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Schöner, H., Stetter, M., Schiessl, I., Mayhew, J., Lund, J., McLoughlin, N. and Obermayer, K. (1999). Blind Separation of Noisy Mixtures by Iterative Decorrelation.. Proceedings. The Learning Workshop, Snowbird, USA, (1 – 2).,


Schöner, H., Stetter, M., Schießl, I., Mayhew, J., Lund, J., McLoughlin, N. and Obermayer, K. (2000). Application of Blind Separation of Sources to Optical Recording of Brain Activity. Advances in Neural Information Processing Systems 12. MIT Press, 949 – 955.,


Seo, S., Beck, A., Matthis, C., Genauck, A., Banaschewski, T., Bokde, A., Bromberg, U., Büchel, C., Quinlan, E., Flor, H., Frouin, V., Garavan, H., Gowland, P., Ittermann, B., Martinot, J., Martinot, M., Nees, F., Orfanos, D., Poustka, L., Hohmann, S., Froehner, J., Smolka, M., Walter, H., Whelan, R., Desrivieres, S., Heinz, A., Schumann, G. and Obermayer, K. (2019). Risk Profiles for Heavy Drinking in Adolescence: Differential Effects of Gender. Addiction Biology, 24, 787-801.


Seo, S., Mohr, J., Beck, A., Wüstenberg, T., Heinz, A. and Obermayer, K. (2015). Predicting the future relapse of alcohol-dependent patients from structural and functional brain images. Addiction Biology, 20, 1042-1055.


Seo, S., Bode, M. and Obermayer, K. (2003). Soft Nearest Prototype Classification. IEEE Transactions on Neural Networks, 14, 390 – 398.


Seo, S., Mohr, J., Ningfei, L., Horn, A. and Obermayer, K. (2015). Incremental pairwise clustering for large proximity matrices. 2015 International Joint Conference on Neural Networks (IJCNN), 1-8.,10.1109/IJCNN.2015.7280637


Seo, S., Mohr, J. and Obermayer, K. (2009). A New Incremental Pairwise Clustering Algorithm. Proceedings of the ICMLA -09: The Eighth International Conference on Machine Learning and Applications. IEEE, 223 – 228.,10.1109/ICMLA.2009.42


Seo, S. and Obermayer, K. (2006). Dynamic Hyperparameter Scaling Method for LVQ Algorithms. IJCNN 2006 Conference Proceedings. IEEE, 3196 – 3203.,10.1109/IJCNN.2006.247304


Seo, S. and Obermayer, K. (2004). Self-Organizing Maps and Clustering Methods for Matrix Data. Neural Networks Special Issue, 17, 1211 – 1229.


Seo, S. and Obermayer, K. (2003). Soft Learning Vector Quantization. Neural Computation, 15, 1589 – 1604.


Seo, S., Wallat, M., Graepel, T. and Obermayer, K. (2000). Gaussian Process Regression: Active Data Selection and Test Point Rejection. Neural Networks - IJCNN 2000. IEEE, 241 – 246.,10.1109/IJCNN.2000.861310


Seo, S., Mohr, J., Heekeren, H., Heinz, A., Eppinger, B., Li, S. and Obermayer, K. (2012). A voxel selection method for the multivariate analysis of imaging genetics data.. IJCNN. IEEE, 2884-2890.,10.1109/IJCNN.2012.6252766


Sheikh, A.-S., Shelton, J. A. and Lücke, J. (2014). A Truncated EM Approach for Spike-and-Slab Sparse Coding. Journal of Machine Learning Research, 15, 2653–2687.


Shelton, J. A., Sheikh, A.-S., Bornschein, J., Sterne, P. and Lücke, J. (2015). Nonlinear Spike-And-Slab Sparse Coding for Interpretable Image Encoding. PLoS ONE, 10, e0124088.



Shen, Y., Huang, R., Yan, C. and Obermayer, K. (2014). Risk-Averse Reinforcement Learning for Algorithmic Trading. 2014 IEEE Computational Intelligence for Financial Engineering and Economics, 391-398.,10.1109/CIFEr.2014.6924100


Shen, Y., Stannat, W. and Obermayer, K. (2014). A Unified Framework for Risk-sensitive Markov Control Processes. 53rd IEEE Conference on Decision and Control, 1073-1078.,10.1109/CDC.2014.7039524


Shen, Y., Stannat, W. and Obermayer, K. (2013). Risk-sensitive Markov Control Processes. SIAM Journal on Control and Optimization, 51, 3652–3672.


Shen, Y., Tobia, M. J., Sommer, T. and Obermayer, K. (2014). Risk-sensitive Reinforcement Learning. Neural Computation, 26, 1298-1328.


Spanagel, R., Durstewitz, D., Hansson, A., Heinz, A., Kiefer, F., Köhr, G., Matthäus, F., Nöthen, M. M., Noori, H. R., Obermayer, K., Rietschel, M., Schloss, P., Scholz, H., Schumann, G., Smolka, M., Sommer, W., Vengeliene, V., Walter, H., Wurst, W., Zimmermann, U. S., Group, A. G. R., Stringer, S., Smits, Y. and Derks, E. M. (2013). A systems medicine research approach for studying alcohol addiction. Addiction Biology, 18, 883–896.


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