M. Kohler

发表

Inequalities for uniform deviations of averages from expectations with applications to nonparametric regression

M. Kohler, 2000 .

A Distribution-Free Theory of Nonparametric Regression

Adam Krzyzak, László Györfi, Michael Kohler, 2002, Springer series in statistics.

Strong consistency of automatic kernel regression estimates

A. Krzyżak, M. Kohler, Harro Walk, 2003 .

Nonlinear orthogonal series estimates for random design regression

M. Kohler, 2003 .

Universally Consistent Regression Function Estimation Using Hierarchial B-Splines

M. Kohler, 1999 .

Application of structural risk minimization to multivariate smoothing spline regression estimates

A. Krzyżak, M. Kohler, D. Schäfer, 2002 .

Universal Consistency of Local Polynomial Kernel Regression Estimates

M. Kohler, 2002 .

Adaptive regression estimation with multilayer feedforward neural networks

A. Krzyżak, M. Kohler, 2005 .

Empirical Comparison of Nonparametric Regression Estimates on Real Data

Adam Krzyzak, Alexander Richter, Michael Kohler, 2016, Commun. Stat. Simul. Comput..

Density estimation using real and artificial data

Adam Krzyzak, Michael Kohler, Tina Felber, 2014, 2014 IEEE International Symposium on Information Theory.

Adaptive density estimation based on real and artificial data

A. Krzyżak, M. Kohler, T. Felber, 2015 .

Optimal global rates of convergence for interpolation problems with random design

A. Krzyżak, M. Kohler, 2013 .

Adaptive Density Estimation From Data With Small Measurement Errors

Adam Krzyzak, Michael Kohler, Tina Felber, 2015, IEEE Transactions on Information Theory.

Estimation of a distribution from data with small measurement errors

L. Devroye, M. Kohler, Ann-Kathrin Bott, 2013 .

A Distribution-Free Theory of Nonparametric Regression (Springer Series in Statistics)

L. Györfi, M. Kohler, Harro Walk, 2002 .

Wallace CS: Statistical and inductive inference by minimum message length

M. Kohler, 2006 .

Lower bounds on the rate of convergence of nonparametric regression estimates

A. Antos, L. Györfi, M. Kohler, 2000 .

WEAK AND STRONG UNIVERSAL CONSISTENCY OF SEMI-RECURSIVE KERNEL AND PARTITIONING REGRESSION ESTIMATES

L. Györfi, M. Kohler, Harro Walk, 1998 .

On deep learning as a remedy for the curse of dimensionality in nonparametric regression

M. Kohler, Benedikt Bauer, B. Bauer, 2019, The Annals of Statistics.

Nonparametric regression estimation using penalized least squares

Adam Krzyzak, Michael Kohler, A. Krzyżak, 2001, IEEE Trans. Inf. Theory.

On Optimal Global Rates Of Convergence For Nonparametric Regression With Random Design

M. Kohler, 2000 .

Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory

M. Kohler, Yufeng Liu, J. Malley, 2014, Biometrical journal. Biometrische Zeitschrift.

Nonasymptotic Bounds on the L2 Error of Neural Network Regression Estimates

M. Kohler, M. Hamers, 2006 .

Nonparametric regression function estimation using interaction least squares splines and comlexity regularization

Michael Kohler, M. Kohler, 1998 .

A dynamic look-ahead Monte Carlo algorithm for pricing Bermudan options

M. Kohler, Daniel Egloff, N. Todorović, 2007, 0710.3640.

A regression-based smoothing spline Monte Carlo algorithm for pricing American options in discrete time

M. Kohler, 2008 .

PRICING OF HIGH‐DIMENSIONAL AMERICAN OPTIONS BY NEURAL NETWORKS

A. Krzyżak, M. Kohler, N. Todorović, 2010 .

On estimation of surrogate models for multivariate computer experiments

A. Krzyżak, M. Kohler, Benedikt Bauer, 2019 .

On estimation of surrogate models for high-dimensional computer experiments ∗

A. Krzyżak, M. Kohler, Benedikt Bauer, 2016 .

ON THE CONSISTENCY OF REGRESSION‐BASED MONTE CARLO METHODS FOR PRICING BERMUDAN OPTIONS IN CASE OF ESTIMATED FINANCIAL MODELS

M. Kohler, Andreas Fromkorth, 2015 .

On data-based optimal stopping under stationarity and ergodicity

M. Kohler, Harro Walk, 2013, 1307.5976.

Analysis of least squares regression estimates in case of additional errors in the variables

M. Kohler, Andreas Fromkorth, 2011 .

A Review on Regression-based Monte Carlo Methods for Pricing American Options

M. Kohler, 2010 .

Universit ät Stuttgart Fachbereich Mathematik On optimal exercising of American options in discrete time for stationary and ergodic data

M. Kohler, Harro Walk, 2010 .

On the rate of convergence of a neural network regression estimate learned by gradient descent

M. Kohler, Harro Walk, Alina Braun, 2019, 1912.03921.

Nonparametric estimation of piecewise smooth regression functions

M. Kohler, 1999 .

Rates of convergence for partitioning and nearest neighbor regression estimates with unbounded data

Adam Krzyżak, Michael Kohler, Harro Walk, 2006 .

