Search Results - "MATHÉ, PETER"

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  1. 1

    Inverse learning in Hilbert scales by Rastogi, Abhishake, Mathé, Peter

    Published in Machine learning (01-07-2023)
    “…We study linear ill-posed inverse problems with noisy data in the framework of statistical learning. The corresponding linear operator equation is assumed to…”
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  2. 2

    Tractability of linear ill-posed problems in Hilbert space by Mathé, Peter, Hofmann, Bernd

    Published in Journal of Complexity (01-10-2024)
    “…We introduce a notion of tractability for ill-posed operator equations in Hilbert space. For such operator equations the asymptotics of the best possible rate…”
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  3. 3
  4. 4

    Two-layer networks with the ReLUk activation function: Barron spaces and derivative approximation by Li, Yuanyuan, Lu, Shuai, Mathé, Peter, Pereverzev, Sergei V.

    Published in Numerische Mathematik (2024)
    “…We investigate the use of two-layer networks with the rectified power unit, which is called the ReLU k activation function, for function and derivative…”
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  5. 5

    Optimal rates for Lavrentiev regularization with adjoint source conditions by Plato, Robert, Mathé, Peter, Hofmann, Bernd

    Published in Mathematics of computation (01-03-2018)
    “…There are various ways to regularize ill-posed operator equations in Hilbert space. If the underlying operator is accretive, then Lavrentiev regularization…”
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  6. 6

    Complexity of linear ill-posed problems in Hilbert space by Mathé, Peter, Pereverzev, Sergei V.

    Published in Journal of Complexity (01-02-2017)
    “…Information complexity of ill-posed problems may be seen as controversial. On the one hand side there were pessimistic results stating that the complexity is…”
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  7. 7

    Convergence analysis of Tikhonov regularization for non-linear statistical inverse problems by Rastogi, Abhishake, Blanchard, Gilles, Mathé, Peter

    Published in Electronic journal of statistics (01-01-2020)
    “…We study a non-linear statistical inverse problem, where we observe the noisy image of a quantity through a non-linear operator at some random design points…”
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  8. 8

    Discrepancy based model selection in statistical inverse problems by Lu, Shuai, Mathé, Peter

    Published in Journal of Complexity (01-06-2014)
    “…The reconstruction of solutions in statistical inverse problems in Hilbert spaces requires regularization, which is often based on a parametrized family of…”
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  9. 9

    Adaptive discretization for signal detection in statistical inverse problems by Mathe, Peter

    Published in Applicable analysis (04-03-2015)
    “…We discuss statistical tests in inverse problems when the original equation is replaced by a discretized one, i.e. a linear system of equations. Previous…”
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  10. 10

    Analysis of Profile Functions for General Linear Regularization Methods by Hofmann, Bernd, Mathé, Peter

    Published in SIAM journal on numerical analysis (01-01-2007)
    “…The stable approximate solution of ill-posed linear operator equations in Hilbert spaces requires regularization. Tight bounds for the noise-free part of the…”
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  11. 11

    Regularization properties of the sequential discrepancy principle for Tikhonov regularization in Banach spaces by Anzengruber, Stephan W., Hofmann, Bernd, Mathé, Peter

    Published in Applicable analysis (03-07-2014)
    “…The stable solution of ill-posed non-linear operator equations in Banach space requires regularization. One important approach is based on Tikhonov…”
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  12. 12

    Heuristic parameter selection based on functional minimization: Optimality and model function approach by LU, SHUAI, MATHÉ, PETER

    Published in Mathematics of computation (01-07-2013)
    “…We analyze some parameter choice strategies in regularization of inverse problems, in particular, the (modified) L-curve method and a variant of the Hanke-Raus…”
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  13. 13

    Regularization of some linear ill-posed problems with discretized random noisy data by Mathé, Peter, Pereverzev, Sergei V.

    Published in Mathematics of computation (01-10-2006)
    “…For linear statistical ill-posed problems in Hilbert spaces we introduce an adaptive procedure to recover the unknown solution from indirect discrete and noisy…”
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  14. 14

    Simple Monte Carlo and the Metropolis algorithm by Mathé, Peter, Novak, Erich

    Published in Journal of Complexity (01-08-2007)
    “…We study the integration of functions with respect to an unknown density. Information is available as oracle calls to the integrand and to the non-normalized…”
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  15. 15

    Discretized Lavrent'ev regularization for the autoconvolution equation by Bürger, Steven, Mathé, Peter

    Published in Applicable analysis (27-07-2017)
    “…Lavrent'ev regularization for the autoconvolution equation was considered by Janno J. in Lavrent'ev regularization of ill-posed problems containing nonlinear…”
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  16. 16

    Influence of the carrier reservoir dimensionality on electron-electron scattering in quantum dot materials by Wilms, Alexander, Mathé, Peter, Schulze, Franz, Koprucki, Thomas, Knorr, Andreas, Bandelow, Uwe

    “…We calculated Coulomb scattering rates from quantum dots (QDs) coupled to a two-dimensional (2D) carrier reservoir and QDs coupled to a three-dimensional (3D)…”
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  17. 17

    Direct and inverse results in variable Hilbert scales by Mathé, Peter, Hofmann, Bernd

    Published in Journal of approximation theory (01-10-2008)
    “…Variable Hilbert scales are an important tool for the recent analysis of inverse problems in Hilbert spaces, as these constitute a way to describe smoothness…”
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  18. 18

    The use of higher order finite difference schemes is not dangerous by Mathé, Peter, Pereverzev, Sergei V.

    Published in Journal of Complexity (01-02-2009)
    “…We discuss the issue of choosing a finite difference scheme for numerical differentiation in case the smoothness of the underlying function is unknown. If low…”
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  19. 19

    Saturation of Regularization Methods for Linear Ill-Posed Problems in Hilbert Spaces by Mathe, Peter

    Published in SIAM journal on numerical analysis (01-01-2004)
    “…We prove the saturation of methods for solving linear ill-posed problems in Hilbert spaces for a wide class of regularization methods. It turns out that, under…”
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