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Machine Learning

All Publications

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Müller, L., Ploner, M., Goerttler, T. and Obermayer, K. (2021). An Interactive Introduction to Model-Agnostic Meta-Learning. Workshop on Visualization for AI Explainability at IEEE VIS


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


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Liu, C., Xie, S., Xie, X., Duan, X., Wang, W. and Obermayer, K. (2018). Design of a Video Feedback SSVEP-BCI System for Car Control Based on the Improved MUSIC Method. Proceedings of the IEEE 6th International Winter Conference on Brain-Computer Interfaces


Boehmer, W., Guo, R. and Obermayer, K. (2016). Non-deterministic Policy Improvement Stabilizes Approximate Reinforcement Learning. Proceedings of the 13th European Workshop on Reinforcement Learning


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.


Graepel, T., Burger, M. and Obermayer, K. (1997). Phase Transitions in Stochastic Self-Organizing Maps. PHYSICAL REVIEW E, 56, 3876 – 3890.


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Lohoff, F., Lautenschlager, M., Mohr, J., Ferraro, T., Sander, T. and Gallinat, J. (2008). Association Between Variation in the Vesicular Monoamine Transporter 1 Gene on Chromosome 8p and Anxiety-Related Personality Traits. Neuroscience Letters, 434, 41 – 45.


Tobia, M. J., Guo, R., Schwarze, U., Böhmer, W., Gläscher, J., Finckh, B., Marschner, A., Büchel, C., Obermayer, K. and Sommer, T. (2014). Neural Systems for Choice and Valuation with Counterfactual Learning Signals. NeuroImage, 89, 57-69.


Graepel, T., Burger, M. and Obermayer, K. (1998). Self-Organizing Maps: Generalizations and New Optimization Techniques. Neurocomputing, 20, 173 – 190.


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.


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


Graepel, T. and Obermayer, K. (1999). A Self-Organizing Map for Proximity Data. Neural Computation, 11, 139 – 155.


Hochreiter, J. and Obermayer, K. (2006). Support Vector Machines for Dyadic Data. Neural Comput., 18, 1472 – 1510.


Knebel, T., Hochreiter, S. and Obermayer, K. (2008). An SMO algorithm for the Potential Support Vector Machine. Neural Comput., 20, 271 – 287.


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Böhmer, W., Grünewalder, S., Nickisch, H. and Obermayer, K. (2012). Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis. Machine Learning, 89, 67–86.


Grünwälder, S. and Obermayer, K. (2011). The Optimal Unbiased Extimator and its Relation to LSTD, TD and MC. Machine Learning, 83, 289 – 330.


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Jain, B. and Obermayer, K. (2007). Theory of the Sample Mean of Structures. LNVD 2007, Learning from Non-vectorial Data, 9-16.


Goerttler, T. and Obermayer, K. (2021). Exploring the Similarity of Representations in Model-Agnostic Meta-Learning. Learning to Learn workshop at ICLR 2021


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Hutter, F., Lücke, J. and Schmidt-Thieme, L. (2015). Beyond Manual Tuning of Hyperparameters. KI - Künstliche Intelligenz, 29, 329-337.


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