Dr. Housen Li

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  • 2024 | Preprint
    ​ ​Robust inference of cooperative behaviour of multiple ion channels in voltage-clamp recordings​
    Requadt, R.; Fink, M.; Kubica, P.; Steinem, C. ; Munk, A.  & Li, H. ​ (2024)
    ​Details 
  • 2024 | Preprint
    ​ ​Multiscale Quantile Regression with Local Error Control​
    Liu, Z.& Li, H. ​ (2024). DOI: https://doi.org/10.48550/ARXIV.2403.11356 
    ​Details  DOI  arXiv 
  • 2024 | Preprint
    ​ ​MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy​
    Naas, J.; Nies, G.; Li, H. ; Stoldt, S. ; Schmitzer, B. ; Jakobs, S.  & Munk, A. ​ (2024). DOI: https://doi.org/10.1101/2024.02.28.581557 
    ​Details  DOI 
  • 2023 | Preprint
    ​ ​Quick Adaptive Ternary Segmentation: An Efficient Decoding Procedure For Hidden Markov Models​
    Mösching, A.; Li, H.  & Munk, A. ​ (2023). DOI: https://doi.org/10.48550/arxiv.2305.18578 
    ​Details  DOI  arXiv 
  • 2023 | Preprint
    ​ ​Adaptive minimax optimality in statistical inverse problems via SOLIT - Sharp Optimal Lepskii-Inspired Tuning​
    Li, H.  & Werner, F. ​ (2023). DOI: https://doi.org/10.48550/arxiv.2304.10356 
    ​Details  DOI  arXiv 
  • 2023 | Preprint
    ​ ​A scalable clustering algorithm to approximate graph cuts​
    Suchan, L.; Li, H.  & Munk, A. ​ (2023). DOI: https://doi.org/10.48550/ARXIV.2308.09613 
    ​Details  DOI 
  • 2023 | Journal Article
    ​ ​Adaptive minimax optimality in statistical inverse problems via SOLIT—Sharp Optimal Lepskiĭ-Inspired Tuning​
    Li, H.   & Werner, F. ​ (2023) 
    Inverse Problems, 40(2) art. 025005​.​ DOI: https://doi.org/10.1088/1361-6420/ad12e0 
    ​Details  DOI 
  • 2022 | Journal Article
    ​ ​A Variational View on Statistical Multiscale Estimation​
    Haltmeier, M.; Li, H.   & Munk, A. ​ (2022) 
    Annual Review of Statistics and Its Application, 9(1) pp. 343​-372​.​ DOI: https://doi.org/10.1146/annurev-statistics-040120-030531 
    ​Details  DOI 
  • 2021 | Journal Article
    ​ ​Frame-constrained total variation regularization for white noise regression​
    del Álamo, M.; Li, H.   & Munk, A. ​ (2021) 
    The Annals of Statistics, 49(3).​ DOI: https://doi.org/10.1214/20-AOS2001 
    ​Details  DOI  arXiv 
  • 2021 | Journal Article | Research Paper
    ​ ​Multiple haplotype reconstruction from allele frequency data​
    Pelizzola, M.; Behr, M.; Li, H. ; Munk, A.   & Futschik, A.​ (2021) 
    Nature computational science, 1(4) pp. 262​-271​.​ DOI: https://doi.org/10.1038/s43588-021-00056-5 
    ​Details  DOI 
  • 2020 | Preprint
    ​ ​Variational Multiscale Nonparametric Regression: Algorithms and Implementation​
    del Alamo, M.; Li, H. ; Munk, A.  & Werner, F.​ (2020)
    ​Details  arXiv 
  • 2020 | Preprint
    ​ ​Optimistic search strategy: Change point detection for large-scale data via adaptive logarithmic queries​
    Kovács, S.; Li, H. ; Haubner, L.; Munk, A.  & Bühlmann, P.​ (2020)
    ​Details  arXiv 
  • 2020 | Preprint
    ​ ​Seeded Binary Segmentation: A general methodology for fast and optimal change point detection​
    Kovács, S.; Li, H. ; Bühlmann, P.& Munk, A. ​ (2020)
    ​Details  arXiv 
  • 2020 | Preprint
    ​ ​Multiple Haplotype Reconstruction from Allele Frequency Data​
    Pelizzola, M.; Behr, M.; Li, H. ; Munk, A.  & Futschik, A.​ (2020). DOI: https://doi.org/10.1101/2020.07.09.191924 
    ​Details  DOI 
  • 2020 | Book Chapter
    ​ ​Photonic Imaging with Statistical Guarantees: From Multiscale Testing to Multiscale Estimation​
    Munk, A. ; Proksch, K. ; Li, H.  & Werner, F. ​ (2020)
    In:​Salditt, Tim; Egner, Alexander; Luke, D. Russell​ (Eds.), Nanoscale Photonic Imaging pp. 283​-312. (Vol. 134). ​Cham: ​Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-34413-9_11 
    ​Details  DOI 
  • 2020 | Journal Article | Research Paper
    ​ ​Seeded intervals and noise level estimation in change point detection: a discussion of Fryzlewicz (2020)​
    Kovács, S.; Li, H.   & Bühlmann, P.​ (2020) 
    Journal of the Korean Statistical Society, 49(4) pp. 1081​-1089​.​ DOI: https://doi.org/10.1007/s42952-020-00077-2 
    ​Details  DOI 
  • 2020 | Journal Article | Research Paper
