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Interpretability is becoming increasingly important for predictive model analysis. Unfortunately, as remarked by many authors, there is still no consensus regarding this notion. The goal of this paper is to propose the definition of a score…

Machine Learning · Statistics 2021-11-24 Vincent Margot , George Luta

Weak structures abound in higher category theory, but are often suitably equivalent to stricter structures that are easier to understand. We extend strictification for tricategories and trihomomorphisms to trinatural transformations,…

Category Theory · Mathematics 2023-07-06 Adrian Miranda

Reachability analysis evaluates system safety, by identifying the set of states a system may evolve within over a finite time horizon. In contrast to model-based reachability analysis, data-driven reachability analysis estimates reachable…

Systems and Control · Electrical Eng. & Systems 2026-04-06 Elizabeth Dietrich , Hanna Krasowski , Murat Arcak

Recursive relational specifications are commonly used to describe the computational structure of formal systems. Recent research in proof theory has identified two features that facilitate direct, logic-based reasoning about such…

Logic in Computer Science · Computer Science 2010-09-24 Andrew Gacek , Dale Miller , Gopalan Nadathur

An operational definition of contextuality is introduced which generalizes the standard notion in three ways: (1) it applies to arbitrary operational theories rather than just quantum theory, (2) it applies to arbitrary experimental…

Quantum Physics · Physics 2016-09-08 R. W. Spekkens

Let $\mathcal M=\langle K;O\rangle$ be a real closed valued field and let $k$ be its residue field. We prove that every interpretable field in $\mathcal M$ is definably isomorphic to either $K$, $K(\sqrt{-1})$, $k$, or $k(\sqrt{-1})$. The…

Logic · Mathematics 2021-05-11 Assaf Hasson , Ya'acov Peterzil

Standard abstract model checking relies on abstract Kripke structures which approximate concrete models by gluing together indistinguishable states, namely by a partition of the concrete state space. Strong preservation for a specification…

Logic in Computer Science · Computer Science 2007-05-23 Francesco Ranzato , Francesco Tapparo

This paper provides a model theoretic semantics to feature terms augmented with set descriptions. We provide constraints to specify HPSG style set descriptions, fixed cardinality set descriptions, set-membership constraints, restricted…

cmp-lg · Computer Science 2008-02-03 Suresh Manandhar

We systematically investigate the complexity of model checking the existential positive fragment of first-order logic. In particular, for a set of existential positive sentences, we consider model checking where the sentence is restricted…

Logic in Computer Science · Computer Science 2015-03-20 Hubie Chen

The idea of fully accepting statements when the evidence has rendered them probable enough faces a number of difficulties. We leave the interpretation of probability largely open, but attempt to suggest a contextual approach to full belief.…

Artificial Intelligence · Computer Science 2013-02-08 Henry E. Kyburg

Along with the great success of deep neural networks, there is also growing concern about their black-box nature. The interpretability issue affects people's trust on deep learning systems. It is also related to many ethical problems, e.g.,…

Machine Learning · Computer Science 2022-02-01 Yu Zhang , Peter Tiňo , Aleš Leonardis , Ke Tang

Contextuality is a central property in comparative analysis of classical, quantum, and supercorrelated systems. We examine and compare two well-motivated approaches to contextuality. One approach ("contextuality-by-default") is based on the…

Quantum Physics · Physics 2016-01-21 J. Acacio de Barros , Ehtibar N. Dzhafarov , Janne V. Kujala , Gary Oas

Frege's theorem says that second-order Peano arithmetic is interpretable in Hume's Principle and full impredicative comprehension. Hume's Principle is one example of an abstraction principle, while another paradigmatic example is Basic Law…

Logic · Mathematics 2015-11-16 Sean Walsh

The large size and complex decision mechanisms of state-of-the-art text classifiers make it difficult for humans to understand their predictions, leading to a potential lack of trust by the users. These issues have led to the adoption of…

Improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of brain decoding…

Machine Learning · Statistics 2016-06-21 Seyed Mostafa Kia , Andrea Passerini

The use of nonstandard methods to characterize properties of weak, strong and mixed extensions of congruences to ultrafilters has been the main topic of several recent papers. We show that similar methods can be used to characterize the…

interpreters are tools to compute approximations for behaviors of a program. These approximations can then be used for optimisation or for error detection. In this paper, we show how to describe an abstract interpreter using the type-theory…

Logic in Computer Science · Computer Science 2008-10-20 Yves Bertot

We look at characterizing which formulas are expressible in rich decidable logics such as guarded fixpoint logic, unary negation fixpoint logic, and guarded negation fixpoint logic. We consider semantic characterizations of definability, as…

Logic in Computer Science · Computer Science 2023-06-22 Michael Benedikt , Pierre Bourhis , Michael Vanden Boom

With the growing pervasiveness of artificial intelligence, the ability to explain the inferences made by machine learning models has become increasingly important. Numerous techniques for model explainability have been proposed, with…

Human-Computer Interaction · Computer Science 2026-04-08 Nicola Rossberg , Bennett Kleinberg , Barry O'Sullivan , Luca Longo , Andrea Visentin

Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned…

Machine Learning · Statistics 2019-11-15 W. James Murdoch , Chandan Singh , Karl Kumbier , Reza Abbasi-Asl , Bin Yu