Li, Housen

[["dc.contributor.author","Pelizzola, Marta"],["dc.contributor.author","Behr, Merle"],["dc.contributor.author","Li, Housen"],["dc.contributor.author","Munk, Axel"],["dc.contributor.author","Futschik, Andreas"],["dc.date.accessioned","2022-02-23T13:16:07Z"],["dc.date.available","2022-02-23T13:16:07Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1101/2020.07.09.191924"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/100361"],["dc.identifier.url","https://mbexc.uni-goettingen.de/literature/publications/156"],["dc.relation","EXC 2067: Multiscale Bioimaging"],["dc.relation.workinggroup","RG Li"],["dc.relation.workinggroup","RG Munk"],["dc.title","Multiple Haplotype Reconstruction from Allele Frequency Data"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","283"],["dc.bibliographiccitation.lastpage","312"],["dc.bibliographiccitation.volume","134"],["dc.contributor.author","Munk, Axel"],["dc.contributor.author","Proksch, Katharina"],["dc.contributor.author","Li, Housen"],["dc.contributor.author","Werner, Frank"],["dc.contributor.editor","Salditt, Tim"],["dc.contributor.editor","Egner, Alexander"],["dc.contributor.editor","Luke, D. Russell"],["dc.date.accessioned","2021-03-05T08:58:56Z"],["dc.date.available","2021-03-05T08:58:56Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1007/978-3-030-34413-9_11"],["dc.identifier.eisbn","978-3-030-34413-9"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/80304"],["dc.notes.intern","DOI Import GROB-393"],["dc.publisher","Springer International Publishing"],["dc.publisher.place","Cham"],["dc.relation.eissn","1437-0859"],["dc.relation.isbn","978-3-030-34412-2"],["dc.relation.ispartof","Nanoscale Photonic Imaging"],["dc.relation.issn","0303-4216"],["dc.title","Photonic Imaging with Statistical Guarantees: From Multiscale Testing to Multiscale Estimation"],["dc.type","book_chapter"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1039"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Magnetic Resonance in Medicine"],["dc.bibliographiccitation.lastpage","1048"],["dc.bibliographiccitation.volume","72"],["dc.contributor.author","Li, Housen"],["dc.contributor.author","Haltmeier, Markus"],["dc.contributor.author","Zhang, Shuo"],["dc.contributor.author","Frahm, Jens"],["dc.contributor.author","Munk, Axel"],["dc.date.accessioned","2017-09-07T11:45:30Z"],["dc.date.available","2017-09-07T11:45:30Z"],["dc.date.issued","2014"],["dc.description.abstract","PurposeIn real-time MRI serial images are generally reconstructed from highly undersampled datasets as the iterative solutions of an inverse problem. While practical realizations based on regularized nonlinear inversion (NLINV) have hitherto been surprisingly successful, strong assumptions about the continuity of image features may affect the temporal fidelity of the estimated reconstructions. Theory and MethodsThe proposed method for real-time image reconstruction integrates the deformations between nearby frames into the data consistency term of the inverse problem. The aggregated motion estimation (AME) is not required to be affine or rigid and does not need additional measurements. Moreover, it handles multi-channel MRI data by simultaneously determining the image and its coil sensitivity profiles in a nonlinear formulation which also adapts to non-Cartesian (e.g., radial) sampling schemes. The new method was evaluated for real-time MRI studies using highly undersampled radial gradient-echo sequences. ResultsAME reconstructions for a motion phantom with controlled speed as well as for measurements of human heart and tongue movements demonstrate improved temporal fidelity and reduced residual undersampling artifacts when compared with NLINV reconstructions without motion estimation. ConclusionNonlinear inverse reconstructions with aggregated motion estimation offer improved image quality and temporal acuity for visualizing rapid dynamic processes by real-time MRI. Magn Reson Med 72:1039-1048, 2014. (c) 2013 Wiley Periodicals, Inc."],["dc.identifier.doi","10.1002/mrm.25020"],["dc.identifier.gro","3142044"],["dc.identifier.isi","000342342300015"],["dc.identifier.pmid","24243541"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/3923"],["dc.language.iso","en"],["dc.notes.intern","WoS Import 2017-03-10"],["dc.notes.status","final"],["dc.notes.submitter","PUB_WoS_Import"],["dc.relation.eissn","1522-2594"],["dc.relation.issn","0740-3194"],["dc.title","Aggregated Motion Estimation for Real-Time MRI Reconstruction"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Kovács, Solt"],["dc.contributor.author","Li, Housen"],["dc.contributor.author","Haubner, Lorenz"],["dc.contributor.author","Munk, Axel"],["dc.contributor.author","Bühlmann, Peter"],["dc.date.accessioned","2022-02-23T10:50:59Z"],["dc.date.available","2022-02-23T10:50:59Z"],["dc.date.issued","2020-10-20"],["dc.description.abstract","As a classical and ever reviving topic, change point detection is often formulated as a search for the maximum of a gain function describing improved fits when segmenting the