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Inclusion logic is a variant of dependence logic that was shown to have the same expressive power as positive greatest fixed-point logic. Inclusion logic is not axiomatizable in full, but its first-order consequences can be axiomatized. In…

Logic · Mathematics 2020-01-22 Fan Yang

We consider the decidability of the verification problem of programs \emph{modulo axioms} --- that is, verifying whether programs satisfy their assertions, when the functions and relations it uses are assumed to interpreted by arbitrary…

Programming Languages · Computer Science 2019-10-30 Umang Mathur , P. Madhusudan , Mahesh Viswanathan

Fair classification aims to stress the classification models to achieve the equality (treatment or prediction quality) among different sensitive groups. However, fair classification can be under the risk of poisoning attacks that…

Machine Learning · Computer Science 2022-10-19 Han Xu , Xiaorui Liu , Yuxuan Wan , Jiliang Tang

The disjunctive restricted chase is a sound and complete procedure for solving boolean conjunctive query entailment over knowledge bases of disjunctive existential rules. Alas, this procedure does not always terminate and checking if it…

Logic in Computer Science · Computer Science 2023-09-25 Lukas Gerlach , David Carral

We define a proof system for exceptions which is close to the syntax for exceptions, in the sense that the exceptions do not appear explicitly in the type of any expression. This proof system is sound with respect to the intended…

Logic in Computer Science · Computer Science 2012-03-15 Jean-Guillaume Dumas , Dominique Duval , Laurent Fousse , Jean-Claude Reynaud

Combinations of neural ODEs with recurrent neural networks (RNN), like GRU-ODE-Bayes or ODE-RNN are well suited to model irregularly observed time series. While those models outperform existing discrete-time approaches, no theoretical…

Machine Learning · Statistics 2021-05-11 Calypso Herrera , Florian Krach , Josef Teichmann

The standard conditional probability definition formula is derived as a consequence of the Insufficient Reason Principle expressed as the Maximum Relative Divergence Principle for grading (order-comonotonic) functions on a totally ordered…

Probability · Mathematics 2025-07-14 Alexander Dukhovny

We combine several folklore observations to provide a working framework for iterating constructions which contradict the axiom of choice. We use this to define a model in which any kind of structural failure must fail with a proper class of…

Logic · Mathematics 2021-07-26 Asaf Karagila

The present work proposes an alternative approach to the problem of the emergence of classicality. Typical approaches developed in the literature derive the classical behaviour of a quantum system from conditions that concern the value of…

Quantum Physics · Physics 2024-12-11 Giuseppe Nisticò

It is described dynamics of a large class of accelerating cosmological models in terms of dynamical systems of the Newtonian type. The evolution of the models is reduced to the motion of a particle in a potential well parameterized by the…

Astrophysics · Physics 2017-09-29 Marek Szydlowski , Aleksandra Kurek

A typical desideratum for quantifying the uncertainty from a classification model as a prediction set is class-conditional singleton set calibration. That is, such sets should map to the output of well-calibrated selective classifiers,…

Machine Learning · Computer Science 2022-12-02 Allen Schmaltz , Danielle Rasooly

We present a general formal argumentation system for dealing with the detachment of conditional obligations. Given a set of facts, constraints, and conditional obligations, we answer the question whether an unconditional obligation is…

Artificial Intelligence · Computer Science 2016-06-02 Mathieu Beirlaen , Christian Straßer

We consider a novel variant of the contextual bandit problem (i.e., the multi-armed bandit with side-information, or context, available to a decision-maker) where the context used at each decision may be corrupted ("useless context"). This…

Machine Learning · Computer Science 2020-06-30 Djallel Bouneffouf

Learning-based approaches, such as reinforcement and imitation learning are gaining popularity in decision-making for autonomous driving. However, learned policies often fail to generalize and cannot handle novel situations well. Asking and…

Machine Learning · Computer Science 2020-11-13 Patrick Hart , Alois Knoll

Conditional acceptability refers to how plausible a conditional statement is perceived to be. It plays an important role in communication and reasoning, as it influences how individuals interpret implications, assess arguments, and make…

Computation and Language · Computer Science 2026-03-20 Jasmin Orth , Philipp Mondorf , Barbara Plank

We study the problem of classification with a reject option for a fixed predictor, applicable in natural language processing. We introduce a new problem formulation for this scenario, and an algorithm minimizing a new surrogate loss…

Machine Learning · Computer Science 2023-02-01 Christopher Mohri , Daniel Andor , Eunsol Choi , Michael Collins

In this paper, we explore how, and if, free choice permission (FCP) can be accepted when we consider deontic conflicts between certain types of permissions and obligations. As is well known, FCP can license, under some minimal conditions,…

Logic in Computer Science · Computer Science 2019-06-11 Guido Governatori , Antonino Rotolo

Recently the problem of Unambiguous State Discrimination (USD) of mixed quantum states has attracted much attention. So far, bounds on the optimum success probability have been derived [1]. For two mixed states they are given in terms of…

Quantum Physics · Physics 2008-06-04 Philippe Raynal , Norbert Lütkenhaus

Most off-policy evaluation methods for contextual bandits have focused on the expected outcome of a policy, which is estimated via methods that at best provide only asymptotic guarantees. However, in many applications, the expectation may…

Machine Learning · Statistics 2022-10-27 Muhammad Faaiz Taufiq , Jean-Francois Ton , Rob Cornish , Yee Whye Teh , Arnaud Doucet

Latent Chain-of-Thought (Latent CoT) models promise efficient reasoning via continuous representations, yet exhibit puzzling performance inconsistencies: excelling at exploration (ProsQA: 97.0%) but failing at computation (GSM8K: 34.1%). We…

Artificial Intelligence · Computer Science 2026-02-03 Jiaxuan Zou , Yaozhong Xiong , Yong Liu
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