Munk, Axel

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07.03.2025

Optimal and fast online change point estimation in linear regression

Authors Hüselitz A, Li H, Munk A Journal Arxiv Citation arXiv:2503.05270. Abstract We consider the problem of sequential estimation of a single change point in a piecewise linear regression model under a Gaussian setup. We demonstrate that a certain CUSUM-type statistic attains the minimax optimal rates for localizing the change
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04.03.2025

Adaptive monotonicity testing in sublinear time

Authors Li H, Liu Z, Munk A Journal Arxiv Citation arXiv:2503.03020. Abstract Modern large-scale data analysis increasingly faces the challenge of achieving computational efficiency as well as statistical accuracy, as classical statistically efficient methods often fall short in the first regard. In the context of testing monotonicity of a regression
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20.01.2025

Robust inference of cooperative behaviour of multiple ion channels in voltage-clamp recordings

Authors Requardt R, Fink M, Kubica P, Steinem C, Munk A, Li H   Journal IEEE Transactions on NanoBioScience (TNB)   Citation IEEE Transactions on NanoBioScience (TNB). 2025.   Abstract Recent experimental studies have shed light on the intriguing possibility that ion channels exhibit cooperative behaviour. However, a comprehensive understanding
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23.12.2024

Global alignment and local curvature of microtubules in mouse fibroblasts are robust against perturbations of vimentin and actin

Authors Blob A, Ventzke D, Rölleke U, Nies G, Munk A, Schaedel L, Köster S   Journal Soft Matter   Citation Soft Matter, 2025.   Abstract The eukaryotic cytoskeleton is an intricate network of three types of mechanically distinct biopolymers — actin filaments, microtubules and intermediate filaments (IFs). These filamentous
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01.11.2024

A Unifying Approach to Distributional Limits for Empirical Optimal Transport

Authors Hundrieser S, Klatt M, Munk A, Staudt T Journal Bernoulli Citation Bernoulli 30 (4), 2846-2877. Abstract We provide a unifying approach to central limit type theorems for empirical optimal transport (OT). The limit distribution is given by a supremum of a centered Gaussian process, and we explicitly characterize when it
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30.10.2024

Identifiability of the Optimal Transport Cost on Finite Spaces

Authors Gonualez-Sanz A, Groppe M, Munk A Journal Arxiv Citation arXiv:2410.23146. Abstract The goal of optimal transport (OT) is to find optimal assignments or matchings between data sets which minimize the total cost for a given cost function. However, sometimes the cost function is unknown but we have access to
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21.10.2024

Multiscale scanning with nuisance parameters

Authors König C, Munk A, Werner F Journal Journal of the Royal Statistical Society Series B: Statistical Methodology Citation J R Stat Soc Series B: Statistical Methodology, 2024, 00, 1–19. Abstract We develop a multiscale scanning method to find anomalies in a d-dimensional random field in the presence of nuisance
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13.09.2024

MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy

Authors Naas J, Nies G, Li H, Stoldt S, Schmitzer B, Jakobs S, Munk A Journal Communications Biology Citation Commun Biol 7, 1139 (2024). Abstract With recent advances in multi-color super-resolution light microscopy, it is possible to simultaneously visualize multiple subunits within biological structures at nanometer resolution. To optimally evaluate
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01.09.2024

Optimistic Search: Change Point Estimation for Large-scale Data via Adaptive Logarithmic Queries

Authors Kovacs S, Li H, Haubner L, Munk A, Buhlmann P Journal Journal of Machine Learning Research Citation J Mach Learn Res 25(297):1−64, 2024. Abstract Change point estimation is often formulated as a search for the maximum of a gain function describing improved fits when segmenting the data. Searching one
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22.08.2024

Empirical Optimal Transport under Estimated Costs: Distributional Limits and Statistical Applications

Authors Hundrieser S, Mordant G. Weitkamp CA, Munk A Journal Stochastic Processes and their Applications Citation Stochastic Processes and their Applications 178 (2024) 104462. Abstract Optimal transport (OT) based data analysis is often faced with the issue that the underlying cost function is (partially) unknown. This is addressed in this
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