Daniel Horn

Cited by

	All	Since 2018
Citations	483	427
h-index	9	9
i10-index	8	7

2015201620172018201920202021202220233 16 31 59 71 74 82 84 57

Public access

View all

5 articles

1 article

available

not available

Based on funding mandates

Co-authors

Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
Jakob BossekPaderborn UniversityVerified email at uni-paderborn.de
Claus WeihsProfessor für Computergestützte Statistik, TU DortmundVerified email at tu-dortmund.de
Michel LangTU Dortmund; LMU MunichVerified email at statistik.tu-dortmund.de
Jakob RichterTU DortmundVerified email at statistik.tu-dortmund.de
Janek ThomasLudwig-Maximilians-University MunichVerified email at stat.uni-muenchen.de
Tobias WagnerInstitute of Machining Technology (ISF), TU DortmundVerified email at isf.de
Tobias GlasmachersVerified email at idsia.ch
Aydin DemirciogluUniversity Hospital EssenVerified email at uk-essen.de
Dirk BiermannProfessor für Maschinenbau, TU DortmundVerified email at isf.de
Martin ZaeffererDHBW RavensburgVerified email at dhbw-ravensburg.de
Karin SchorkRuhr-Universität BochumVerified email at rub.de
Xudong SunHelmholtz MunichVerified email at kth.se
Kristian KerstingProfessor of AI & ML, TU Darmstadt, Co-Director hessian.ai, DFKI, Germany, CLAIRE & ELLISVerified email at cs.tu-darmstadt.de
Jens TeubnerTU Dortmund UniversityVerified email at cs.tu-dortmund.de
Malte JastrowFakultät Statistik, TU DortmundVerified email at tu-dortmund.de
Jörg StorkSenior Data Scientist, Otto Fuchs KGVerified email at otto-fuchs.com
Pascal KerschkeProfessor of Big Data Analytics in Transportation, TU Dresden, GermanyVerified email at tu-dresden.de
Andreas GrollProfessor der Statistik, Technische Universität DortmundVerified email at math.lmu.de
Markus PaulyDepartment of Statistics, TU Dortmund UniversityVerified email at udo.edu

Daniel Horn

Statistical Methods for Big Data, TU Dortmund

Verified email at tu-dortmund.de - Homepage

Multi-Objective / Model-Based Optimization Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions B Bischl, J Richter, J Bossek, D Horn, J Thomas, M Lang arXiv preprint arXiv:1703.03373, 2017	196*	2017
Model-based multi-objective optimization: taxonomy, multi-point proposal, toolbox and benchmark D Horn, T Wagner, D Biermann, C Weihs, B Bischl International Conference on Evolutionary Multi-Criterion Optimization, 64-78, 2015	82	2015
Multi-objective parameter configuration of machine learning algorithms using model-based optimization D Horn, B Bischl Computational Intelligence (SSCI), 2016 IEEE Symposium Series on, 1-8, 2016	49	2016
BBmisc: Miscellaneous helper functions for B. Bischl B Bischl, M Lang, J Bossek, D Horn, J Richter, D Surmann R package version 1.11, 2017	44*	2017
A comparative study on large scale kernelized support vector machines D Horn, A Demircioğlu, B Bischl, T Glasmachers, C Weihs Advances in Data Analysis and Classification 12, 867-883, 2018	29	2018
First Investigations on Noisy Model-Based Multi-objective Optimization D Horn, M Dagge, X Sun, B Bischl International Conference on Evolutionary Multi-Criterion Optimization, 298-313, 2017	23	2017
Industrial data science: developing a qualification concept for machine learning in industrial production N Bauer, L Stankiewicz, M Jastrow, D Horn, J Teubner, K Kersting, ... European Conference on Data Analysis (ECDA), 2018	13	2018
Efficient global optimization: Motivation, variations and applications C Weihs, S Herbrandt, N Bauer, K Friedrichs, D Horn	10	2016
A first analysis of kernels for kriging-based optimization in hierarchical search spaces M Zaefferer, D Horn Parallel Problem Solving from Nature–PPSN XV: 15th International Conference …, 2018	9	2018
Surrogates for hierarchical search spaces: the wedge-kernel and an automated analysis D Horn, J Stork, NJ Schüßler, M Zaefferer Proceedings of the Genetic and Evolutionary Computation Conference, 916-924, 2019	7	2019
ParamHelpers: Helpers for parameters in black-box optimization, tuning, and machine learning B Bischl, M Lang, J Bossek, D Horn, K Schork, J Richter, P Kerschke R package version 1, 23, 2016	7	2016
Using Sequential Statistical Tests to Improve the Performance of Random Search in hyperparameter Tuning P Buczak, D Horn arXiv preprint arXiv:2112.12438, 2021	3	2021
Multi-objective selection of algorithm portfolios: Experimental validation D Horn, K Schork, T Wagner Parallel Problem Solving from Nature–PPSN XIV: 14th International Conference …, 2016	3	2016
Multi-objective Analysis of Machine Learning Algorithms Using Model-based Optimization Techniques D HORN	2	2019
Multi-objective selection of algorithm portfolios D Horn, B Bischl, A Demircioglu, T Glasmachers, T Wagner, C Weihs Archives of Data Science, Series A (Online First) 2 (1), 15s, 2017	2	2017
Fast model selection by limiting SVM training times A Demircioglu, D Horn, T Glasmachers, B Bischl, C Weihs arXiv preprint arXiv:1602.03368, 2016	2	2016
Big Data Classification-Many Features, Many Observations C Weihs, D Horn, B Bischl	2*
Contextual Shift Method (CSM) G Schmitz, D Wilmes, A Gerharz, D Horn, E Müller International Conference on Big Data Analytics and Knowledge Discovery, 101-106, 2023		2023
RODD: Robust Outlier Detection in Data Cubes L Kuhlmann, D Wilmes, E Müller, M Pauly, D Horn arXiv preprint arXiv:2303.08193, 2023		2023
Using Sequential Statistical Tests for Efficient Hyperparameter Tuning P Buczak, A Groll, M Pauly, J Rehof, D Horn arXiv preprint arXiv:2112.12438, 2021		2021

The system can't perform the operation now. Try again later.

Articles 1–20

Citations per year

Duplicate citations

Merged citations

Add co-authorsCo-authors

Follow

Cited by

Co-authors