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Feedforward neural networks (FNNs) are typically viewed as pure prediction algorithms, and their strong predictive performance has led to their use in many machine-learning applications. However, their flexibility comes with an…

Methodology · Statistics 2023-11-15 Andrew McInerney , Kevin Burke

Most work in neural networks focuses on estimating the conditional mean of a continuous response variable given a set of covariates.In this article, we consider estimating the conditional distribution function using neural networks for both…

Methodology · Statistics 2022-07-07 Bingqing Hu , Bin Nan

There has been an ever-increasing interest in multidisciplinary research on representing and reasoning with imperfect data. Possibilistic networks present one of the powerful frameworks of interest for representing uncertain and imprecise…

Artificial Intelligence · Computer Science 2016-07-14 Maroua Haddad , Philippe Leray , Nahla Ben Amor

Comparing weighted networks in neuroscience is hard, because the topological properties of a given network are necessarily dependent on the number of edges of that network. This problem arises in the analysis of both weighted and unweighted…

Applications · Statistics 2014-03-28 Cedric E. Ginestet , Arnaud P. Fournel , Andrew Simmons

Neural network pruning techniques reduce the number of parameters without compromising predicting ability of a network. Many algorithms have been developed for pruning both over-parameterized fully-connected networks (FCNs) and…

Machine Learning · Computer Science 2021-05-24 Xin Qian , Diego Klabjan

There has been concern within the artificial intelligence (AI) community and the broader society regarding the potential lack of fairness of AI-based decision-making systems. Surprisingly, there is little work quantifying and guaranteeing…

Machine Learning · Computer Science 2023-01-31 Wenbin Zhang , Jeremy C. Weiss

Statistical learning in high-dimensional spaces is challenging without a strong underlying data structure. Recent advances with foundational models suggest that text and image data contain such hidden structures, which help mitigate the…

Machine Learning · Statistics 2025-02-04 Charles Arnal , Clement Berenfeld , Simon Rosenberg , Vivien Cabannes

The network data has attracted considerable attention in modern statistics. In research on complex network data, one key issue is finding its underlying connection structure given a network sample. The methods that have been proposed in…

Methodology · Statistics 2024-08-09 Kang Fu , Jianwei Hu , Seydou Keita

Estimating how uncertain an AI system is in its predictions is important to improve the safety of such systems. Uncertainty in predictive can result from uncertainty in model parameters, irreducible data uncertainty and uncertainty due to…

Machine Learning · Statistics 2018-12-03 Andrey Malinin , Mark Gales

The past few years has witnessed the great success of recommender systems, which can significantly help users find relevant and interesting items for them in the information era. However, a vast class of researches in this area mainly focus…

Information Retrieval · Computer Science 2012-04-10 Xiao Hu , Chuibo Chen , Xiaolong Chen , Zi-Ke Zhang

Link prediction is one of the fundamental problems in network analysis. In many applications, notably in genetics, a partially observed network may not contain any negative examples of absent edges, which creates a difficulty for many…

Machine Learning · Statistics 2013-01-30 Yunpeng Zhao , Elizaveta Levina , Ji Zhu

When the historical data are limited, the conditional probabilities associated with the nodes of Bayesian networks are uncertain and can be empirically estimated. Second order estimation methods provide a framework for both estimating the…

Machine Learning · Statistics 2022-08-09 Conrad D. Hougen , Lance M. Kaplan , Federico Cerutti , Alfred O. Hero

Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using a…

Machine Learning · Statistics 2023-05-02 Aliaksandr Hubin , Geir Storvik

The continuous net reclassification improvement (NRI) statistic is a popular model change measure that was developed to assess the incremental value of new factors in a risk prediction model. Two prominent statistical issues identified in…

Methodology · Statistics 2022-04-08 Glenn Heller

Prediction models based on deep neural networks are increasingly gaining attention for fast and accurate virtual screening systems. For decision makings in virtual screening, researchers find it useful to interpret an output of…

Machine Learning · Computer Science 2020-03-18 Soojung Yang , Kyung Hoon Lee , Seongok Ryu

Fake news poses global risks by influencing elections and spreading misinformation, making detection critical. Existing NLP and supervised Machine Learning methods perform well under cross-validation but struggle to generalise across…

Machine Learning · Computer Science 2025-02-28 Nathaniel Hoy , Theodora Koulouri

Deep Bayesian neural networks (BNNs) are a powerful tool, though computationally demanding, to perform parameter estimation while jointly estimating uncertainty around predictions. BNNs are typically implemented using arbitrary…

Machine Learning · Computer Science 2020-05-12 Daniele Silvestro , Tobias Andermann

Mechanistic network models can capture salient characteristics of empirical networks using a small set of domain-specific, interpretable mechanisms. Yet inference remains challenging because the likelihood is often intractable. We show…

Social and Information Networks · Computer Science 2025-12-30 Till Hoffmann , Jukka-Pekka Onnela

The fair-ranking problem, which asks to rank a given set of items to maximize utility subject to group fairness constraints, has received attention in the fairness, information retrieval, and machine learning literature. Recent works,…

Machine Learning · Computer Science 2022-12-01 Anay Mehrotra , Nisheeth K. Vishnoi

Network models are widely used to represent relational information among interacting units and the structural implications of these relations. Recently, social network studies have focused a great deal of attention on random graph models of…

Applications · Statistics 2010-10-06 Mark S. Handcock , Krista J. Gile