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Pentina, Anastasia. Theoretical Foundations of Multi-Task Lifelong Learning. Institute of Science and Technology Austria, 2016, doi:10.15479/AT:ISTA:TH_776.

Pentina, Anastasia, and Christoph Lampert. Lifelong Learning with Non-i.i.d. Tasks. Vol. 2015, Neural Information Processing Systems, 2015, pp. 1540–48.

Xia, Wei, et al. “Segmentation over Detection via Optimal Sparse Reconstructions.”10.1109/TCSVT.2014.2379972__IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 8, IEEE, 2015, pp. 1295–308, doi:10.1109/TCSVT.2014.2379972.

Der, Ralf, and Georg S. Martius. “Novel Plasticity Rule Can Explain the Development of Sensorimotor Intelligence.”10.1073/pnas.1508400112__PNAS, vol. 112, no. 45, National Academy of Sciences, 2015, pp. 24–32, doi:10.1073/pnas.1508400112.

Pentina, Anastasia, and Shai Ben David. Multi-Task and Lifelong Learning of Kernels. Vol. 9355, Springer, 2015, pp. 194–208, doi:10.1007/978-3-319-24486-0_13.

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Royer, Amélie, and Christoph Lampert. Classifier Adaptation at Prediction Time. IEEE, 2015, pp. 1401–09, doi:10.1109/CVPR.2015.7298746.

Lampert, Christoph. Predicting the Future Behavior of a Time-Varying Probability Distribution. IEEE, 2015, pp. 942–50, doi:10.1109/CVPR.2015.7298696.

Pentina, Anastasia, et al. Curriculum Learning of Multiple Tasks. IEEE, 2015, pp. 5492–500, doi:10.1109/CVPR.2015.7299188.

Martius, Georg S., and Eckehard Olbrich. “Quantifying Self-Organizing Behavior of Autonomous Robots.”10.7551/978-0-262-33027-5-ch018__Proceedings of the 13th European Conference on Artificial Life, MIT Press, 2015, p. 78, doi:10.7551/978-0-262-33027-5-ch018.

Sharmanska, Viktoriia. Learning with Attributes for Object Recognition: Parametric and Non-Parametrics Views. Institute of Science and Technology Austria, 2015, doi:10.15479/at:ista:1401.

Martius, Georg S., and Eckehard Olbrich. “Quantifying Emergent Behavior of Autonomous Robots.”10.3390/e17107266__Entropy, vol. 17, no. 10, MDPI, 2015, pp. 7266–97, doi:10.3390/e17107266.

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Kolesnikov, Alexander, et al. “Closed-Form Approximate CRF Training for Scalable Image Segmentation.”10.1007/978-3-319-10578-9_36__Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), edited by David Fleet et al., vol. 8691, no. PART 3, Springer, 2014, pp. 550–65, doi:10.1007/978-3-319-10578-9_36.

Khamis, Sameh, and Christoph Lampert. “CoConut: Co-Classification with Output Space Regularization.” Proceedings of the British Machine Vision Conference 2014, BMVA Press, 2014.

Sydorov, Vladyslav, et al. “Deep Fisher Kernels – End to End Learning of the Fisher Kernel GMM Parameters.”10.1109/CVPR.2014.182__Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2014, pp. 1402–09, doi:10.1109/CVPR.2014.182.