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cs.AI2025

Oruga: An Avatar of Representational Systems Theory

Daniel Raggi, Gem Stapleton, Mateja Jamnik +3

Humans use representations flexibly. We draw diagrams, change representations and exploit creative analogies across different domains. We want to harness this kind of power and end…

cs.LG2025

Structure Transfer: an Inference-Based Calculus for the Transformation of Representations

Daniel Raggi, Gem Stapleton, Mateja Jamnik +3

Representation choice is of fundamental importance to our ability to communicate and reason effectively. A major unsolved problem, addressed in this paper, is how to devise represe…

cs.LG2025

Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts

Mateo Espinosa Zarlenga, Gabriele Dominici, Pietro Barbiero +2

In this paper, we investigate how concept-based models (CMs) respond to out-of-distribution (OOD) inputs. CMs are interpretable neural architectures that first predict a set of hig…

cs.LG2025

Foundations of Interpretable Models

Pietro Barbiero, Mateo Espinosa Zarlenga, Alberto Termine +2

We argue that existing definitions of interpretability are not actionable in that they fail to inform users about general, sound, and robust interpretable model design. This makes…

cs.LG2025

Do Concept Bottleneck Models Respect Localities?

Naveen Raman, Mateo Espinosa Zarlenga, Juyeon Heo +1

Concept-based explainability methods use human-understandable intermediaries to produce explanations for machine learning models. These methods assume concept predictions can help…

cs.LG2025

Multimodal Lego: Model Merging and Fine-Tuning Across Topologies and Modalities in Biomedicine

Konstantin Hemker, Nikola Simidjievski, Mateja Jamnik

Learning holistic computational representations in physical, chemical or biological systems requires the ability to process information from different distributions and modalities…