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The chase is a fundamental algorithm with ubiquitous uses in database theory. Given a database and a set of existential rules (aka tuple-generating dependencies), it iteratively extends the database to ensure that the rules are satisfied in…

Logic in Computer Science · Computer Science 2025-05-23 David Carral , Lukas Gerlach , Lucas Larroque , Michaël Thomazo

Evaluating the in-context learning classification performance of language models poses challenges due to small dataset sizes, extensive prompt-selection using the validation set, and intentionally difficult tasks that lead to near-random…

Computation and Language · Computer Science 2024-11-12 Gregory Yauney , David Mimno

Outlier detection is an essential capability in safety-critical applications of supervised visual recognition. Most of the existing methods deliver best results by encouraging standard closed-set models to produce low-confidence predictions…

Computer Vision and Pattern Recognition · Computer Science 2024-09-10 Anja Delić , Matej Grcić , Siniša Šegvić

Answer set programming - the most popular problem solving paradigm based on logic programs - has been recently extended to support uninterpreted function symbols. All of these approaches have some limitation. In this paper we propose a…

Artificial Intelligence · Computer Science 2010-07-22 Sabrina Baselice , Piero A. Bonatti

Current techniques in machine learning are so far are unable to learn classifiers that are robust to adversarial perturbations. However, they are able to learn non-robust classifiers with very high accuracy, even in the presence of random…

Machine Learning · Computer Science 2019-01-04 Preetum Nakkiran

The multiple extension problem arises frequently in diagnostic and default inference. That is, we can often use any of a number of sets of defaults or possible hypotheses to explain observations or make Predictions. In default inference,…

Artificial Intelligence · Computer Science 2013-04-11 Eric Neufeld , David L Poole

Type qualifiers offer a lightweight mechanism for enriching existing type systems to enforce additional, desirable, program invariants. They do so by offering a restricted but effective form of subtyping. While the theory of type qualifiers…

Programming Languages · Computer Science 2024-02-27 Edward Lee , Yaoyu Zhao , James You , Kavin Satheeskumar , Ondřej Lhoták , Jonathan Brachthäuser

Let K be an abstract elementary class of models. Assume that there are less than the maximal number of models in K_{\lambda^{+n}} (namely models in K of power \lambda^{+n}) for all n. We provide conditions on K_\lambda, that imply the…

Logic · Mathematics 2010-01-17 Adi Jarden , Saharon Shelah

Deep neural classifiers have recently found tremendous success in data-driven control systems. However, existing models suffer from a trade-off between accuracy and adversarial robustness. This limitation must be overcome in the control of…

Machine Learning · Computer Science 2024-06-05 Yatong Bai , Brendon G. Anderson , Somayeh Sojoudi

Assessing the fairness of a decision making system with respect to a protected class, such as gender or race, is challenging when class membership labels are unavailable. Probabilistic models for predicting the protected class based on…

Applications · Statistics 2018-11-28 Jiahao Chen , Nathan Kallus , Xiaojie Mao , Geoffry Svacha , Madeleine Udell

Non-classical negations may fail to be contradictory-forming operators in more than one way, and they often fail also to respect fundamental meta-logical properties such as the replacement property. Such drawbacks are witnessed by intricate…

Logic in Computer Science · Computer Science 2017-07-26 Ori Lahav , João Marcos , Yoni Zohar

Two types of explanations have been receiving increased attention in the literature when analyzing the decisions made by classifiers. The first type explains why a decision was made and is known as a sufficient reason for the decision, also…

Artificial Intelligence · Computer Science 2023-07-25 Chunxi Ji , Adnan Darwiche

The lexicographic closure of any given finite set D of normal defaults is defined. A conditional assertion "if a then b" is in this lexicographic closure if, given the defaults D and the fact a, one would conclude b. The lexicographic…

Artificial Intelligence · Computer Science 2007-05-23 Daniel Lehmann

In critical applications, it is vital for classifiers to defer decision-making to humans. We propose a post-hoc method that makes existing classifiers selectively abstain from predicting certain samples. Our abstaining classifier is…

Machine Learning · Computer Science 2023-10-11 Tongxin Yin , Jean-François Ton , Ruocheng Guo , Yuanshun Yao , Mingyan Liu , Yang Liu

A prefix normal word is a binary word whose prefixes contain at least as many 1s as any of its factors of the same length. Introduced by Fici and Lipt\'ak in 2011 the notion of prefix normality is so far only defined for words over the…

Formal Languages and Automata Theory · Computer Science 2021-04-20 Yannik Eikmeier , Pamela Fleischmann , Mitja Kulczynski , Dirk Nowotka

This paper shows that default-based phonologies have the potential to capture morphophonological generalisations which cannot be captured by non-defaul theories. In achieving this result, I offer a characterisation of Underspecification…

cmp-lg · Computer Science 2008-02-03 T. Mark Ellison

In our paper "Essential normality, essential norms and hyperrigidity" we claimed that the restriction of the identity representation of a certain operator system (constructed from a polynomial ideal) has the unique extension property,…

Operator Algebras · Mathematics 2015-07-20 Matthew Kennedy , Orr Shalit

Recently, in Axioms 10(2): 119 (2021), a nonclassical first-order theory T of sets and functions has been introduced as the collection of axioms we have to accept if we want a foundational theory for (all of) mathematics that is not weaker…

General Mathematics · Mathematics 2026-03-13 Marcoen J. T. F. Cabbolet , Adrian R. D. Mathias

Class-conditional generative models hold promise to overcome the shortcomings of their discriminative counterparts. They are a natural choice to solve discriminative tasks in a robust manner as they jointly optimize for predictive…

Machine Learning · Computer Science 2020-02-18 Ethan Fetaya , Jörn-Henrik Jacobsen , Will Grathwohl , Richard Zemel

This thesis scrutinizes common assumptions underlying traditional machine learning approaches to fairness in consequential decision making. After challenging the validity of these assumptions in real-world applications, we propose ways to…

Machine Learning · Computer Science 2021-02-01 Niki Kilbertus