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We prove a mean ergodic theorem for amenable discrete quantum groups. As an application, we prove a Wiener type theorem for continuous measures on compact metrizable groups.

Operator Algebras · Mathematics 2016-07-14 Huichi Huang

Interpretability methods are valuable only if their explanations faithfully describe the explained model. In this work, we consider neural networks whose predictions are invariant under a specific symmetry group. This includes popular…

Machine Learning · Computer Science 2023-10-06 Jonathan Crabbé , Mihaela van der Schaar

We construct an infinitely exchangeable process on the set $\cate$ of subsets of the power set of the natural numbers $\mathbb{N}$ via a Poisson point process with mean measure $\Lambda$ on the power set of $\mathbb{N}$. Each $E\in\cate$…

Statistics Theory · Mathematics 2011-10-25 Harry Crane

We initiate the study of property testing problems concerning relations between permutations. In such problems, the input is a tuple $(\sigma_1,\dotsc,\sigma_d)$ of permutations on $\{1,\dotsc,n\}$, and one wishes to determine whether this…

Data Structures and Algorithms · Computer Science 2024-07-11 Oren Becker , Alexander Lubotzky , Jonathan Mosheiff

This paper presents algebraic methods for the study of polynomial relative invariants, when the group G formed by the symmetries and relative symmetries is a compact Lie group. We deal with the case when the subgroup H of symmetries is…

Dynamical Systems · Mathematics 2012-07-09 Patricia H. Baptistelli , Miriam Manoel

Applied statistical problems often come with pre-specified groupings to predictors. It is natural to test for the presence of simultaneous group-wide signal for groups in isolation, or for multiple groups together. Classical tests for the…

Applications · Statistics 2015-11-25 Stephen Reid , Jonathan Taylor , Robert Tibshirani

The postulate of independence of cause and mechanism (ICM) has recently led to several new causal discovery algorithms. The interpretation of independence and the way it is utilized, however, varies across these methods. Our aim in this…

Machine Learning · Statistics 2017-05-08 Michel Besserve , Naji Shajarisales , Bernhard Schölkopf , Dominik Janzing

A multivariate dispersion control chart monitors changes in the process variability of multiple correlated quality characteristics. In this article, we investigate and compare the performance of charts designed to monitor variability based…

Methodology · Statistics 2019-06-20 Jimoh Olawale Ajadi , Inez Maria Zwetsloot

Given a random sample from a random variable $T$ which is bounded from above, $T\le\tau$ a.s., we define processes that are positive supermartingales if $E(T)\ge\mu$. Such processes are called test martingales. Tests of the supermartingale…

Methodology · Statistics 2018-02-20 Harrie Hendriks

We introduce a class of Monte Carlo estimators that aim to overcome the rapid growth of variance with dimension often observed for standard estimators by exploiting the target's independence structure. We identify the most basic…

Statistics Theory · Mathematics 2021-11-02 Juan Kuntz , Francesca R. Crucinio , Adam M. Johansen

In [6], a constraint on invariant measures of bi-permutative cellular automata has been observed: fixed values at the positive indices determine almost-surely a uniform conditional probability on the subset of values of positive conditional…

Dynamical Systems · Mathematics 2026-05-28 Matan Tal

A machine learning model is traditionally considered robust if its prediction remains (almost) constant under input perturbations with small norm. However, real-world tasks like molecular property prediction or point cloud segmentation have…

Machine Learning · Computer Science 2024-01-17 Jan Schuchardt , Yan Scholten , Stephan Günnemann

The preferential sampling of locations chosen to observe a spatio-temporal process has been identified as a major problem across multiple fields. Predictions of the process can be severely biased when standard statistical methodologies are…

Methodology · Statistics 2020-03-05 Joe Watson

Random effects are a flexible addition to statistical models to capture structural heterogeneity in the data, such as spatial dependencies, individual differences, temporal dependencies, or non-linear effects. Testing for the presence (or…

Methodology · Statistics 2024-10-21 Fabio Vieira , Hongwei Zhao , Joris Mulder

We study how the problem of observables is fully resolved for background independent theories defined on finite graphs. We argue the correct analogue of coordinate independence is the invariance under changes of graph labels, a kind of…

General Relativity and Quantum Cosmology · Physics 2025-08-05 Emil Broukal , Andrea Di Biagio , Eugenio Bianchi , Marios Christodoulou

We describe a method to determine the eigenvalue density of empirical covariance matrix in the presence of correlations between samples. This is a straightforward generalization of the method developed earlier by the authors for…

Statistical Mechanics · Physics 2008-12-02 Z. Burda , J. Jurkiewicz , B. Waclaw

A rank-invariant clustering of variables is introduced that is based on the predictive strength between groups of variables, i.e., two groups are assigned a high similarity if the variables in the first group contain high predictive…

Methodology · Statistics 2023-12-29 Sebastian Fuchs , Yuping Wang

On unitary compact groups the decomposition of a generic element into product of reflections induces a decomposition of the characteristic polynomial into a product of factors. When the group is equipped with the Haar probability measure,…

Probability · Mathematics 2010-03-25 Paul Bourgade , Ashkan Nikeghbali , Alain Rouault

A measure preserving action of a countably infinite group \Gamma is called totally ergodic if every infinite subgroup of \Gamma acts ergodically. For example, all mixing and mildly mixing actions are totally ergodic. This note shows that if…

Dynamical Systems · Mathematics 2012-08-06 Robin Tucker-Drob

We introduce inherent measures for effective quality control of brain segmentation based on a Bayesian fully convolutional neural network, using model uncertainty. Monte Carlo samples from the posterior distribution are efficiently…

Computer Vision and Pattern Recognition · Computer Science 2018-06-11 Abhijit Guha Roy , Sailesh Conjeti , Nassir Navab , Christian Wachinger