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Machine Learning Section

 

Department of Computer Science (DIKU)

University of Copenhagen

 

Universitetsparken 1

2100 København N

Denmark

 

Room: 1-1-N110

 

EMail: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

 

Publications

You can find my Google Scholar profile here.

 

Journals

 

  1. Corneliu Florea and Fabian Gieseke. Artistic Movement Recognition by Consensus of Boosted SVM Based Experts. Journal of Visual Communication and Image Representation, 2018.  accepted 

  2. Fabian Gieseke, Cosmin Oancea, and Christian Igel. bufferkdtree: A Python library for massive nearest neighbor queries on multi-many-core devices. Knowledge-Based Systems 120:1–3, 2017.   

  3. Fabian Gieseke, Steven Bloemen, Cas Bogaard, Tom Heskes, Jonas Kindler, Richard A Scalzo, Valerio A R M Ribeiro, Jan Roestel, Paul J Groot, Fang Yuan, Anais Möller, and Brad E Tucker. Convolutional Neural Networks for Transient Candidate Vetting in Large-Scale Surveys. Monthly Notices of the Royal Astronomical Society (MNRAS), 2017.   

  4. R S Souza, M L L Dantas, M V Costa-Duarte, E D Feigelson, M Killedar, P -Y Lablanche, R Vilalta, A Krone-Martins, R Beck, and Fabian Gieseke. A probabilistic approach to emission-line galaxy classification. Monthly Notices of the Royal Astronomical Society (MNRAS), 2017.   

  5. Jan Kremer, Kristoffer Stensbo-Smidt, Fabian Gieseke, Kim Steenstrup Pedersen, and Christian Igel. Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy. IEEE Intelligent Systems 32(2):16–22, 2017. 

  6. Robert Beck, Chieh A Lin, Emille O Ishida, Fabian Gieseke, Rafel S Souza, Marcus V Costa-Duarte, Mohammed W Hattab, and Alberto Krone-Martins. On the realistic validation of photometric redshifts. Monthly Notices of the Royal Astronomical Society (MNRAS) 468(4):4323-4339, 2017.   

  7. Kristoffer Stensbo-Smidt, Fabian Gieseke, Andrew Zirm, Kim Steenstrup Pedersen, and Christian Igel. Sacrificing information for the greater good: how to select photometric bands for optimal accuracy. Monthly Notices of the Royal Astronomical Society (MNRAS) 464(3):2577-2596, 2017.   

  8. Michele Sasdelli, E O Ishida, R Vilalta, M Aguena, V C Busti, H Camacho, A M M Trindade, Fabian Gieseke, R S Souza, Y T Fantaye, and P A Mazzali. Exploring the spectroscopic diversity of type Ia supernovae with DRACULA: A machine learning approach. Monthly Notices of the Royal Astronomical Society (MNRAS) 461(2):2044-2059, 2016.   

  9. Jan Kremer, Fabian Gieseke, Kim Steenstrup Pedersen, and Christian Igel. Nearest Neighbor Density Ratio Estimation for Large-Scale Applications in Astronomy. Astronomy and Computing 12:62–72, 2015.   

  10. Fabian Gieseke, Antti Airola, Tapio Pahikkala, and Oliver Kramer. Fast and Simple Gradient-Based Optimization for Semi-Supervised Support Vector Machines. Neurocomputing (ICPRAM 2012 Special Issue) 123(10):23-32, 2014.   

  11. Tapio Pahikkala, Antti Airola, Fabian Gieseke, and Oliver Kramer. On Unsupervised Training of Multi-Class Regularized Least-Squares Classifiers. Journal of Computer Science and Technology (ICDM 2012 Special Issue) 29(1):90–104, 2014.   

  12. Fabian Gieseke. From Supervised to Unsupervised Support Vector Machines and Applications in Astronomy. KI – Künstliche Intelligenz (abstract of my PhD thesis) 27(3):281-285, 2013.   

  13. Oliver Kramer, Fabian Gieseke, and Kai Lars Polsterer. Learning Morphological Maps of Galaxies with Unsupervised Regression. Expert Systems with Applications 40(8):2841-2844, 2013.   