Estimation of a jump point in random design regression

Adam Krzyżak, Michael Kohler, A. Krzyżak, 2015 .

Estimation of the essential supremum of a regression function

Adam Krzyżak, Michael Kohler, Harro Walk, 2011 .

Inequalities for uniform deviations of averages fromexpectations with applications to nonparametric regressionMichael

M. Kohler, 2000 .

Adaptive Estimation of a Conditional Density

Michael Kohler, M. Kohler, Ann-Kathrin Bott, 2016 .

Data – dependent parameter choice for a conditional kernel density estimate

M. Kohler, Ann-Kathrin Bott, 2015 .

On the density estimation problem for uncertainty propagation with unknown input distributions

M. Kohler, Sebastian Kersting, 2020, 2012.10113.

Estimation of a density in a simulation model

M. Kohler, T. Felber, Ann-Kathrin Bott, 2015 .

Adaptive Density Estimation from Data Containing Bounded Measurement Errors

A. Krzyżak, M. Kohler, T. Felber, 2015 .

Asymptotic confidence intervals for Poisson regression

Adam Krzyżak, Michael Kohler, A. Krzyżak, 2007 .

Prediction from Randomly Right Censored Data

Michael Kohler, M. Kohler, Kinga Máthé, 2002 .

Nonparametric quantile estimation using surrogate models and importance sampling

M. Kohler, R. Tent, 2020, Metrika.

Estimation of an improved surrogate model in uncertainty quantification by neural networks

M. Kohler, Sebastian Kersting, Benedict Götz, 2020 .

Nonparametric estimation of time-dependent quantiles in a simulation model

M. Kohler, Felix Heimrich, Lisa Kristl, 2019, Statistica Sinica.

Estimation of extreme quantiles in a simulation model

A. Krzyżak, M. Kohler, 2019, Journal of Nonparametric Statistics.

Improving a surrogate model in uncertainty quanti cation by real data ∗

M. Kohler, 2017 .

Analysis of the rate of convergence of neural network regression estimates which are easy to implement.

A. Krzyżak, M. Kohler, Alina Braun, 2019, 1912.05436.

Multivariate orthogonal series estimates for random design regression

M. Kohler, 2008 .

Analysis of convolutional neural network image classifiers in a rotationally symmetric model

M. Kohler, B. Walter, 2022, 2205.05500.

Statistical theory for image classification using deep convolutional neural networks with cross-entropy loss

M. Kohler, Sophie Langer, S. Langer, 2020, 2011.13602.

Uncertainty Quantification in Case of Imperfect Models: A Non‐Bayesian Approach

A. Krzyżak, M. Kohler, R. Platz, 2018 .

Estimating quantiles in imperfect simulation models using conditional density estimation

A. Krzyżak, M. Kohler, 2018, Annals of the Institute of Statistical Mathematics.

Recent Developments in Applied Probability and Statistics

Luc Devroye, Bülent Karasözen, Ralf Korn, 2010 .

L1-consistent estimation of the density of residuals in random design regression models

Adam Krzyżak, Luc Devroye, Michael Kohler, 2012 .

On the universal consistency of an over-parametrized deep neural network estimate learned by gradient descent

M. Kohler, Selina Drews, 2022, 2208.14283.

Advanced Techniques from Empirical Process Theory

Adam Krzyżak, László Györfi, Michael Kohler, 2002 .

Dimension Reduction Techniques

Adam Krzyżak, László Györfi, Michael Kohler, 2002 .

Neural Networks Estimates

Adam Krzyżak, László Györfi, Michael Kohler, 2002 .

