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The graph similarity problem, also known as approximate graph isomorphism or graph matching problem, has been extensively studied in the machine learning community, but has not received much attention in the algorithms community: Given two…

Data Structures and Algorithms · Computer Science 2018-02-26 Martin Grohe , Gaurav Rattan , Gerhard J. Woeginger

Numerous approximation algorithms for problems on unit disk graphs have been proposed in the literature, exhibiting a sharp trade-off between running times and approximation ratios. We introduce a variation of the known shifting strategy…

Data Structures and Algorithms · Computer Science 2016-11-08 Guilherme D. da Fonseca , Vinícius G. Pereira de Sá , Celina M. H. de Figueiredo

Stein's method provides a way of bounding the distance of a probability distribution to a target distribution $\mu$. Here we develop Stein's method for the class of discrete Gibbs measures with a density $e^V$, where $V$ is the energy…

Probability · Mathematics 2008-08-22 Peter Eichelsbacher , Gesine Reinert

Dynamic Time Wrapping (DTW) is a widely used algorithm for measuring similarities between two time series. It is especially valuable in a wide variety of applications, such as clustering, anomaly detection, classification, or video…

Machine Learning · Computer Science 2023-01-31 Hugo Lerogeron , Romain Picot-Clemente , Alain Rakotomamonjy , Laurent Heutte

We present a numerical analysis of the entropy rate and statistical complexity related to the spin flip dynamics of the 2D Ising Ferromagnet at different temperatures T. We follow an information theoretic approach and test three different…

Statistical Mechanics · Physics 2012-07-02 O. Melchert , A. K. Hartmann

We study the problem of estimating the Earth Mover's Distance (EMD) between probability distributions when given access only to samples. We give closeness testers and additive-error estimators over domains in $[0, \Delta]^d$, with sample…

Data Structures and Algorithms · Computer Science 2009-04-03 Khanh Do Ba , Huy L Nguyen , Huy N Nguyen , Ronitt Rubinfeld

We consider inverse problems with large null spaces, which arise in important applications such as in inverse ECG and EEG procedures. Standard regularization methods typically produce solutions in or near the orthogonal complement of the…

Numerical Analysis · Mathematics 2025-12-05 Martin Burger , Ole Løseth Elvetun , Bjørn Fredrik Nielsen

We deal with stochastic differential equations with jumps. In order to obtain an accurate approximation scheme, it is usual to replace the "small jumps" by a Brownian motion. In this paper, we prove that for every fixed time $t$, the…

Probability · Mathematics 2023-10-17 Vlad Bally , Yifeng Qin

In this article, we first obtain, for the Kolmogorov distance, an error bound between a tempered stable and a compound Poisson distribution and also an error bound between a tempered stable and an alpha stable distribution via Stein method.…

Probability · Mathematics 2024-08-20 Kalyan Burman , Neelesh S Upadhye , Palaniappan Vellaisamy

The continuous random energy model (CREM) is a toy model of spin glasses on $\{0,1\}^N$ that, in the limit, exhibits an infinitely hierarchical correlation structure. We give two polynomial-time algorithms to approximately sample from the…

Probability · Mathematics 2025-02-20 Holden Lee , Qiang Wu

A sampling algorithm is presented that generates spin glass configurations of the 2D Edwards-Anderson Ising spin glass at finite temperature, with probabilities proportional to their Boltzmann weights. Such an algorithm overcomes the slow…

Disordered Systems and Neural Networks · Physics 2009-10-30 Creighton K. Thomas , A. Alan Middleton

By the continuous mapping theorem, if a sequence of $d$-dimensional random vectors $(\mathbf{W}_n)_{n\geq1}$ converges in distribution to a multivariate normal random variable $\Sigma^{1/2}\mathbf{Z}$, then the sequence of random variables…

Probability · Mathematics 2020-03-18 Robert E. Gaunt

We study the existence of jointly measurable POVM approximations to two non-commuting sharp spin observables. We compare two different ways to specify optimal approximations.

Quantum Physics · Physics 2008-11-16 Teiko Heinosaari , Peter Stano , Daniel Reitzner

Let $X_1,X_2,...,X_n$ be a sequence of independent or locally dependent random variables taking values in $\mathbb{Z}_+$. In this paper, we derive sharp bounds, via a new probabilistic method, for the total variation distance between the…

Statistics Theory · Mathematics 2010-10-11 Michael V. Boutsikas , Eutichia Vaggelatou

Spin-glasses are natural Gibbs distributions that have been studied in Theoretical CS for many decades. Recently, they have been gaining attention from the community as they emerge naturally in neural computation and learning, network…

Discrete Mathematics · Computer Science 2026-03-25 Charilaos Efthymiou , Kostas Zampetakis

We prove Gibbs distribution of two-state spin systems(also known as binary Markov random fields) without hard constrains on a tree exhibits strong spatial mixing(also known as strong correlation decay), under the assumption that, for…

Discrete Mathematics · Computer Science 2009-03-05 Jinshan Zhang

Bias evaluation is fundamental to trustworthy AI, both in terms of checking data quality and in terms of checking the outputs of AI systems. In testing data quality, for example, one may study the distance of a given dataset, viewed as a…

Machine Learning · Computer Science 2025-06-12 Jiří Němeček , Mark Kozdoba , Illia Kryvoviaz , Tomáš Pevný , Jakub Mareček

When a rigid rough solid slides on a rigid rough surface, it experiences a random motion in the direction normal to the average contact plane. Here, through simulations of the separation at single-point contact between self-affine…

A two-state spin system is specified by a 2 x 2 matrix A = {A_{0,0} A_{0,1}, A_{1,0} A_{1,1}} = {\beta 1, 1 \gamma} where \beta, \gamma \ge 0. Given an input graph G=(V,E), the partition function Z_A(G) of a system is defined as Z_A(G) =…

Computational Complexity · Computer Science 2015-03-20 Jin-Yi Cai , Xi Chen , Heng Guo , Pinyan Lu

Computing the discrepancy between time series of variable sizes is notoriously challenging. While dynamic time warping (DTW) is popularly used for this purpose, it is not differentiable everywhere and is known to lead to bad local optima…

Machine Learning · Computer Science 2021-03-01 Mathieu Blondel , Arthur Mensch , Jean-Philippe Vert