Munk, Axel

Authors Struleva M, Hundrieser S, Schuhmacher D, Munk A Journal Arxiv Citation arXiv:2509.04225. Abstract We statistically analyze empirical plug-in estimators for unbalanced optimal transport (UOT) formalisms, focusing on the Kantorovich-Rubinstein distance, between general intensity measures based on observations from spatio-temporal point processes. Specifically, we model the observations by two weakly

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

Authors Hundrieser S, Manole T, Litskevich D, Munk A Journal Arxiv Citation arXiv:2508.00824. Abstract We analyze the statistical problem of recovering an atomic signal, modeled as a discrete uniform distribution μ, from a binned Poisson convolution model. This question is motivated, among others, by super-resolution laser microscopy applications, where precise

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

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

Authors Hundrieser S, Heinemann F, Klatt M, Struleva M, Munk A Journal Journal of Machine Learning Research Citation Journal of Machine Learning Research 26 (2025) 1-70. Abstract We analyze statistical properties of plug-in estimators for unbalanced optimal transport quantities between finitely supported measures in different prototypical sampling models. Specifically, our

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

Authors Blob A, Ventzke D, Rölleke U, Nies G, Munk A, Schaedel L, Köster S   Journal Soft Matter   Citation Soft Matter, 2025, 21, 641-651.   Abstract The eukaryotic cytoskeleton is an intricate network of three types of mechanically distinct biopolymers — actin filaments, microtubules and intermediate filaments (IFs).

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

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