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Nicolas Schilling
Nicolas Schilling
Universität Hildesheim
Bestätigte E-Mail-Adresse bei ismll.de - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Learning time-series shapelets
J Grabocka, N Schilling, M Wistuba, L Schmidt-Thieme
Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014
5622014
Scalable gaussian process-based transfer surrogates for hyperparameter optimization
M Wistuba, N Schilling, L Schmidt-Thieme
Machine Learning 107 (1), 43-78, 2018
1292018
Learning hyperparameter optimization initializations
M Wistuba, N Schilling, L Schmidt-Thieme
2015 IEEE international conference on data science and advanced analytics …, 2015
1172015
Two-stage transfer surrogate model for automatic hyperparameter optimization
M Wistuba, N Schilling, L Schmidt-Thieme
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2016
742016
Hyperparameter search space pruning–a new component for sequential model-based hyperparameter optimization
M Wistuba, N Schilling, L Schmidt-Thieme
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015
712015
Sequential model-free hyperparameter tuning
M Wistuba, N Schilling, L Schmidt-Thieme
2015 IEEE international conference on data mining, 1033-1038, 2015
542015
Learning DTW-shapelets for time-series classification
M Shah, J Grabocka, N Schilling, M Wistuba, L Schmidt-Thieme
Proceedings of the 3rd IKDD Conference on Data Science, 2016, 1-8, 2016
532016
Hyperparameter optimization with factorized multilayer perceptrons
N Schilling, M Wistuba, L Drumond, L Schmidt-Thieme
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015
462015
Automatic Frankensteining: Creating complex ensembles autonomously
M Wistuba, N Schilling, L Schmidt-Thieme
Proceedings of the 2017 SIAM International Conference on Data Mining, 741-749, 2017
432017
Hyperparameter optimization machines
M Wistuba, N Schilling, L Schmidt-Thieme
2016 IEEE International Conference on Data Science and Advanced Analytics …, 2016
412016
Latent time-series motifs
J Grabocka, N Schilling, L Schmidt-Thieme
ACM Transactions on Knowledge Discovery from Data (TKDD) 11 (1), 1-20, 2016
372016
Scalable hyperparameter optimization with products of gaussian process experts
N Schilling, M Wistuba, L Schmidt-Thieme
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2016
362016
Near real-time geolocation prediction in twitter streams via matrix factorization based regression
N Duong-Trung, N Schilling, L Schmidt-Thieme
Proceedings of the 25th ACM international on conference on information and …, 2016
282016
Calculation of upper esophageal sphincter restitution time from high resolution manometry data using machine learning
M Jungheim, A Busche, S Miller, N Schilling, L Schmidt-Thieme, M Ptok
Physiology & behavior 165, 413-424, 2016
232016
Learning data set similarities for hyperparameter optimization initializations.
M Wistuba, N Schilling, L Schmidt-Thieme
Metasel@ pkdd/ecml, 15-26, 2015
202015
Joint model choice and hyperparameter optimization with factorized multilayer perceptrons
N Schilling, M Wistuba, L Drumond, L Schmidt-Thieme
2015 IEEE 27th International Conference on Tools with Artificial …, 2015
122015
Event Prediction in Pharyngeal High-Resolution Manometry.
N Schilling, A Busche, S Miller, M Jungheim, M Ptok, L Schmidt-Thieme
ECDA, 341-352, 2013
42013
An effective approach for geolocation prediction in twitter streams using clustering based discretization
N Duong-Trung, N Schilling, LR Drumond, L Schmidt-Thieme
Archives of Data Science, Series A, 2017
32017
Matrix Factorization for Near Real-time Geolocation Prediction in Twitter Stream.
N Duong-Trung, N Schilling, L Drumond, L Schmidt-Thieme
LWDA, 89-100, 2016
32016
Towards distributed pairwise ranking using implicit feedback
M Jameel, N Schilling, L Schmidt-Thieme
The 41st International ACM SIGIR Conference on Research & Development in …, 2018
22018
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