data. Searching through all candidate split points on the grid for finding the best one requires (T)$ evaluations of the gain function for an interval with $ observations. If each evaluation is computationally demanding (e.g. in high-dimensional models), this can become infeasible. Instead, we propose optimistic search strategies with (\\log T)$ evaluations exploiting specific structure of the gain function. Towards solid understanding of our strategies, we investigate in detail the classical univariate Gaussian change in mean setup. For some of our proposals we prove asymptotic minimax optimality for single and multiple change point scenarios. Our search strategies generalize far beyond the theoretically analyzed univariate setup. We illustrate, as an example, massive computational speedup in change point detection for high-dimensional Gaussian graphical models. More generally, we demonstrate empirically that our optimistic search methods lead to competitive estimation performance while heavily reducing run-time."],["dc.identifier.arxiv","2010.10194"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/100306"],["dc.identifier.url","https://mbexc.uni-goettingen.de/literature/publications/75"],["dc.relation","EXC 2067: Multiscale Bioimaging"],["dc.relation.workinggroup","RG Li"],["dc.relation.workinggroup","RG Munk"],["dc.title","Optimistic search strategy: Change point detection for large-scale data via adaptive logarithmic queries"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Li, Housen"],["dc.contributor.author","Munk, Axel"],["dc.contributor.author","Sieling, Hannes"],["dc.contributor.author","Walther, Guenther"],["dc.date.accessioned","2017-09-07T11:50:35Z"],["dc.date.available","2017-09-07T11:50:35Z"],["dc.date.issued","2016"],["dc.description.abstract","The histogram is widely used as a simple, exploratory display of data, but it is usually not clear how to choose the number and size of bins for this purpose. We construct a confidence set of distribution functions that optimally address the two main tasks of the histogram: estimating probabilities and detecting features such as increases and (anti)modes in the distribution. We define the essential histogram as the histogram in the confidence set with the fewest bins. Thus the essential histogram is the simplest visualization of the data that optimally achieves the main tasks of the histogram. We provide a fast algorithm for computing a slightly relaxed version of the essential histogram, which still possesses most of its beneficial theoretical properties, and we illustrate our methodology with examples. An R-package is available online."],["dc.identifier.arxiv","1612.07216"],["dc.identifier.gro","3145900"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/3635"],["dc.identifier.url","https://mbexc.uni-goettingen.de/literature/publications/446"],["dc.language.iso","en"],["dc.notes.intern","lifescience"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation","EXC 2067: Multiscale Bioimaging"],["dc.relation.workinggroup","RG Li"],["dc.relation.workinggroup","RG Munk"],["dc.subject","Histogram significant features optimal estimation multiscale testing mode detection"],["dc.title","The Essential Histogram"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","405"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Annales de l´Institut Henri Poincaré. B, Probability and Statistics"],["dc.bibliographiccitation.lastpage","427"],["dc.bibliographiccitation.volume","56"],["dc.contributor.author","Li, Housen"],["dc.contributor.author","Werner, Frank"],["dc.date.accessioned","2020-12-10T18:41:49Z"],["dc.date.available","2020-12-10T18:41:49Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1214/19-AIHP966"],["dc.identifier.issn","0246-0203"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77687"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Empirical risk minimization as parameter choice rule for general linear regularization methods"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1081"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Journal of the Korean Statistical Society"],["dc.bibliographiccitation.lastpage","1089"],["dc.bibliographiccitation.volume","49"],["dc.contributor.author","Kovács, Solt"],["dc.contributor.author","Li, Housen"],["dc.contributor.author","Bühlmann, Peter"],["dc.date.accessioned","2021-03-05T09:05:23Z"],["dc.date.available","2021-03-05T09:05:23Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1007/s42952-020-00077-2"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/80458"],["dc.identifier.url","https://mbexc.uni-goettingen.de/literature/publications/155"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-393"],["dc.relation","EXC 2067: Multiscale Bioimaging"],["dc.relation.eissn","2005-2863"],["dc.relation.issn","1226-3192"],["dc.relation.workinggroup","RG Li"],["dc.title","Seeded intervals and noise level estimation in change point detection: a discussion of Fryzlewicz (2020)"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","del