Analysis of the rate of convergence of an over-parametrized deep neural network estimate learned by gradient descent

A. Krzyżak, M. Kohler, 2022, 2210.01443.

Smoothing spline regression estimation based on real and artificial data

M. Kohler, Dmytro Furer, 2015, Metrika.

Nonparametric quantile estimation using importance sampling

A. Krzyżak, M. Kohler, Harro Walk, 2017, Annals of the Institute of Statistical Mathematics.

Estimation of an improved surrogate model in uncertainty quantification by neural networks

M. Kohler, Sebastian Kersting, Benedict Götz, 2020, Annals of the Institute of Statistical Mathematics.

Estimating quantiles in imperfect simulation models using conditional density estimation

A. Krzyżak, M. Kohler, 2018, Annals of the Institute of Statistical Mathematics.

On estimation of surrogate models for multivariate computer experiments

A. Krzyżak, M. Kohler, Benedikt Bauer, 2017, Annals of the Institute of Statistical Mathematics.

On the strong universal consistency of local averaging regression estimates

M. Kohler, Harro Walk, M. Hansmann, 2018, Annals of the Institute of Statistical Mathematics.

Treatment of gray platelet syndrome.

M. Kohler, 1988 .

WITHDRAWN: Approximation properties of fully connected deep neural networks

M. Kohler, S. Langer, 2019, Journal of Multivariate Analysis.

Optimal global rates of convergence for nonparametric regression with unbounded data

Adam Krzyżak, Michael Kohler, Harro Walk, 2009 .

Estimation of a density using an improved surrogate model

Adam Krzyżak, Michael Kohler, A. Krzyżak, 2021 .

Nonparametric estimation of a latent variable model

Adam Krzyzak, Michael Kohler, Augustin Kelava, 2017, J. Multivar. Anal..

Estimation of a regression function corresponding to latent variables

Michael Kohler, Harro Walk, M. Kohler, 2015 .

Estimation of a regression function on a manifold by fully connected deep neural networks

Ulrich Reif, Michael Kohler, Sophie Langer, 2021, Journal of Statistical Planning and Inference.

On the strong universal consistency of local averaging regression estimates

Michael Kohler, Harro Walk, Matthias Hansmann, 2018, Annals of the Institute of Statistical Mathematics.

Estimation of a Density Using Real and Artificial Data

Luc Devroye, Michael Kohler, Tina Felber, 2013, IEEE Transactions on Information Theory.

A statistical approach to case based reasoning, with application to breast cancer data

Jürgen Dippon, M. Kohler, J. Dippon, 2002 .

Estimation of conditional distribution functions from data with additional errors applied to shape optimization

Stefan Ulbrich, Michael Kohler, Matthias Hansmann, 2021, Metrika.

Estimation of conditional quantiles from data with additional measurement errors

Michael Kohler, Matthias Hansmann, M. Kohler, 2019, Journal of Statistical Planning and Inference.

Nonparametric quantile estimation using importance sampling

Adam Krzyżak, Michael Kohler, Harro Walk, 2018 .

Nonparametric estimation of a function from noiseless observations at random points

Adam Krzyzak, Luc Devroye, Michael Kohler, 2017, J. Multivar. Anal..

Nonparametric Quantile Estimation Based on Surrogate Models

Adam Krzyzak, Roland Platz, Michael Kohler, 2016, IEEE Transactions on Information Theory.

Smoothing spline regression estimation based on real and artificial data

Michael Kohler, M. Kohler, Dmytro Furer, 2015 .

Nonparametric estimation of a maximum of quantiles

Adam Krzyżak, Roland Platz, Georg Christoph Enss, 2014 .

Optimal global rates of convergence for noiseless regression estimation problems with adaptively chosen design

Michael Kohler, M. Kohler, 2014, J. Multivar. Anal..

Fixed-design regression estimation based on real and artificial data

Adam Krzyżak, Michael Kohler, A. Krzyżak, 2013 .

Estimation of the optimal design of a nonlinear parametric regression problem via Monte Carlo experiments

Michael Kohler, Ida Hertel, M. Kohler, 2013, Comput. Stat. Data Anal..

Nonparametric estimation of non-stationary velocity fields from 3D particle tracking velocimetry data

Adam Krzyzak, Michael Kohler, A. Krzyżak, 2012, Comput. Stat. Data Anal..

On the Rate of Convergence of Local Averaging Plug-In Classification Rules Under a Margin Condition

Adam Krzyzak, Michael Kohler, A. Krzyżak, 2007, IEEE Transactions on Information Theory.

Rate of convergence of local averaging plug-in classification rules under margin condition

Adam Krzyzak, Michael Kohler, A. Krzyżak, 2006, 2006 IEEE International Symposium on Information Theory.

Nonparametric Regression Estimation

László Györfi, L. Györfi, M. Kohler, 2002 .

Over-parametrized deep neural networks minimizing the empirical risk do not generalize well

Adam Krzyżak, Michael Kohler, A. Krzyżak, 2021 .

