Nonparametric regression estimation using penalized least squares. - Michael Kohler; Adam Krzyzak (IEEE Trans. Inf. Theory, 2001)

A Distribution-Free Theory of Nonparametric Regression. - László Györfi; Michael Kohler; Adam Krzyzak; Harro Walk (Springer, 2002)

Adaptive regression estimation with multilayer feedforward neural networks. - Michael Kohler; Adam Krzyzak (ISIT, 2004)

Rates of convergence for adaptive regression estimates with multiple hidden layer feedforward neural networks. - Michael Kohler; Adam Krzyzak (ISIT, 2005)

Nonparametric Estimation of Conditional Distributions. - László Györfi; Michael Kohler (IEEE Trans. Inf. Theory, 2007)

On the Rate of Convergence of Local Averaging Plug-In Classification Rules Under a Margin Condition. - Michael Kohler; Adam Krzyzak (IEEE Trans. Inf. Theory, 2007)

Automatic recognition of German news focusing on future-directed beliefs and intentions. - Judith Eckle-Kohler; Michael Kohler; Jens Mehnert (Comput. Speech Lang., 2008)

On application of nonparametric regression estimation to options pricing. - Michael Kohler; Adam Krzyzak; Harro Walk (ISIT, 2009)

Estimation of a Regression Function by Maxima of Minima of Linear Functions. - Adil M. Bagirov; Conny Clausen; Michael Kohler (IEEE Trans. Inf. Theory, 2009)

An algorithm for the estimation of a regression function by continuous piecewise linear functions. - Adil M. Bagirov; Conny Clausen; Michael Kohler (Comput. Optim. Appl., 2010)

An L2-boosting algorithm for estimation of a regression function. - Adil M. Bagirov; Conny Clausen; Michael Kohler (IEEE Trans. Inf. Theory, 2010)

Creating ISO/EN 13606 Archetypes Based on Clinical Information Needs. - Christoph Rinner; Michael Kohler; Gudrun Hübner-Bloder; Samrend Saboor; Elske Ammenwerth; Georg Duftschmid (EFMI-STC, 2011)

Clinical Situations and Information Needs of Physicians During Treatment of Diabetes Mellitus Patients: a Triangulation Study. - Gudrun Hübner-Bloder; Georg Duftschmid; Michael Kohler; Christoph Rinner; Samrend Saboor; Elske Ammenwerth (MIE, 2011)

The Archetype-Enabled EHR System ZK-ARCHE - Integrating the ISO/EN 13606 Standard and IHE XDS Profile. - Michael Kohler; Christoph Rinner; Gudrun Hübner-Bloder; Samrend Saboor; Elske Ammenwerth; Georg Duftschmid (MIE, 2011)

Analysis of the rate of convergence of least squares neural network regression estimates in case of measurement errors. - Michael Kohler; Jens Mehnert (Neural Networks, 2011)

An EHR Prototype Using Structured ISO/EN 13606 Documents to Respond to Identified Clinical Information Needs of Diabetes Specialists: A Controlled Study on Feasibility and Impact. - Gudrun Hübner-Bloder; Georg Duftschmid; Michael Kohler; Christoph Rinner; Samrend Saboor; Elske Ammenwerth (AMIA, 2012)

Nonparametric estimation of non-stationary velocity fields from 3D particle tracking velocimetry data. - Michael Kohler; Adam Krzyzak (Comput. Stat. Data Anal., 2012)

Weakly Universally Consistent Forecasting of Stationary and Ergodic Time Series. - Daniel Jones; Michael Kohler; Harro Walk (IEEE Trans. Inf. Theory, 2012)

Searching for Document Contents in an IHE-XDS EHR Architecture via Archetype-Based Indexing of Document Types. - Christoph Rinner; Michael Kohler; Samrend Saboor; Gudrun Hübner-Bloder; Elske Ammenwerth; Georg Duftschmid (MedInfo, 2013)

Estimation of the optimal design of a nonlinear parametric regression problem via Monte Carlo experiments. - Ida Hertel; Michael Kohler (Comput. Stat. Data Anal., 2013)

The EHR-ARCHE project: Satisfying clinical information needs in a Shared Electronic Health Record System based on IHE XDS and Archetypes. - Georg Duftschmid; Christoph Rinner; Michael Kohler; Gudrun Hübner-Bloder; Samrend Saboor; Elske Ammenwerth (Int. J. Medical Informatics, 2013)

Estimation of a Density Using Real and Artificial Data. - Luc Devroye; Tina Felber; Michael Kohler (IEEE Trans. Inf. Theory, 2013)

Density estimation using real and artificial data. - Tina Felber; Michael Kohler; Adam Krzyzak (ISIT, 2014)

Optimal global rates of convergence for noiseless regression estimation problems with adaptively chosen design. - Michael Kohler (J. Multivar. Anal., 2014)

Adaptive Density Estimation From Data With Small Measurement Errors. - Tina Felber; Michael Kohler; Adam Krzyzak (IEEE Trans. Inf. Theory, 2015)

The rates of convergence of neural network estimates of hierarchical interaction regression models. - Michael Kohler; Adam Krzyzak (ISIT, 2016)

