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• Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme (2018):
  Scalable Gaussian Process based Transfer Surrogates for Hyperparameter Optimization, Machine Learning, Volume:107, issue:1, pp:43-78, Springer
• Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme (2015):
  Fast Classification of Univariate and Multivariate Time series Through Shapelets Discovery, Journal of Knowledge and Information Systems, 5-year impact factor 2.02
• Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme (2014):
  Scalable Classification of Repetitive Time Series Through Frequencies of Local Polynomials, IEEE Transactions on Knowledge and Data Engineering, 5-year impact factor 2.87

Peer-Reviewed Conference Papers:

• Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme (2018):
  Scalable gaussian process-based transfer surrogates for hyperparameter optimization , Machine Learning :43-78.
• Hanh TH Nguyen, Martin Wistuba, Lucas Rego Drumond, Lars Schmidt-Thieme (2017):
  Cnn-fm: Personalized content-aware image tag recommendation , Archives of Data Science, Series A (Online First) .
• Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme (2017):
  Automatic Frankensteining: Creating Complex Ensembles Autonomously, in Proceedings of the 2017 SIAM International Conference on Data Mining (SDM 17) , SIAM , pp. 741-749 .
• Hanh T. H. Nguyen, Martin Wistuba, Josif Grabocka, Lucas Rego Drumond, Lars Schmidt-Thieme (2017):
  Personalized Deep Learning for Tag Recommendation, in Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017), Jeju, South Korea.
• Hanh T. H. Nguyen, Martin Wistuba, Lars Schmidt-Thieme (2017):
  Personalized Tag Recommendation for Images Using Deep Transfer Learning, in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML), Skopje, Macedonia.
• Martin Wistuba, Nghia Duong-Trung, Nicolas Schilling, Lars Schmidt-Thieme (2016):
  Bank Card Usage Prediction Exploiting Geolocation Information, in , Riva del Garda, Italy. ECML-PKDD 2016 Discovery Challenge Award: Best System of the Bank Card Usage Analysis Challenge.
• Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme (2016):
  Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization, in Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML'16), Riva del Garda, Italy.
• Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme (2016):
  Scalable Hyperparameter Optimization with Products of Gaussian Process Experts, in Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML'16), Riva del Garda, Italy.
• Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme (2016):
  Hyperparameter Optimization Machines, in Proceedings of IEEE International Conference on Data Science and Advanced Analytics (DSAA'16), Montreal, Canada.
• Mit Shah, Josif Grabocka, Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme (2016):
  Learning DTW-Shapelets for Time-Series Classification, in ACM IKDD Conference on Data Science. Best Paper Award.
• Nghia Duong-Trung, Martin Wistuba, Lucas Rego Drumond, Lars Schmidt-Thieme (2015):
  Geo_ML@MediaEval Placing Task 2015, in Proceedings of the MediaEval 2015 Multimedia Benchmark Workshop, Wurzen, Germany.
• Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme (2015):
  Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons, in 27th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2015), Vietri sul Mare, Italy.
• Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme (2015):
  Sequential Model-free Hyperparameter Tuning, in Proceedings of IEEE International Conference on Data Mining (ICDM'15), Atlantic City, NJ, USA. Acceptance Rate: 18.2% (147 out of 807).
• Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme (2015):
  Learning Hyperparameter Optimization Initializations, in Proceedings of IEEE International Conference on Data Science and Advanced Analytics (DSAA'15), Paris, France.
• Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme (2015):
  Hyperparameter Optimization with Factorized Multilayer Perceptrons, in Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML'15), Porto, Portugal.
• Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme (2015):
  Hyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization, in Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML'15), Porto, Portugal.
• Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme (2015):
  Learning Data Set Similarities for Hyperparameter Optimization Initializations, in Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2015 (MetaSel@ECML'15), Porto, Portugal.
• Josif Grabocka, Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme (2014):
  Learning Time-Series Shapelets, in Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2014. Acceptance Rate: 14.6% (151 out of 1036).
• Carlotta Schatten, Martin Wistuba, Lars Schmidt-Thieme, Sergio Gutiérrez-Santos (2014):
  Minimal Invasive Integration of Learning Analytics Services in Intelligent Tutoring Systems, in Proceedings of the 14th IEEE International Conference on Advanced Learning Technologies.
• Rasoul Karimi, Martin Wistuba, Alexandros Nanopoulos, Lars Schmidt-Thieme (2013):
  Factorized Decision Trees for Active Learning in Recommender Systems, in 25th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Washington D.C, USA.
• Martin Wistuba, Lars Schmidt-Thieme (2013):
  Supervised Clustering of Social Media Streams, in Working Notes Proceedings of the MediaEval 2013 Workshop, Barcelona, Spain, October 18-19, 2013, CEUR-WS.org.
• Martin Wistuba, Lars Schmidt-Thieme (2013):
  Move Prediction in Go – Modelling Feature Interactions Using Latent Factors, in KI 2013: Advances in Artificial Intelligence, Springer-Verlag Berlin Heidelberg, pp. 260-271. Nominated for Best Paper Award (1 out of 3).
• Martin Wistuba, Lars Schäfers, Marco Platzner (2012):
  Comparison of Bayesian Move Prediction Systems for Computer Go, in Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG), pp. 91-99.