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Martin Wistuba
Martin Wistuba
Amazon Web Services
Verified email at ismll.de
Title
Cited by
Cited by
Year
Adversarial Robustness Toolbox v1. 0.0
MI Nicolae, M Sinn, MN Tran, B Buesser, A Rawat, M Wistuba, ...
arXiv preprint arXiv:1807.01069, 2018
5862018
Learning time-series shapelets
J Grabocka, N Schilling, M Wistuba, L Schmidt-Thieme
Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014
5662014
A survey on neural architecture search
M Wistuba, A Rawat, T Pedapati
arXiv preprint arXiv:1905.01392, 2019
3532019
Scalable gaussian process-based transfer surrogates for hyperparameter optimization
M Wistuba, N Schilling, L Schmidt-Thieme
Machine Learning 107 (1), 43-78, 2018
1362018
Learning hyperparameter optimization initializations
M Wistuba, N Schilling, L Schmidt-Thieme
2015 IEEE international conference on data science and advanced analytics …, 2015
1232015
Ultra-fast shapelets for time series classification
M Wistuba, J Grabocka, L Schmidt-Thieme
arXiv preprint arXiv:1503.05018, 2015
962015
A comprehensive survey on hardware-aware neural architecture search
H Benmeziane, KE Maghraoui, H Ouarnoughi, S Niar, M Wistuba, ...
arXiv preprint arXiv:2101.09336, 2021
872021
Fast classification of univariate and multivariate time series through shapelet discovery
J Grabocka, M Wistuba, L Schmidt-Thieme
Knowledge and information systems 49, 429-454, 2016
832016
Personalized deep learning for tag recommendation
HTH Nguyen, M Wistuba, J Grabocka, LR Drumond, L Schmidt-Thieme
Advances in Knowledge Discovery and Data Mining: 21st Pacific-Asia …, 2017
802017
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
782015
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
762016
Few-shot bayesian optimization with deep kernel surrogates
M Wistuba, J Grabocka
arXiv preprint arXiv:2101.07667, 2021
682021
Deep learning architecture search by neuro-cell-based evolution with function-preserving mutations
M Wistuba
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2019
652019
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
552016
Sequential model-free hyperparameter tuning
M Wistuba, N Schilling, L Schmidt-Thieme
2015 IEEE international conference on data mining, 1033-1038, 2015
552015
Optimal exploitation of clustering and history information in multi-armed bandit
D Bouneffouf, S Parthasarathy, H Samulowitz, M Wistub
arXiv preprint arXiv:1906.03979, 2019
482019
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
472015
Practical Deep Learning Architecture Optimization
M Wistuba
2018 IEEE 5th International Conference on Data Science and Advanced …, 2018
44*2018
Hardware-Aware Neural Architecture Search: Survey and Taxonomy.
H Benmeziane, K El Maghraoui, H Ouarnoughi, S Niar, M Wistuba, ...
IJCAI, 4322-4329, 2021
432021
Adversarial phenomenon in the eyes of Bayesian deep learning
A Rawat, M Wistuba, MI Nicolae
arXiv preprint arXiv:1711.08244, 2017
432017
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