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Related papers: A Computational Model for Logical Analysis of Data

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Checklists have been widely recognized as effective tools for completing complex tasks in a systematic manner. Although originally intended for use in procedural tasks, their interpretability and ease of use have led to their adoption for…

Machine Learning · Computer Science 2024-11-27 Yukti Makhija , Edward De Brouwer , Rahul G. Krishnan

Multi-modal affective computing aims to automatically recognize and interpret human attitudes from diverse data sources such as images and text, thereby enhancing human-computer interaction and emotion understanding. Existing approaches…

Computation and Language · Computer Science 2025-06-10 Yuanhe Tian , Pengsen Cheng , Guoqing Jin , Lei Zhang , Yan Song

Stochastic processes offer a flexible mathematical formalism to model and reason about systems. Most analysis tools, however, start from the premises that models are fully specified, so that any parameters controlling the system's dynamics…

Systems and Control · Computer Science 2017-01-11 Luca Bortolussi , Guido Sanguinetti

Inspired by e-participation systems, in this paper we propose a new model to represent human debates and methods to obtain collective conclusions from them. This model overcomes drawbacks of existing approaches by allowing users to…

Artificial Intelligence · Computer Science 2020-07-15 Jordi Ganzer , Natalia Criado , Maite Lopez-Sanchez , Simon Parsons , Juan A. Rodriguez-Aguilar

A notion of probabilistic lambda-calculus usually comes with a prescribed reduction strategy, typically call-by-name or call-by-value, as the calculus is non-confluent and these strategies yield different results. This is a break with one…

Logic in Computer Science · Computer Science 2020-02-21 Ugo Dal Lago , Giulio Guerrieri , Willem Heijltjes

Logistic models are studied as a tool to convert output from numerical weather forecasting systems (deterministic and ensemble) into probability forecasts for binary events. A logistic model obtains by putting the logarithmic odds ratio…

Atmospheric and Oceanic Physics · Physics 2009-01-29 Jochen Bröcker

Symbolic computation, powered by modern computer algebra systems, has important applications in mathematical reasoning through exact deep computations. The efficiency of symbolic computation is largely constrained by such deep computations…

Symbolic Computation · Computer Science 2026-01-21 Rui-Juan Jing , Yuegang Zhao , Changbo Chen

Probabilistic Logic Programming is an effective formalism for encoding problems characterized by uncertainty. Some of these problems may require the optimization of probability values subject to constraints among probability distributions…

Logic in Computer Science · Computer Science 2023-06-22 Damiano Azzolini , Fabrizio Riguzzi

The vast majority of real world classification problems are imbalanced, meaning there are far fewer data from the class of interest (the positive class) than from other classes. We propose two machine learning algorithms to handle highly…

Machine Learning · Statistics 2014-06-10 Siong Thye Goh , Cynthia Rudin

This two-part paper presents a new approach to predictive analysis for social processes. Part I identifies a class of social processes, called positive externality processes, which are both important and difficult to predict, and introduces…

Adaptation and Self-Organizing Systems · Physics 2016-11-17 Richard Colbaugh , Kristin Glass

Exploring data requires a fast feedback loop from the analyst to the system, with a latency below about 10 seconds because of human cognitive limitations. When data becomes large or analysis becomes complex, sequential computations can no…

Human-Computer Interaction · Computer Science 2016-07-19 Jean-Daniel Fekete , Romain Primet

We propose a combined model, which integrates the latent factor model and the logistic regression model, for the citation network. It is noticed that neither a latent factor model nor a logistic regression model alone is sufficient to…

Machine Learning · Statistics 2019-12-03 Namjoon Suh , Xiaoming Huo , Eric Heim , Lee Seversky

The computational method of parametric probability analysis is introduced. It is demonstrated how to embed logical formulas from the propositional calculus into parametric probability networks, thereby enabling sound reasoning about the…

Logic · Mathematics 2012-05-24 Joseph W. Norman

The syntactic nature of logic and computation separates them from other fields of mathematics. Nevertheless, syntax has been the only way to adequately capture the dynamics of proofs and programs such as cut-elimination, and the finiteness…

Logic · Mathematics 2023-06-14 Norihiro Yamada

Categorical regressor variables are usually handled by introducing a set of indicator variables, and imposing a linear constraint to ensure identifiability in the presence of an intercept, or equivalently, using one of various coding…

Computation · Statistics 2018-05-21 Felicitas J. Detmer , Martin Slawski

Symbolic data analysis (SDA) is an emerging area of statistics concerned with understanding and modelling data that takes distributional form (i.e. symbols), such as random lists, intervals and histograms. It was developed under the premise…

Computation · Statistics 2020-04-09 Boris Beranger , Huan Lin , Scott A. Sisson

Machine learning can provide predictions with disparate outcomes, in which subgroups of the population (e.g., defined by age, gender, or other sensitive attributes) are systematically disadvantaged. In order to comply with upcoming…

Machine Learning · Computer Science 2024-01-25 Moritz von Zahn , Oliver Hinz , Stefan Feuerriegel

Many representation schemes combining first-order logic and probability have been proposed in recent years. Progress in unifying logical and probabilistic inference has been slower. Existing methods are mainly variants of lifted variable…

Artificial Intelligence · Computer Science 2012-02-20 Vibhav Gogate , Pedro Domingos

A full parametric and linear specification may be insufficient to capture complicated patterns in studies exploring complex features, such as those investigating age-related changes in brain functional abilities. Alternatively, a partially…

Methodology · Statistics 2024-02-07 Jia Liang , Shuo Chen , Peter Kochunov , L Elliot Hong , Chixiang Chen

We introduce a family of interpretable machine learning models, with two broad additions: Linearised Additive Models (LAMs) which replace the ubiquitous logistic link function in General Additive Models (GAMs); and SubscaleHedge, an expert…

Machine Learning · Computer Science 2022-11-14 Danial Dervovic , Nicolas Marchesotti , Freddy Lecue , Daniele Magazzeni