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20172026
most citedAutoformalization with Large Language Models

43 citations · 117 across the 49 of their papers we have counts for

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Showing 2025Show all

9 papers · 1 filter

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

TabStruct: Measuring Structural Fidelity of Tabular Data

Xiangjian Jiang, Nikola Simidjievski, Mateja Jamnik

Evaluating tabular generators remains a challenging problem, as the unique causal structural prior of heterogeneous tabular data does not lend itself to intuitive human inspection.…

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.LG20252 cited

RO-FIGS: Efficient and Expressive Tree-Based Ensembles for Tabular Data

Urška Matjašec, Nikola Simidjievski, Mateja Jamnik

Tree-based models are often robust to uninformative features and can accurately capture non-smooth, complex decision boundaries. Consequently, they often outperform neural network-…

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…