    ​ ​The essential histogram​
    Li, H. ; Munk, A. ; Sieling, H.   & Walther, G.​ (2020) 
    Biometrika, 107(2) pp. 347​-364​.​ DOI: https://doi.org/10.1093/biomet/asz081 
    ​Details  DOI 
  • 2020 | Journal Article | 
    ​ ​Empirical risk minimization as parameter choice rule for general linear regularization methods​
    Li, H.   & Werner, F. ​ (2020) 
    Annales de l´Institut Henri Poincaré. B, Probability and Statistics, 56(1) pp. 405​-427​.​ DOI: https://doi.org/10.1214/19-AIHP966 
    ​Details  DOI 
  • 2020 | Journal Article | Research Paper | 
    ​ ​NETT: solving inverse problems with deep neural networks​
    Li, H. ; Schwab, J.; Antholzer, S. & Haltmeier, M.​ (2020) 
    Inverse Problems, 36(6) pp. 065005​.​ DOI: https://doi.org/10.1088/1361-6420/ab6d57 
    ​Details  DOI 
  • 2020 | Journal Article | Research Paper | 
    ​ ​Variational Multiscale Nonparametric Regression: Algorithms and Implementation​
    del Alamo, M.; Li, H. ; Munk, A.   & Werner, F. ​ (2020) 
    Algorithms, 13(11) pp. 296​.​ DOI: https://doi.org/10.3390/a13110296 
    ​Details  DOI 
  • 2019 | Journal Article | Research Paper | 
    ​ ​Multiscale Change-point Segmentation: Beyond Step Functions​
    Li, H. ; Guo, Q. & Munk, A. ​ (2019) 
    Electronic Journal of Statistics, 13(2) pp. 3254​-3296​.​ DOI: https://doi.org/10.1214/19-EJS1608 
    ​Details  DOI  arXiv 
  • 2018 | Preprint
    ​ ​Frame-constrained Total Variation Regularization for White Noise Regression​
    del Álamo, M.; Li, H.  & Munk, A. ​ (2018)
    ​Details  arXiv 
  • 2018 | Journal Article
    ​ ​The Averaged Kaczmarz Iteration for Solving Inverse Problems​
    Li, H.   & Haltmeier, M.​ (2018) 
    SIAM Journal on Imaging Sciences, 11(1) pp. 618​-642​.​ DOI: https://doi.org/10.1137/17M1146178 
    ​Details  DOI 
  • 2018 | Journal Article | 
    ​ ​Variational multiscale nonparametric regression: Smooth functions​
    Grasmair, M.; Li, H.   & Munk, A. ​ (2018) 
    Annales de l´Institut Henri Poincaré. B, Probability and Statistics, 54(2) pp. 1058​-1097​.​ DOI: https://doi.org/10.1214/17-AIHP832 
    ​Details  DOI  arXiv 
  • 2016 | Preprint
    ​ ​The Essential Histogram​
    Li, H. ; Munk, A. ; Sieling, H.  & Walther, G.​ (2016)
    ​Details  arXiv 
  • 2016 | Journal Article | 
    ​ ​FDR-control in multiscale change-point segmentation​
    Li, H. ; Munk, A.   & Sieling, H. ​ (2016) 
    Electronic Journal of Statistics, 10(1) pp. 918​-959​.​ DOI: https://doi.org/10.1214/16-EJS1131 
    ​Details  DOI  WoS 
  • 2015 | Conference Paper
    ​ ​Justifying Tensor-Driven Diffusion from Structure-Adaptive Statistics of Natural Images​
    Peter, P.; Weickert, J.; Munk, A. ; Krivobokova, T.   & Li, H. ​ (2015)
    In:Tai, Xue-Cheng​ (Ed.), ​Energy minimization methods in computer vision and pattern recognition pp. 263​-277. , Hong Kong, China.
    Cham​: Springer. DOI: https://doi.org/10.1007/978-3-319-14612-6_20 
    ​Details  DOI 
  • 2014 | Journal Article
    ​ ​Aggregated Motion Estimation for Real-Time MRI Reconstruction​
    Li, H. ; Haltmeier, M.; Zhang, S.; Frahm, J.   & Munk, A. ​ (2014) 
    Magnetic Resonance in Medicine, 72(4) pp. 1039​-1048​.​ DOI: https://doi.org/10.1002/mrm.25020 
    ​Details  DOI  PMID  PMC  WoS 
  • 2011 | Journal Article
    ​ ​Incremental manifold learning by spectral embedding methods​
    Li, H. ; Jiang, H.; Barrio, R.; Liao, X.; Cheng, L. & Su, F.​ (2011) 
    Pattern Recognition Letters, 32(10) pp. 1447​-1455​.​ DOI: https://doi.org/10.1016/j.patrec.2011.04.004 
    ​Details  DOI 
  • 2011 | Journal Article
    ​ ​Accurate evaluation of a polynomial in Chebyshev form​
    Jiang, H.; Barrio, R.; Li, H. ; Liao, X.; Cheng, L. & Su, F.​ (2011) 
    Applied Mathematics and Computation, 217(23) pp. 9702​-9716​.​ DOI: https://doi.org/10.1016/j.amc.2011.04.054 
    ​Details  DOI 
  • 2009 | Journal Article
    ​ ​Some second-derivative-free variants of Halley’s method for multiple roots​
    Li, S.; Li, H.   & Cheng, L.​ (2009) 
    Journal of Applied Mathematics and Computing, 215(6) pp. 2192​-2198​.​ DOI: https://doi.org/10.1016/j.amc.2009.08.010 
    ​Details  DOI 

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