  14. Kai Lars Polsterer, Peter Zinn, and Fabian Gieseke. Finding New High-Redshift Quasars by Asking the Neighbours. Monthly Notices of the Royal Astronomical Society (MNRAS) 428(1):226-235, 2013.   

  15. Oliver Kramer, Fabian Gieseke, and Benjamin Satzger. Wind Energy Prediction and Monitoring with Neural Computation. Neurocomputing 109(0):84-93, 2013.   

  16. Fabian Gieseke, Gabriel Moruz, and Jan Vahrenhold. Resilient K-d Trees: K-Means in Space Revisited. Frontiers of Computer Science (ICDM 2010 Special Issue) 6(2):166-178, 2012.   

  17. Oliver Kramer and Fabian Gieseke. Evolutionary Kernel Density Regression. Expert Systems with Applications 10(39):9246-9254, 2012.   

  18. Fabian Gieseke, Oliver Kramer, Antti Airola, and Tapio Pahikkala. Efficient Recurrent Local Search Strategies for Semi- and Unsupervised Regularized Least-Squares Classification. Evolutionary Intelligence 5(3):189-205, 2012.   

  19. Fabian Gieseke, Joachim Gudmundsson, and Jan Vahrenhold. Pruning Spanners and Constructing Well-Separated Pair Decompositions in the Presence of Memory Hierarchies. Journal of Discrete Algorithms (JDA) 8(3):259-272, 2010.   

 

Peer-Reviewed Conference and Workshop Contributions

 

  1. Fabian Gieseke, Cosmin Eugen Oancea, Ashish Mahaba, Christian Igel, and Tom Heskes. Bigger Buffer k-d Trees on Multi-Many-Core Systems. In High Performance Computing for Computational Science – VECPAR. 2018, Accepted.  draft 

  2. Fabian Gieseke and Christian Igel. Training Big Random Forests with Little Resources. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). 2018, Accepted.  draft 

  3. Malte Mehren, Fabian Gieseke, Jan Verbesselt, Sabina Rosca, Stéphanie Horion, and Achim Zeileis. Massively-Parallel Break Detection for Satellite Data. In Proceedings of the 30th International Conference on Scientific and Statistical Database Management (SSDBM). 2018, Accepted.  draft 

  4. Fabian Gieseke, Kai Polsterer, Ashish Mahabal, Christian Igel, and Tom Heskes. Massively-Parallel Best Subset Selection for Ordinary Least-Squares Regression. In IEEE Symposium Series on Computational Intelligence (SSCI). 2017, in press.   

  5. Ashish Mahabal, Kshiteej Sheth, Fabian Gieseke, Akshay Pai, George Djorgovski, Andrew Drake, and Matthew Graham. Deep-Learnt Classification of Light Curves. In IEEE Symposium Series on Computational Intelligence (SSCI). 2017, in press.   

  6. Corneliu Florea, Cosmin Toca, and Fabian Gieseke. Artistic Movement Recognition by Boosted Fusion of Color Structure and Topographic Description. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). 2017, 569–577.   

  7. Kai Lars Posterer, Fabian Gieseke, Christian Igel, Bernd Doser, and Nikos Gianniotis. Parallelized rotation and flipping INvariant Kohonen maps (PINK ) on GPUs. In Proceedings of the 24nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). 2016, 405-410.   

  8. Fabian Gieseke, Tapio Pahikkala, and Tom Heskes. Batch Steepest-Descent-Mildest-Ascent for Interactive Maximum Margin Clustering. In Proceedings of the 14th International Symposium on Intelligent Data Analysis. Advances in Intelligent Data Analysis XIV 9385. 2015, 95–107.   

  9. Fabian Gieseke. An Efficient Many-Core Implementation for Semi-Supervised Support Vector Machines. In International Workshop on Machine Learning, Optimization, and Big Data (MOD2015). 2015, 145–157.   

  10. Oliver Kramer, Fabian Gieseke, Justin Heinermann, Jendrik Poloczek, and Nils Treiber. A Framework for Data Mining in Wind Power Time Series. In Proceedings of the 2nd ECML/PKDD 2014 International Workshop on Data Analytics for Renewable Energy Integration (DARE'14), Lecture Notes in Compute Science 8817. 2014, 97-107.   

