientations

ICLR 2024poster

The structure learning problem consists of fitting data generated by a Directed Acyclic Graph (DAG) to correctly reconstruct its arcs. In this context, differentiable approaches constrain or regularize an optimization problem with a continuous relaxation of the acyclicity property. The computational…

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2024

Learning Causal Abstractions of Linear Structural Causal Models

UAI 2024poster

The need for modelling causal knowledge at different levels of granularity arises in several settings. Causal Abstraction provides a framework for formalizing this problem by relating two Structural Causal Models at different levels of detail. Despite increasing interest in applying causal abstracti…