On the rate of convergence of fully connected very deep neural network regression estimates pdf

Michael Kohler, Sophie Langer, M. Kohler, 2019, The Annals of Statistics.

Deep Learning and MARS: A Connection pdf

Adam Krzyzak, Michael Kohler, Sophie Langer, 2019, ArXiv.

Nonparametric Regression Based on Hierarchical Interaction Models

Adam Krzyżak, Michael Kohler, A. Krzyżak, 2017, IEEE Transactions on Information Theory.

The rates of convergence of neural network estimates of hierarchical interaction regression models

Adam Krzyzak, Michael Kohler, A. Krzyżak, 2016, 2016 IEEE International Symposium on Information Theory (ISIT).

Estimation of a Regression Function by Maxima of Minima of Linear Functions

Adil M. Bagirov, Michael Kohler, Conny Clausen, 2009, IEEE Transactions on Information Theory.

Rates of convergence for adaptive regression estimates with multiple hidden layer feedforward neural networks

Adam Krzyzak, Michael Kohler, A. Krzyżak, 2005, Proceedings. International Symposium on Information Theory, 2005. ISIT 2005..

Adaptive regression estimation with multilayer feedforward neural networks

Adam Krzyzak, Michael Kohler, A. Krzyżak, 2004, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..

How well can a regression function be estimated if the distribution of the (random) design is con

M. Kohler, M. Hamers, 2004 .

Nonparametric regression with additional measurement errors in the dependent variable

Michael Kohler, M. Kohler, 2006 .

Analysis of the rate of convergence of least squares neural network regression estimates in case of measurement errors

Michael Kohler, Jens Mehnert, M. Kohler, 2011, Neural Networks.

Prediction from Randomly Right Censored Data 1

M. Kohler, 1999 .

Estimation of a Function of Low Local Dimensionality by Deep Neural Networks

Adam Krzyzak, Michael Kohler, Sophie Langer, 2019, IEEE Transactions on Information Theory.

Estimation of quantiles from data with additional measurement errors

Michael Kohler, Matthias Hansmann, M. Kohler, 2017 .

Types of Uncertainty

M. Kohler, S. Ulbrich, M. Pfetsch, 2021, Springer Tracts in Mechanical Engineering.

Over-parametrized deep neural networks do not generalize well

A. Krzyżak, M. Kohler, 2019, 1912.03925.

On the rate of convergence of image classifiers based on convolutional neural networks

A. Krzyżak, M. Kohler, Benjamin Walter, 2020, Annals of the Institute of Statistical Mathematics.

Nonparametric estimation of a conditional density

Michael Kohler, M. Kohler, Ann-Kathrin Bott, 2017 .

Estimation of a time-dependent density

M. Kohler, T. Felber, Ann-Kathrin Bott, 2017 .

Nonparametric Estimation of Conditional Distributions

László Györfi, Michael Kohler, L. Györfi, 2007, IEEE Transactions on Information Theory.

A DYNAMIC LOOK-AHEAD MONTE CARLO ALGORITHM FOR PRICING AMERICAN OPTIONS

M. Kohler, Daniel Egloff, N. Todorović, 2006 .

Mental health professionals’ attitudes toward patients with PTSD and depression

M. Kohler, U. Schnyder, H. Moergeli, 2015, European journal of psychotraumatology.

Pricing of American options in discrete time using least squares estimates with complexity penalties

A. Krzyżak, M. Kohler, 2012 .

The Smoking Gun: Statistical Theory Improves Neural Network Estimates

Michael Kohler, Harro Walk, Sophie Langer, 2021 .

Weakly universally consistent static forecasting of stationary and ergodic time series via local averaging and least squares estimates

Michael Kohler, Harro Walk, Tina Felber, 2013 .

Weakly Universally Consistent Forecasting of Stationary and Ergodic Time Series

Michael Kohler, Daniel Jones, Harro Walk, 2012, IEEE Transactions on Information Theory.

Uncertainty Quantification in Case of Imperfect Models: A Review pdf

Michael Kohler, Sebastian Kersting, M. Kohler, 2020, ArXiv.

A bound on the expected maximal deviation of averages from their means

Michael Kohler, Michael Hamers, M. Kohler, 2003 .

Nonparametric recursive quantile estimation

A. Krzyżak, M. Kohler, Harro Walk, 2014 .

Convergence rates for shallow neural networks learned by gradient descent

M. Kohler, Harro Walk, Alina Braun, 2021, Bernoulli.

Nonasymptotic Bounds on the L2 Error of Neural Network Regression Estimates

M. Kohler, M. Hamers, 2006 .