  11. Fabian Gieseke, Justin Heinermann, Cosmin Oancea, and Christian Igel. Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs. In Proceedings of the 31st International Conference on Machine Learning (ICML) 32(1). 2014, 172-180.   

  12. Fabian Gieseke, Kai Lars Posterer, Cosmin Oancea, and Christian Igel. Speedy Greedy Feature Selection: Better Redshift Estimation via Massive Parallelism. In Proceedings of the 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). 2014, 87–92.   

  13. Fabian Gieseke, Tapio Pahikkala, and Christian Igel. Polynomial Runtime Bounds for Fixed-Rank Unsupervised Least-Squares Classification. In Proceedings of the 5th Asian Conference on Machine Learning (ACML). 2013, 62-71.   

  14. Justin Heinermann, Oliver Kramer, Kai Lars Polsterer, and Fabian Gieseke. On GPU-Based Nearest Neighbor Queries for Large-Scale Photometric Catalogs in Astronomy. In KI 2013: Advances in Artificial Intelligence. Lecture Notes in Computer Science series, volume 8077, Springer, 2013, pages 86-97.   

  15. Oliver Kramer, Nils Treiber, and Fabian Gieseke. Machine Learning in Wind Energy Information Systems. In EnviroInfo. 2013, 16-24. 

  16. Fabian Gieseke and Oliver Kramer. Towards Non-Linear Constraint Estimation for Expensive Optimization. In EvoApplications. 2013, 459-468.   

  17. Tapio Pahikkala, Antti Airola, Fabian Gieseke, and Oliver Kramer. Unsupervised Multi-Class Regularized Least-Squares Classification. In Proceedings of the 12th IEEE International Conference on Data Mining (ICDM). 2012, 585-594.   

  18. Fabian Gieseke, Antti Airola, Tapio Pahikkala, and Oliver Kramer. Sparse Quasi-Newton Optimization for Semi-Supervised Support Vector Machines. In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods (ICPRAM). 2012, 45-54.   

  19. Oliver Kramer and Fabian Gieseke. Short-Term Wind Energy Forecasting Using Support Vector Regression. In Proceedings of the International Conference on Soft Computing Models in Industrial and Environmental Applications. 2011, 271-280.   

  20. Fabian Gieseke, Oliver Kramer, Antti Airola, and Tapio Pahikkala. Speedy Local Search for Semi-Supervised Regularized Least-Squares. In Proceedings of the 34th Annual German Conference on Artificial Intelligence. 2011, 87-98.   

  21. Oliver Kramer and Fabian Gieseke. Variance Scaling for EDAs Revisited. In Proceedings of the 34th Annual German Conference on Artificial Intelligence. 2011, 169-178.   

  22. Oliver Kramer and Fabian Gieseke. Analysis of wind energy time series with kernel methods and neural networks. In Proceedings of the 7th International Conference on Natural Computation. 2011, 2381-2385.   

  23. Fabian Gieseke, Gabriel Moruz, and Jan Vahrenhold. Resilient K-d Trees: K-Means in Space Revisited. In Proceedings of the 10th IEEE International Conference on Data Mining (ICDM). 2010, 815-820.   

  24. Fabian Gieseke, Kai Lars Polsterer, Andreas Thom, Peter Zinn, Dominik Bomans, Ralf-Jürgen Dettmar, Oliver Kramer, and Jan Vahrenhold. Detecting Quasars in Large-Scale Astronomical Surveys. In Proceedings of the 9th International Conference on Machine Learning and Applications (ICMLA). 2010, 352-357.   

  25. Fabian Gieseke, Tapio Pahikkala, and Oliver Kramer. Fast Evolutionary Maximum Margin Clustering. In Proceedings of the 26th International Conference on Machine Learning (ICML). 2009, 361-368.   

  26. Fabian Gieseke and Jan Vahrenhold. Cache-Oblivious Construction of a Well-Separated Pair Decomposition. In Proceedings of the 25th European Workshop on Computational Geometry. 2009, 341-344.   

  27. Evgeni Tsivtsivadze, Fabian Gieseke, Tapio Pahikkala, Jorma Boberg, and Tapio Salakoski. Learning Preferences with Co-Regularized Least-Squares. In Proceedings of the ECML/PKDD Workshop on Preference Learning. 2008, 52-66.   

