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We examine overlapping clustering schemes with functorial constraints, in the spirit of Carlsson--Memoli. This avoids issues arising from the chaining required by partition-based methods. Our principal result shows that any clustering…

Machine Learning · Computer Science 2016-08-16 Jared Culbertson , Dan P. Guralnik , Jakob Hansen , Peter F. Stiller

Modularity is one of the most widely used quality measures for graph clusterings. Maximizing modularity is NP-hard, and the runtime of exact algorithms is prohibitive for large graphs. A simple and effective class of heuristics coarsens the…

Data Structures and Algorithms · Computer Science 2009-09-22 Andreas Noack , Randolf Rotta

We consider the task of estimating a conditional density using i.i.d. samples from a joint distribution, which is a fundamental problem with applications in both classification and uncertainty quantification for regression. For joint…

Statistics Theory · Mathematics 2023-06-16 Blair Bilodeau , Dylan J. Foster , Daniel M. Roy

In a previous paper, we defined a version of the percolation triangle condition that is suitable for the analysis of bond percolation on a finite connected transitive graph, and showed that this triangle condition implies that the…

Probability · Mathematics 2007-05-23 Christian Borgs , Jennifer T. Chayes , Remco van der Hofstad , Gordon Slade , Joel Spencer

We propose a mixture of latent trait models with common slope parameters (MCLT) for model-based clustering of high-dimensional binary data, a data type for which few established methods exist. Recent work on clustering of binary data, based…

Methodology · Statistics 2017-10-09 Yang Tang , Ryan P. Browne , Paul D. McNicholas

We consider the problem of minimizing a function, which is the sum of a linear function and a composition of a strongly convex function with a linear transformation, over a compact polyhedral set. Jaggi and Lacoste-Julien [14] showed that…

Optimization and Control · Mathematics 2015-04-21 Amir Beck , Shimrit Shtern

In this paper, we provide two types of sufficient conditions for ensuring the quadratic growth conditions of a class of constrained convex symmetric and non-symmetric matrix optimization problems regularized by nonsmooth spectral functions.…

Optimization and Control · Mathematics 2017-05-08 Ying Cui , Chao Ding , Xinyuan Zhao

The central limit theorem of martingales is the fundamental tool for studying the convergence of stochastic processes, especially stochastic integrals and differential equations. In this paper, general central limit theorems and functional…

Probability · Mathematics 2020-05-08 Li-Xin Zhang

The complex Langevin method (CLM) provides a promising way to perform the path integral with a complex action using a stochastic equation for complexified dynamical variables. It is known, however, that the method gives wrong results in…

High Energy Physics - Lattice · Physics 2016-12-01 Shinji Shimasaki , Keitaro Nagata , Jun Nishimura

We produce a series of Central Limit Theorems (CLTs) associated to compact metric measure spaces $(K,d,\eta)$, with $\eta$ a reasonable probability measure. For the first CLT, we can ignore $\eta$ by isometrically embedding $K$ into…

Probability · Mathematics 2020-01-14 Steven Rosenberg , Jie Xu

We study Rado functionals and the maximal condition (first introduced by J. M. Barret et al.) in terms of the partition regularity of mixed systems of linear equations and inequalities. By strengthening the maximal Rado condition, we…

Combinatorics · Mathematics 2022-09-19 Paulo Henrique Arruda , Lorenzo Luperi Baglini

Let $V_* : \mathbb{R}^d \to \mathbb{R}$ be some (possibly non-convex) potential function, and consider the probability measure $\pi \propto e^{-V_*}$. When $\pi$ exhibits multiple modes, it is known that sampling techniques based on…

Optimization and Control · Mathematics 2023-02-24 Carles Domingo-Enrich , Aram-Alexandre Pooladian

We prove Berry-Esseen type rates of convergence for central limit theorems (CLTs) of regenerative processes which generalize previous results of Bolthausen under weaker moment assumptions. We then show how this general result can be applied…

Probability · Mathematics 2017-10-03 Xiaoqin Guo , Jonathon Peterson

We employ a fuzzy optimality condition for the Frechet subdifferential and some advanced techniques of variational analysis such as formulae for the subdifferentials of an infinite family of nonsmooth functions and the coderivative…

Optimization and Control · Mathematics 2022-11-16 Maryam Saadati , Morteza Oveisiha

We establish limit theorems involving weak convergence of multiple generations of critical and supercritical branching processes. These results arise naturally when dealing with the joint asymptotic behavior of functionals defined in terms…

Probability · Mathematics 2009-12-25 James Kuelbs , Anand N. Vidyashankar

Statistical inference for non-stationary data is hindered by the failure of classical central limit theorems (CLTs), not least because there is no fixed Gaussian limit to converge to. To resolve this, we introduce relative weak convergence,…

Statistics Theory · Mathematics 2025-10-28 Nicolai Palm , Thomas Nagler

We consider substitution tilings and Delone sets without the assumption of finite local complexity (FLC). We first give a sufficient condition for tiling dynamical systems to be uniquely ergodic and a formula for the measure of cylinder…

Dynamical Systems · Mathematics 2019-10-18 Jeong-Yup Lee , Boris Solomyak

A microscopic approach is presented for calculating general properties of interacting Brownian particles under steady shearing. We start from exact expressions for shear-dependent steady-state averages, such as correlation and structure…

Soft Condensed Matter · Physics 2009-07-02 Matthias Fuchs , Michael E. Cates

We present a systematic study on minimally perturbed neutrino mass matrices which at the leading order give rise to Tri-BiMaximal (TBM) mixing due to a residual $\mathbb{Z}_2\times \mathbb{Z}_2^{\mu\tau}$ Klein symmetry in the neutrino mass…

High Energy Physics - Phenomenology · Physics 2019-02-07 Rome Samanta , Mainak Chakraborty

The tetrad constraint is a condition of which the satisfaction signals a rank reduction of a covariance submatrix and is used to design causal discovery algorithms that detects the existence of latent (unmeasured) variables, such as FOFC.…

Machine Learning · Statistics 2020-09-30 Shuyan Wang