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5Estimation of the spectral measure from convex combinations of regularly varying random vectors
M. Oesting, and O. Wintenberger. The annals of statistics, 52 (6): 2529-2556 (2024)
12 days ago by @unibiblio
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1Which Spaces can be Embedded in Reproducing Kernel Hilbert Spaces?
M. Schölpple, and I. Steinwart. Constructive approximation, (2025)
2 months ago by @unibiblio
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1Periportale Steatose bei Mäusen beeinflusst verschiedene Parameter des perizentralen Arzneimittelstoffwechsels
M. Albadry, S. Höpfl, N. Ehteshamzad, M. König, M. Böttcher, J. Neumann, A. Lupp, O. Dirsch, N. Radde, B. Christ and 5 other author(s). 66. Jahrestagung der Fachgruppe Pathologie der Deutschen Veterinärmedizinischen Gesellschaft, 51, 03, page 209. Thieme, (2023)
3 months ago by @unibiblio
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1Adaptive meshfree approximation for linear elliptic partial differential equations with PDE-greedy kernel methods
T. Wenzel, D. Winkle, G. Santin, and B. Haasdonk. BIT, 65 (1): 11 (2025)
3 months ago by @unibiblio
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6Extending a synthetic Notch morphogen circuit model to construct 2D cell structures
A. Beyer, V. Wagner, V. Klingel, M. Heymann, and N. Radde. 58, 23, page 31-36. Elsevier, (2024)
5 months ago by @unibiblio
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1Distribution-free tests for lossless feature selection in classification and regression
L. Györfi, T. Linder, and H. Walk. Test, 34 (1): 262-287 (2024)
6 months ago by @unibiblio
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1A sphingolipid rheostat controls apoptosis versus apical cell extrusion as alternative tumour-suppressive mechanisms
J. Armistead, S. Höpfl, P. Goldhausen, A. Müller-Hartmann, E. Fahle, J. Hatzold, R. Franzen, S. Brodesser, N. Radde, and M. Hammerschmidt. Cell death & disease, 15 (10): 746 (2024)
7 months ago by @unibiblio
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9Marginal Percentile Intervals in Bayesian Inference are Overconfident
S. Höpfl, H. Tautenhahn, V. Wagner, and N. Radde. 10th IFAC Conference on Foundations of Systems Biology in Engineering FOSBE 2024, 58, 23, page 19-24. Elsevier, (2024)
7 months ago by @unibiblio
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1DLC1 knockdown in EMT in MCF10A cells
S. Höpfl, and N. Radde. Software, (2025)
8 months ago by @unibiblio
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1The impact of incomplete revascularization on survival in minimal invasive off-pump coronary artery surgery: a propensity score analysis of 1,149 cases
M. Rufa, A. Ursulescu, J. Dippon, D. Aktuerk, S. Ahad, R. Nagib, M. Albert, and U. Franke. Journal of thoracic disease, 16 (7): 4504-4514 (2024)
9 months ago by @unibiblio
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1Martin boundary theory on inhomogeneous fractals
U. Freiberg, and S. Kohl. The Asian journal of mathematics, 27 (5): 639-674 (2023)
9 months ago by @unibiblio
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1Is minimally invasive multi-vessel off-pump coronary surgery as safe and effective as MIDCAB?
M. Rufa, A. Ursulescu, J. Dippon, D. Aktuerk, R. Nagib, M. Albert, and U. Franke. Frontiers in Cardiovascular Medicine, (2024)
9 months ago by @unibiblio
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5Extremes in High Dimensions : Methods and Scalable Algorithms
J. Lederer, and M. Oesting. Preprint, (2023)
10 months ago by @unibiblio
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6Estimation of the Spectral Measure from Convex Combinations of Regularly Varying Random Vectors
M. Oesting, and O. Wintenberger. Preprint, (2023)
10 months ago by @unibiblio
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4Bayesian modelling of time series data (BayModTS)-a FAIR workflow to process sparse and highly variable data
S. Höpfl, M. Albadry, U. Dahmen, K. Herrmann, E. Kindler, M. König, J. Reichenbach, H. Tautenhahn, W. Wei, W. Zhao and 1 other author(s). Bioinformatics, 40 (5): btae312 (2024)
11 months ago by @unibiblio
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1Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data [NeurIPS, arXiv v2]
D. Holzmüller, L. Grinsztajn, and I. Steinwart. Software, (2024)Related to: David Holzmüller, Léo Grinsztajn, and Ingo Steinwart. Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data, Neural Information Processing Systems, 2024. arXiv: 2407.04491.
a year ago by @unibiblio
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3Convergence rates for shallow neural networks learned by gradient descent
A. Braun, M. Kohler, S. Langer, and H. Walk. Bernoulli, 30 (1): 475-502 (2024)
a year ago by @unibiblio
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10Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
V. Zaverkin, D. Holzmüller, H. Christiansen, F. Errica, F. Alesiani, M. Takamoto, M. Niepert, and J. Kästner. npj computational materials, 10 (1): 83 (2024)
a year ago by @unibiblio
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1Reproducing kernel Hilbert spaces cannot contain all continuous functions on a compact metric space
I. Steinwart. Archiv der Mathematik, 122 (5): 553-557 (2024)
a year ago by @unibiblio
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1Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data
D. Holzmüller, L. Grinsztajn, and I. Steinwart. Software, (2024)Related to: David Holzmüller, Léo Grinsztajn, and Ingo Steinwart. Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data, 2024. arXiv: 2407.04491.
a year ago by @unibiblio
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publications (hide)143
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5Estimation of the spectral measure from convex combinations of regularly varying random vectors

1Which Spaces can be Embedded in Reproducing Kernel Hilbert Spaces?

1Periportale Steatose bei Mäusen beeinflusst verschiedene Parameter des perizentralen Arzneimittelstoffwechsels

1Adaptive meshfree approximation for linear elliptic partial differential equations with PDE-greedy kernel methods

6Extending a synthetic Notch morphogen circuit model to construct 2D cell structures

1Distribution-free tests for lossless feature selection in classification and regression

1A sphingolipid rheostat controls apoptosis versus apical cell extrusion as alternative tumour-suppressive mechanisms

9Marginal Percentile Intervals in Bayesian Inference are Overconfident

1DLC1 knockdown in EMT in MCF10A cells

1The impact of incomplete revascularization on survival in minimal invasive off-pump coronary artery surgery: a propensity score analysis of 1,149 cases

1Martin boundary theory on inhomogeneous fractals

1Is minimally invasive multi-vessel off-pump coronary surgery as safe and effective as MIDCAB?

5Extremes in High Dimensions : Methods and Scalable Algorithms

6Estimation of the Spectral Measure from Convex Combinations of Regularly Varying Random Vectors

4Bayesian modelling of time series data (BayModTS)-a FAIR workflow to process sparse and highly variable data

1Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data [NeurIPS, arXiv v2]

3Convergence rates for shallow neural networks learned by gradient descent

10Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials

1Reproducing kernel Hilbert spaces cannot contain all continuous functions on a compact metric space

1Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data

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