Álamo, Miguel"],["dc.contributor.author","Li, Housen"],["dc.contributor.author","Munk, Axel"],["dc.date.accessioned","2022-02-23T10:02:47Z"],["dc.date.available","2022-02-23T10:02:47Z"],["dc.date.issued","2018-07-05"],["dc.description.abstract","Despite the popularity and practical success of total variation (TV) regularization for function estimation, surprisingly little is known about its theoretical performance in a statistical setting. While TV regularization has been known for quite some time to be minimax optimal for denoising one-dimensional signals, for higher dimensions this remains elusive until today. In this paper we consider frame-constrained TV estimators including many well-known (overcomplete) frames in a white noise regression model, and prove their minimax optimality w.r.t. ^qehBrisk (\\leq q<\\infty$) up to a logarithmic factor in any dimension \\geq 1$. Overcomplete frames are an established tool in mathematical imaging and signal recovery, and their combination with TV regularization has been shown to give excellent results in practice, which our theory now confirms. Our results rely on a novel connection between frame-constraints and certain Besov norms, and on an interpolation inequality to relate them to the risk functional."],["dc.identifier.arxiv","1807.02038"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/100235"],["dc.identifier.url","https://mbexc.uni-goettingen.de/literature/publications/205"],["dc.relation","EXC 2067: Multiscale Bioimaging"],["dc.relation.workinggroup","RG Li"],["dc.relation.workinggroup","RG Munk"],["dc.title","Frame-constrained Total Variation Regularization for White Noise Regression"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","262"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Nature computational science"],["dc.bibliographiccitation.lastpage","271"],["dc.bibliographiccitation.volume","1"],["dc.contributor.author","Pelizzola, Marta"],["dc.contributor.author","Behr, Merle"],["dc.contributor.author","Li, Housen"],["dc.contributor.author","Munk, Axel"],["dc.contributor.author","Futschik, Andreas"],["dc.date.accessioned","2021-11-17T14:30:18Z"],["dc.date.available","2021-11-17T14:30:18Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1038/s43588-021-00056-5"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/93112"],["dc.identifier.url","https://mbexc.uni-goettingen.de/literature/publications/281"],["dc.relation","EXC 2067: Multiscale Bioimaging"],["dc.relation.issn","2662-8457"],["dc.relation.workinggroup","RG Li"],["dc.relation.workinggroup","RG Munk"],["dc.title","Multiple haplotype reconstruction from allele frequency data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1058"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Annales de l´Institut Henri Poincaré. B, Probability and Statistics"],["dc.bibliographiccitation.lastpage","1097"],["dc.bibliographiccitation.volume","54"],["dc.contributor.author","Grasmair, Markus"],["dc.contributor.author","Li, Housen"],["dc.contributor.author","Munk, Axel"],["dc.date.accessioned","2020-12-10T18:41:47Z"],["dc.date.available","2020-12-10T18:41:47Z"],["dc.date.issued","2018"],["dc.description.abstract","For the problem of nonparametric regression of smooth functions, we reconsider and analyze a constrained variational approach, which we call the MultIscale Nemirovski-Dantzig (MIND) estimator. This can be viewed as a multiscale extension of the Dantzig selector (\\emph{Ann. Statist.}, 35(6): 2313--51, 2009) based on early ideas of Nemirovski (\\emph{J. Comput. System Sci.}, 23:1--11, 1986). MIND minimizes a homogeneous Sobolev norm under the constraint that the multiresolution norm of the residual is bounded by a universal threshold. The main contribution of this paper is the derivation of convergence rates of MIND with respect to Lq-loss, 1≤q≤∞, both almost surely and in expectation. To this end, we introduce the method of approximate source conditions. For a one-dimensional signal, these can be translated into approximation properties of B-splines. A remarkable consequence is that MIND attains almost minimax optimal rates simultaneously for a large range of Sobolev and Besov classes, which provides certain adaptation. Complimentary to the asymptotic analysis, we examine the finite sample performance of MIND by numerical simulations."],["dc.identifier.arxiv","1512.01068"],["dc.identifier.doi","10.1214/17-AIHP832"],["dc.identifier.gro","3145904"],["dc.identifier.issn","0246-0203"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77677"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","DOI-Import GROB-394"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation","RTG 2088: Research Training Group 2088 Discovering structure in complex data: Statistics meets Optimization and Inverse Problems"],["dc.subject","Nonparametric regression adaptation convergence rates minimax optimality multiresolution norm approximate source conditions"],["dc.title","Variational multiscale nonparametric regression: Smooth functions"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]