 

Theses

 

  1. Fabian Gieseke. From Supervised to Unsupervised Support Vector Machines and Applications in Astronomy. PhD thesis, Carl von Ossietzky Universität Oldenburg, 2011.   

  2. Fabian Gieseke. Algorithmen zur Konstruktion und Ausdünnung von Spanner-Graphen im Cache-Oblivious-Modell. Diplomarbeit, Westfälische Wilhelms-Universität Münster (in German), 2006.   

 

Other Contributions

 

  1. Michele Sasdelli, Emille O Ishida, R Vilalta, M Aguena, V C Busti, H Camacho, A M M Trindade, Fabian Gieseke, R S Souza, Y T Fantaye, and P A Mazzali. Exploring the spectroscopic diversity of type Ia supernovae with DRACULA: a machine learning approach. CoRR abs/1512.06810, 2015. 

  2. Fabian Gieseke, Cosmin E Oancea, Ashish Mahabal, Christian Igel, and Tom Heskes. Bigger Buffer k-d Trees on Multi-Many-Core Systems. CoRR abs/1512.02831, 2015. 

  3. Kristoffer Stensbo-Smidt, Fabian Gieseke, Chstian Igel, Andrew Zirm, and Kim Steenstrup Pedersen. Simple, Fast and Accurate Photometric Estimation of Specific Star Formation Rate. CoRR abs/1511.05424, 2015. 

  4. Fabian Gieseke. Big Data Analytics in Astronomy... using the supercomputer under your desk!. Big Data in a Transdisciplinary Perspective. 7th Herrenhausen Conference of the Volkswagen Foundation, 25-27 March 2015, Herrenhausen Palace, 2015.   

  5. Kai Polsterer, Fabian Gieseke, and Christian Igel. Automatic Classification of Galaxies via Machine Learning Techniques - Parallelized Rotation/Flipping Invariant Kohonen Map (PINK). In Proceedings of the 24th Annual Astronomical Data Analysis Software & Systems conference (ADASS). 2014.    

  6. Kai Polsterer, Fabian Gieseke, Christian Igel, and Tomotsugu Goto. Improving the Performance of Photometric Regression Models via Massive Parallel Feature Selection. In Proceedings of the 23rd Annual Astronomical Data Analysis Software & Systems conference (ADASS). 2013.    

  7. Fabian Gieseke. Von uberwachten zu unuberwachten Support-Vektor-Maschinen und Anwendungen in der Astronomie. In Steffen Hölldobler et al. (ed.). Ausgezeichnete Informatikdissertationen 2012. GI-Edition Lecture Notes in Informatics (LNI), D-13 series, Bonner Köllen Verlag (in German), 2013.   

  8. Fabian Gieseke, Kai Polsterer, and Peter Zinn. Photometric Redshift Estimation of Quasars: Local versus Global Regression. In Proceedings of the 21st Annual Astronomical Data Analysis Software & Systems conference (ADASS). 2011.    

  9. Kai Polsterer, Fabian Gieseke, and Oliver Kramer. Galaxy Classification without Feature Extraction. In Proceedings of the 21st Annual Astronomical Data Analysis Software & Systems conference (ADASS). 2011.    

  10. Fabian Gieseke, Kai Polsterer, Andreas Thom, Peter Zinn, Dominik Bomans, Ralf-Jürgen Dettmar, Oliver Kramer, and Jan Vahrenhold. Identifying Quasars in Large-Scale Spectroscopic Surveys. Astroinformatics 2010, Poster, Pasadena, 2010.   

  11. Kai Polsterer, Fabian Gieseke, Andreas Thom, Oliver Kramer, Jan Vahrenhold, Dominik Bomans, and Ralf-Jürgen Dettmar. Discriminating Point and Extended Sources with k-Nearest Neighbors. Astroinformatics 2010, Poster, Pasadena, 2010.   

  12. Fabian Gieseke. Regularized Least-Squares for Maximum Margin Clustering and Semi-Supervised Classification. Machine Learning Summer School, Cambridge, UK, 2009. 

  13. Fabian Gieseke. Constructing and Pruning Spanners in the Cache-Oblivious Model. Summer School on Algorithmic Data Analysis (SADA07), Helsinki, Finland, 2007.   

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