Peter Mathé - researchr alias

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Viewing Publication 1 - 31 from 31

2024

k activation function: Barron spaces and derivative approximationYuanyuan Li, Shuai Lu, Peter Mathé, Sergei V. Pereverzev. nm, 156(1):319-344, February 2024. [doi]

2023

Inverse learning in Hilbert scalesAbhishake Rastogi, Peter Mathé. ml, 112(7):2469-2499, July 2023. [doi]

2022

Stochastic gradient descent for linear inverse problems in Hilbert spacesShuai Lu, Peter Mathé. moc, 91(336):1763-1788, 2022. [doi]

2019

Bayesian inverse problems with non-commuting operatorsPeter Mathé. moc, 88(320):2897-2912, 2019. [doi]

2018

Minimax Rates for Statistical Inverse Problems Under General Source ConditionsLitao Ding, Peter Mathé. cmam, 18(4):603-608, 2018. [doi]

Optimal rates for Lavrentiev regularization with adjoint source conditionsRobert Plato, Peter Mathé, Bernd Hofmann. moc, 87(310):785-801, 2018. [doi]

2017

Complexity of linear ill-posed problems in Hilbert spacePeter Mathé, Sergei V. Pereverzev. jc, 38:50-67, 2017. [doi]

2014

Discrepancy based model selection in statistical inverse problemsShuai Lu, Peter Mathé. jc, 30(3):290-308, 2014. [doi]

2013

Oracle Inequality for a Statistical Raus-Gfrerer-Type RuleQinian Jin, Peter Mathé. juq, 1(1):386-407, 2013. [doi]

Heuristic parameter selection based on functional minimization: Optimality and model function approachShuai Lu, Peter Mathé. moc, 82(283):1609-1630, 2013. [doi]

2011

Parameter choice methods using minimization schemesF. Bauer, Peter Mathé. jc, 27(1):68-85, 2011. [doi]

2009

The use of higher order finite difference schemes is not dangerousPeter Mathé, Sergei V. Pereverzev. jc, 25(1):3-10, 2009. [doi]

2008

Direct and inverse results in variable Hilbert scalesPeter Mathé, Bernd Hofmann. jat, 154(2):77-89, 2008. [doi]

2007

Analysis of Profile Functions for General Linear Regularization MethodsBernd Hofmann, Peter Mathé. siamnum, 45(3):1122-1141, 2007. [doi]

Simple Monte Carlo and the Metropolis algorithmPeter Mathé, Erich Novak. jc, 23(4-6):673-696, 2007. [doi]

2006

The discretized discrepancy principle under general source conditionsPeter Mathé, Sergei V. Pereverzev. jc, 22(3):371-381, 2006. [doi]

Regularization of some linear ill-posed problems with discretized random noisy dataPeter Mathé, Sergei V. Pereverzev. moc, 75(256):1913-1929, 2006. [doi]

2004

Quasi-Monte Carlo integration over Real:::d:::Peter Mathé, Gang Wei. moc, 73(246):827-841, 2004.

Saturation of Regularization Methods for Linear Ill-Posed Problems in Hilbert SpacesPeter Mathé. siamnum, 42(3):968-973, 2004. [doi]

2002

2002 Best Paper Award CommitteeFred J. Hickernell, Peter Mathé. jc, 18(3):681, 2002. [doi]

Stable Summation of Orthogonal Series with Noisy CoefficientsPeter Mathé, Sergei V. Pereverzev. jat, 118(1):66-80, 2002. [doi]

Direct Estimation of Linear Functionals from Indirect Noisy ObservationsPeter Mathé, Sergei V. Pereverzev. jc, 18(2):500-516, 2002. [doi]

2001

Optimal Discretization of Inverse Problems in Hilbert Scales. Regularization and Self-Regularization of Projection MethodsPeter Mathé, Sergei V. Pereverzev. siamnum, 38(6):1999-2021, 2001. [doi]

1999

Numerical Integration using Markov ChainsPeter Mathé. mcma, 5(4):325-344, 1999. [doi]

1998

Relaxation of Product Markov Chains on Product SpacesPeter Mathé. jc, 14(3):319-332, 1998. [doi]

Asymptotically Optimal Weighted Numerical IntegrationPeter Mathé. jc, 14(1):34-48, 1998. [doi]

1997

On quasi-Monte Carlo simulation of stochastic differential equationsNorbert Hofmann, Peter Mathé. moc, 66(218):573-589, 1997.

1995

The Optimal Error of Monte Carlo IntegrationPeter Mathé. jc, 11(4):394-415, 1995. [doi]

1993

On Optimal Random NetsPeter Mathé. jc, 9(1):171-180, 1993. [doi]

1991

Random approximation of Sobolev embeddingsPeter Mathé. jc, 7(3):261-281, 1991. [doi]

1990

::::s::::-Numbers in information-based complexityPeter Mathé. jc, 6(1):41-66, 1990. [doi]

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