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Cluster analysis methods are used to identify homogeneous subgroups in a data set. In biomedical applications, one frequently applies cluster analysis in order to identify biologically interesting subgroups. In particular, one may wish to…

Methodology · Statistics 2016-09-23 Sheila Gaynor , Eric Bair

Statistical models that possess symmetry arise in diverse settings such as random fields associated to geophysical phenomena, exchangeable processes in Bayesian statistics, and cyclostationary processes in engineering. We formalize the…

Statistics Theory · Mathematics 2011-12-01 Parikshit Shah , Venkat Chandrasekaran

We consider a generalization of group testing where the potentially contaminated sets are the members of a given hypergraph ${\cal F}=(V,E)$. This generalization finds application in contexts where contaminations can be conditioned by some…

Data Structures and Algorithms · Computer Science 2023-11-28 Annalisa De Bonis

In literature, many important combinatorial properties of subsets of N have been studied both with nonstandard techniques and from the point of view of N. In this thesis we mix these two different approaches in a technique that, at the same…

Logic · Mathematics 2012-12-11 Lorenzo Luperi Baglini

Ensemble methods combine the predictions of multiple models to improve performance, but they require significantly higher computation costs at inference time. To avoid these costs, multiple neural networks can be combined into one by…

Machine Learning · Computer Science 2024-05-07 Alexia Jolicoeur-Martineau , Emy Gervais , Kilian Fatras , Yan Zhang , Simon Lacoste-Julien

Model selection strategies have been routinely employed to determine a model for data analysis in statistics, and further study and inference then often proceed as though the selected model were the true model that were known a priori. This…

Methodology · Statistics 2018-02-13 Priyam Mitra , Heng Lian , Ritwik Mitra , Hua Liang , Min-ge Xie

Ensembles of artificial neural networks show improved generalization capabilities that outperform those of single networks. However, for aggregation to be effective, the individual networks must be as accurate and diverse as possible. An…

Artificial Intelligence · Computer Science 2007-05-23 P. M. Granitto , P. F. Verdes , H. A. Ceccatto

This paper develops a theory for group Lasso using a concept called strong group sparsity. Our result shows that group Lasso is superior to standard Lasso for strongly group-sparse signals. This provides a convincing theoretical…

Machine Learning · Statistics 2009-03-17 Junzhou Huang , Tong Zhang

The pseudo--rigid body represents an example of a constrained system with a nonunimodular gauge group. This system is treated along the guidelines of an algebraic constraint quantization scheme which focusses on observable quantities,…

High Energy Physics - Theory · Physics 2008-02-03 Michael Trunk

In the context of averaging method, we describe a reconstruction of invariant connection-dependent Poisson structures from canonical actions of compact Lie groups on fibered phase spaces. Some symmetry properties of Wong's type equations…

Differential Geometry · Mathematics 2023-12-18 M. Avendaño-Camacho , J. C. Ruíz-Pantaleón , Yu. Vorobiev

Properties of weighted averages are studied for the general case that the individual measurements are subject to hidden correlations and have asymmetric statistical as well as systematic errors. Explicit expressions are derived for an…

High Energy Physics - Experiment · Physics 2007-05-23 Michael Schmelling

In this contribution we discuss the role disordered (or random) systems have played in the study of non-Gibbsian measures. This role has two main aspects, the distinction between which has not always been fully clear: 1) {\em From}…

Mathematical Physics · Physics 2015-06-26 A. C. D. van Enter , C. Kuelske

We discuss the classical statement of group classification problem and some its extensions in the general case. After that, we carry out the complete extended group classification for a class of (1+1)-dimensional nonlinear…

Mathematical Physics · Physics 2010-11-03 N. M. Ivanova , R. O. Popovych , C. Sophocleous

A sumset semigroup is a non-cancellative commutative monoid obtained from the sumset of finite non-negative integer sets. In this work, an algorithm for computing the ideals associated with some sumset semigroups is provided. Using these…

Number Theory · Mathematics 2021-10-06 J. I. García-García , D. Marín-Aragón , A. Vigneron-Tenorio

Density aggregation is a central problem in machine learning, for instance when combining predictions from a Deep Ensemble. The choice of aggregation remains an open question with two commonly proposed approaches being linear pooling…

We present a new approach to the calculation of measures in weighted networks, based on the translation of a weighted network into an ensemble of edges. This leads to a straightforward generalization of any measure defined on unweighted…

Statistical Mechanics · Physics 2009-07-06 S. E. Ahnert , D. Garlaschelli , T. M. Fink , G. Caldarelli

A class of exact infinitesimal renormalization group transformations is proposed and studied. These transformations are pure changes of variables (i.e., no integration or elimination of some degrees of freedom is required) such that a…

High Energy Physics - Theory · Physics 2017-11-08 Ariel Caticha

Gauge theories in axial gauges are studied using Exact Renormalisation Group flows. We introduce a background field in the infrared regulator, but not in the gauge fixing, in contrast to the usual background field gauge. It is shown how…

High Energy Physics - Theory · Physics 2009-11-07 Daniel F. Litim , Jan M. Pawlowski

We consider the problem of detecting a small subset of defective items from a large set via non-adaptive "random pooling" group tests. We consider both the case when the measurements are noiseless, and the case when the measurements are…

Information Theory · Computer Science 2011-07-25 Chun Lam Chan , Pak Hou Che , Sidharth Jaggi , Venkatesh Saligrama

We consider "nonconventional" averaging setup in the form $\frac {dX^\epsilon(t)}{dt}=\epsilon B\big(X^\epsilon(t),\xi(q_1(t)), \xi(q_2(t)),...,\xi(q_\ell(t))\big)$ where $\xi(t),t\geq 0$ is either a stochastic process or a dynamical system…

Probability · Mathematics 2013-02-21 Yuri Kifer
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