Efficient automotive grid maps using a sensor ray based refinement process. - Ruben Jungnickel; Michael Kohler; Franz Korf (Intelligent Vehicles Symposium, 2016)

Empirical Comparison of Nonparametric Regression Estimates on Real Data. - Daniel Jones; Michael Kohler; Adam Krzyzak; Alexander Richter (Commun. Stat. Simul. Comput., 2016)

Nonparametric Quantile Estimation Based on Surrogate Models. - Georg C. Enss; Michael Kohler; Adam Krzyzak; Roland Platz (IEEE Trans. Inf. Theory, 2016)

Nonparametric estimation of a function from noiseless observations at random points. - Benedikt Bauer; Luc Devroye; Michael Kohler; Adam Krzyzak; Harro Walk (J. Multivar. Anal., 2017)

Nonparametric estimation of a latent variable model. - Augustin Kelava; Michael Kohler; Adam Krzyzak; Tim Fabian Schaffland (J. Multivar. Anal., 2017)

Nonparametric Regression Based on Hierarchical Interaction Models. - Michael Kohler; Adam Krzyzak (IEEE Trans. Inf. Theory, 2017)

Adaptive Estimation of Quantiles in a Simulation Model. - Michael Kohler; Adam Krzyzak (IEEE Trans. Inf. Theory, 2018)

Delay Estimation for Time Synchronization of a Bistatic Transfer Function Measurement Setup to Single Received Pulses. - Michael Kohler; Alexander Saam; Josef Worms; Daniel W. O'Hagan; Jan Novacek; Oliver Bringmann 001 (SPA, 2019)

On the rate of convergence of fully connected very deep neural network regression estimates. - Michael Kohler; Sophie Langer (CoRR, 2019)

Deep Learning and MARS: A Connection. - Michael Kohler; Adam Krzyzak; Sophie Langer (CoRR, 2019)

Estimation of a Density From an Imperfect Simulation Model. - Michael Kohler; Adam Krzyzak (IEEE Trans. Inf. Theory, 2019)

Common Voice: A Massively-Multilingual Speech Corpus. - Rosana Ardila; Megan Branson; Kelly Davis; Michael Kohler; Josh Meyer; Michael Henretty; Reuben Morais; Lindsay Saunders; Francis M. Tyers; Gregor Weber (LREC, 2020)

On the rate of convergence of image classifiers based on convolutional neural networks. - Michael Kohler; Adam Krzyzak; Benjamin Walter 001 (CoRR, 2020)

On the rate of convergence of a deep recurrent neural network estimate in a regression problem with dependent data. - Michael Kohler; Adam Krzyzak (CoRR, 2020)

Uncertainty Quantification in Case of Imperfect Models: A Review. - Sebastian Kersting; Michael Kohler (CoRR, 2020)

Statistical Analysis of Bistatic Radar Ground Clutter for Different German Rural Environments. - Michael Kohler; Daniel W. O'Hagan; Matthias Weiss; David Wegner; Josef Worms; Oliver Bringmann 001 (Sensors, 2020)

Analysis of convolutional neural network image classifiers in a rotationally symmetric model. - Michael Kohler; Benjamin Walter 001 (CoRR, 2022)

Comparison of DVB-T Passive Radar Simulated and Measured Bistatic RCS Values for a Pilatus PC-12 Aircraft. - Peter J. Speirs; S. Martin Ummenhofer; Christof Schüpbach; Matthias Renker; Peter Wellig; Diego Cristallini; Daniel W. O'Hagan; Michael Kohler; Axel Murk (Sensors, 2022)

On the Rate of Convergence of a Classifier Based on a Transformer Encoder. - Iryna Gurevych; Michael Kohler; Gözde Gül Sahin (IEEE Trans. Inf. Theory, 2022)

Estimation of a Function of Low Local Dimensionality by Deep Neural Networks. - Michael Kohler; Adam Krzyzak; Sophie Langer (IEEE Trans. Inf. Theory, 2022)

Analysis of the expected L2 error of an over-parametrized deep neural network estimate learned by gradient descent without regularization. - Selina Drews; Michael Kohler (CoRR, 2023)

On the rate of convergence of an over-parametrized Transformer classifier learned by gradient descent. - Michael Kohler; Adam Krzyzak (CoRR, 2023)

Analysis of Convolutional Neural Network Image Classifiers in a Rotationally Symmetric Model. - Michael Kohler; Benjamin Kohler (IEEE Trans. Inf. Theory, 2023)

Rate of Convergence of an Over-Parametrized Convolutional Neural Network Image Classifier Learned by Gradient Descent. - Michael Kohler; Benjamin Walter 001; Adam Krzyzak (ISIT, 2024)

Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent. - Michael Kohler; Adam Krzyzak; Benjamin Walter 001 (CoRR, 2024)

Statistically guided deep learning. - Michael Kohler; Adam Krzyzak (CoRR, 2025)

Analysis of the Rate of Convergence of an Over-Parametrized Deep Neural Network Estimate Learned by Gradient Descent. - Michael Kohler; Adam Krzyzak (IEEE Trans. Inf. Theory, 2025)