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ORCIDNicolas Schilling
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2010 – 2019
- 2018
[b1]Nicolas Schilling:
Bayesian Hyperparameter Optimization - Relational and Scalable Surrogate Models for Hyperparameter Optimization Across Problem Instances. University of Hildesheim, Germany, 2018- [j2]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
:
Scalable Gaussian process-based transfer surrogates for hyperparameter optimization. Mach. Learn. 107(1): 43-78 (2018) - [c17]Mohsan Jameel, Nicolas Schilling, Lars Schmidt-Thieme:
Towards Distributed Pairwise Ranking using Implicit Feedback. SIGIR 2018: 973-976 - 2017
- [c16]Nghia Duong-Trung, Nicolas Schilling, Lars Schmidt-Thieme:
Finding Hierarchy of Topics from Twitter Data. LWDA 2017: 39 - [c15]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Automatic Frankensteining: Creating Complex Ensembles Autonomously. SDM 2017: 741-749 - 2016
- [j1]Josif Grabocka, Nicolas Schilling, Lars Schmidt-Thieme:
Latent Time-Series Motifs. ACM Trans. Knowl. Discov. Data 11(1): 6:1-6:20 (2016) - [c14]Nghia Duong-Trung, Nicolas Schilling, Lars Schmidt-Thieme:
Near Real-time Geolocation Prediction in Twitter Streams via Matrix Factorization Based Regression. CIKM 2016: 1973-1976 - [c13]Mit Shah, Josif Grabocka, Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme:
Learning DTW-Shapelets for Time-Series Classification. CODS 2016: 3:1-3:8 - [c12]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Hyperparameter Optimization Machines. DSAA 2016: 41-50 - [c11]Nghia Duong-Trung, Nicolas Schilling, Lucas Drumond, Lars Schmidt-Thieme:
Matrix Factorization for Near Real-time Geolocation Prediction in Twitter Stream. LWDA 2016: 89-100 - [c10]Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme:
Scalable Hyperparameter Optimization with Products of Gaussian Process Experts. ECML/PKDD (1) 2016: 33-48 - [c9]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization. ECML/PKDD (1) 2016: 199-214 - [i2]Martin Wistuba, Nghia Duong-Trung, Nicolas Schilling, Lars Schmidt-Thieme:
Bank Card Usage Prediction Exploiting Geolocation Information. CoRR abs/1610.03996 (2016) - 2015
- [c8]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Learning hyperparameter optimization initializations. DSAA 2015: 1-10 - [c7]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Sequential Model-Free Hyperparameter Tuning. ICDM 2015: 1033-1038 - [c6]Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme:
Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons. ICTAI 2015: 72-79 - [c5]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Learning Data Set Similarities for Hyperparameter Optimization Initializations. MetaSel@PKDD/ECML 2015: 15-26 - [c4]Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme:
Hyperparameter Optimization with Factorized Multilayer Perceptrons. ECML/PKDD (2) 2015: 87-103 - [c3]Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Hyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization. ECML/PKDD (2) 2015: 104-119 - [i1]Josif Grabocka, Nicolas Schilling, Lars Schmidt-Thieme:
Optimal Time-Series Motifs. CoRR abs/1505.00423 (2015) - 2014
- [c2]Josif Grabocka, Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme:
Learning time-series shapelets. KDD 2014: 392-401 - 2013
- [c1]Nicolas Schilling, André Busche, Simone Miller, Michael Jungheim, Martin Ptok, Lars Schmidt-Thieme:
Event Prediction in Pharyngeal High-Resolution Manometry. ECDA 2013: 341-352
Coauthor Index
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