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TU Berlin

Inhalt des Dokuments

Maschnielles Lernen

Publikationen

2019

Laschos, V., Obermayer, K., Shen, Y. and Stannat, W. (2019). A Fenchel-Moreau-Rockafellar type theorem on the Kantorovich-Wasserstein space with applications in partially observable Markov decision processes. Journal of Mathematical Analysis and Applications


2018

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


2017

Trowitzsch, I., Mohr, J., Kashef, Y. and Obermayer, K. (2017). Robust Detection of Environmental Sounds in Binaural Auditory Scenes. IEEE Transactions on Audio Speech and Language Processing, 25, 1344-1356.


2016

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


2015

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


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.


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


Böhmer, W., Springenberg, J. T., Boedecker, J., Riedmiller, M. and Obermayer, K. (2015). Autonomous Learning of State Representations for Control: An Emerging Field Aims to Autonomously Learn State Representations for Reinforcement Learning Agents from Their Real-World Sensor Observations. Künstliche Intelligenz. Springer Berlin Heidelberg, 353-362.,10.1007/s13218-015-0356-1


Böhmer, W. and Obermayer, K. (2015). Regression with Linear Factored Functions. Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 119-134.,10.1007/978-3-319-23528-8_8


2014

Mohr, J., Seo, S. and Obermayer, K. (2014). A classifier-based association test for imbalanced data derived from prediction theory. Neural Networks (IJCNN), 2014 International Joint Conference on, 487-493.,10.1109/IJCNN.2014.6889547


Svensson, C.-M., Krusekopf, S., Lücke, J. and Figge, M. T. (2014). Automated Detection of Circulating Tumour Cells With Naive Bayesian Classifiers. Cytometry Part A, 85, 501–511.


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.


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.


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


Dai, Z. and Lücke, J. (2014). Autonomous Document Cleaning – A Generative Approach to Reconstruct Strongly Corrupted Scanned Texts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 1950–1962.


Henniges, M., Turner, R. E., Sahani, M., Eggert, J. and Lücke, J. (2014). Efficient Occlusive Components Analysis. Journal of Machine Learning Research, 15, 2689–2722.


2013

Böhmer, W., Grünewälder, S., Shen, Y., Musial, M. and Obermayer, K. (2013). Construction of Approximation Spaces for Reinforcement Learning. Journal of Machine Learning Research, 14, 2067–2118.


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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