Institution profile

Norwegian Defence Research Establishment

Academic institutioneurope · no
Official website
Research library6linked papers
Opportunities0open roles
Selected work

Representative Papers

Possibilistic operators in Formal Concept Analysis as Kan extensions

Jul 29, 2026

This study clarifies the theoretical origins of the eight possibility operators introduced by Dubois and Prade within formal concept analysis and elucidates their relationship to formal concepts. By leveraging Kan extensions from category theory, the paper provides the first unified interpretation of these operators as natural outcomes of Kan extensions derived from an underlying Boolean profunctor, while systematically constructing their dualities and closure structures. The main contributions include proving that NΠ-pairs correspond precisely to formal concepts of the complementary context, characterizing the unique combinations—symmetric or asymmetric—of possibility operators capable of generating formal concepts, and introducing novel closure operators based on these possibility operators, for which completeness and uniqueness in formal concept generation are rigorously established.

0 citationsRead paper

Computing Conditional Shapley Values Using Tabular Foundation Models

Feb 10, 2026

This work addresses the computational inefficiency of traditional Shapley value estimation in the presence of feature dependencies, which typically requires numerous conditional expectation evaluations and is ill-suited for acceleration via deep learning. The authors propose the first integration of tabular foundation models—such as TabPFN—into conditional Shapley value estimation, leveraging their in-context learning capabilities to efficiently approximate conditional expectations without retraining. By circumventing conventional Monte Carlo integration or repeated model training strategies, the method achieves substantial gains in computational efficiency. Empirical results across multiple synthetic and real-world datasets demonstrate that TabPFN and its variants consistently attain state-of-the-art or near-optimal explanation quality while requiring only a fraction of the runtime of existing approaches.

0 citationsRead paper
Recent publications

Latest Papers

Possibilistic operators in Formal Concept Analysis as Kan extensions

Jul 29, 2026

This study clarifies the theoretical origins of the eight possibility operators introduced by Dubois and Prade within formal concept analysis and elucidates their relationship to formal concepts. By leveraging Kan extensions from category theory, the paper provides the first unified interpretation of these operators as natural outcomes of Kan extensions derived from an underlying Boolean profunctor, while systematically constructing their dualities and closure structures. The main contributions include proving that NΠ-pairs correspond precisely to formal concepts of the complementary context, characterizing the unique combinations—symmetric or asymmetric—of possibility operators capable of generating formal concepts, and introducing novel closure operators based on these possibility operators, for which completeness and uniqueness in formal concept generation are rigorously established.

0 citationsRead paper

Computing Conditional Shapley Values Using Tabular Foundation Models

Feb 10, 2026

This work addresses the computational inefficiency of traditional Shapley value estimation in the presence of feature dependencies, which typically requires numerous conditional expectation evaluations and is ill-suited for acceleration via deep learning. The authors propose the first integration of tabular foundation models—such as TabPFN—into conditional Shapley value estimation, leveraging their in-context learning capabilities to efficiently approximate conditional expectations without retraining. By circumventing conventional Monte Carlo integration or repeated model training strategies, the method achieves substantial gains in computational efficiency. Empirical results across multiple synthetic and real-world datasets demonstrate that TabPFN and its variants consistently attain state-of-the-art or near-optimal explanation quality while requiring only a fraction of the runtime of existing approaches.

0 citationsRead paper