Martius, Georg S, and Eckehard Olbrich. “Quantifying Self-Organizing Behavior of Autonomous Robots.”https://doi.org/10.7551/978-0-262-33027-5-ch018__In Proceedings of the 13th European Conference on Artificial Life, 78. MIT Press, 2015.https://doi.org/10.7551/978-0-262-33027-5-ch018 .

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

Martius, Georg S, and Eckehard Olbrich. “Quantifying Emergent Behavior of Autonomous Robots.”https://doi.org/10.3390/e17107266__Entropy. MDPI, 2015.https://doi.org/10.3390/e17107266 .

Muelling, Katharina, Oliver Kroemer, Christoph Lampert, and Bernhard Schölkopf. “Movement Templates for Learning of Hitting and Batting.”https://doi.org/10.1007/978-3-319-03194-1_3__In Learning Motor Skills, edited by Jens Kober and Jan Peters, 97:69–82. From Algorithms to Robot Experiments. Springer, 2014.https://doi.org/10.1007/978-3-319-03194-1_3 .

Hernandez Lobato, Daniel, Viktoriia Sharmanska, Kristian Kersting, Christoph Lampert, and Novi Quadrianto. “Mind the Nuisance: Gaussian Process Classification Using Privileged Noise.” In Advances in Neural Information Processing Systems, 1:837–45. Neural Information Processing Systems, 2014.

Morvant, Emilie, Amaury Habrard, and Stéphane Ayache. “Majority Vote of Diverse Classifiers for Late Fusion.”https://doi.org/10.1007/978-3-662-44415-3_16__In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8621:153–62. Springer, 2014.https://doi.org/10.1007/978-3-662-44415-3_16 .

Kolesnikov, Alexander, Matthieu Guillaumin, Vittorio Ferrari, and Christoph Lampert. “Closed-Form Approximate CRF Training for Scalable Image Segmentation.”https://doi.org/10.1007/978-3-319-10578-9_36__In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), edited by David Fleet, Tomas Pajdla, Bernt Schiele, and Tinne Tuytelaars, 8691:550–65. Springer, 2014.https://doi.org/10.1007/978-3-319-10578-9_36 .

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

Sydorov, Vladyslav, Mayu Sakurada, and Christoph Lampert. “Deep Fisher Kernels – End to End Learning of the Fisher Kernel GMM Parameters.”https://doi.org/10.1109/CVPR.2014.182__In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1402–9. IEEE, 2014.https://doi.org/10.1109/CVPR.2014.182 .

Bellet, Aurélien, Amaury Habrard, Emilie Morvant, and Marc Sebban. “Learning a Priori Constrained Weighted Majority Votes.”https://doi.org/10.1007/s10994-014-5462-z__Machine Learning. Springer, 2014.https://doi.org/10.1007/s10994-014-5462-z .

Morvant, Emilie. “Adaptation de Domaine de Vote de Majorité Par Auto-Étiquetage Non Itératif,” 1:49–58. Elsevier, 2014.

Pentina, Anastasia, and Christoph Lampert. “A PAC-Bayesian Bound for Lifelong Learning,” 32:991–99. ML Research Press, 2014.

Kazmar, Tomas, Evgeny Kvon, Alexander Stark, and Christoph Lampert. “Drosophila Embryo Stage Annotation Using Label Propagation.”https://doi.org/10.1109/ICCV.2013.139__IEEE, 2013.https://doi.org/10.1109/ICCV.2013.139 .

Sharmanska, Viktoriia, Novi Quadrianto, and Christoph Lampert. “Learning to Rank Using Privileged Information,”https://doi.org/10.1109/ICCV.2013.107__825–32. IEEE, 2013.https://doi.org/10.1109/ICCV.2013.107 .

Lampert, Christoph, Hannes Nickisch, and Stefan Harmeling. “Attribute-Based Classification for Zero-Shot Learning of Object Categories.”https://doi.org/10.1109/TPAMI.2013.140__IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE, 2013.https://doi.org/10.1109/TPAMI.2013.140 .

Quadrianto, Novi, Viktoriia Sharmanska, David Knowles, and Zoubin Ghahramani. “The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models.” In Proceedings of the 29th Conference Uncertainty in Artificial Intelligence, 527–36. AUAI Press, 2013.

Chen, Chao, Vladimir Kolmogorov, Zhu Yan, Dimitris Metaxas, and Christoph Lampert. “Computing the M Most Probable Modes of a Graphical Model,” 31:161–69. JMLR, 2013.

Tommasi, Tatiana, Novi Quadrianto, Barbara Caputo, and Christoph Lampert. “Beyond Dataset Bias: Multi-Task Unaligned Shared Knowledge Transfer.”https://doi.org/10.1007/978-3-642-37331-2_1__Lecture Notes in Computer Science. Springer, 2013.https://doi.org/10.1007/978-3-642-37331-2_1 .

Quadrianto, Novi, and Christoph Lampert. “Kernel Based Learning.”https://doi.org/10.1007/978-1-4419-9863-7_604__In Encyclopedia of Systems Biology, edited by Werner Dubitzky, Olaf Wolkenhauer, Kwang Cho, and Hiroki Yokota, 3:1069–1069. Springer, 2013.https://doi.org/10.1007/978-1-4419-9863-7_604 .

Lampert, Christoph. “Dynamic Pruning of Factor Graphs for Maximum Marginal Prediction,” 1:82–90. Neural Information Processing Systems, 2012.