Martius GS, Olbrich E. Quantifying self-organizing behavior of autonomous robots. In: Proceedings of the 13th European Conference on Artificial Life. MIT Press; 2015:78. doi:10.7551/978-0-262-33027-5-ch018

Sharmanska V. Learning with attributes for object recognition: Parametric and non-parametrics views. 2015. doi:10.15479/at:ista:1401

Martius GS, Olbrich E. Quantifying emergent behavior of autonomous robots. Entropy. 2015;17(10):7266-7297. doi:10.3390/e17107266

Muelling K, Kroemer O, Lampert C, Schölkopf B. Movement templates for learning of hitting and batting. In: Kober J, Peters J, eds. Learning Motor Skills. Vol 97. From Algorithms to Robot Experiments. Springer; 2014:69-82. doi:10.1007/978-3-319-03194-1_3

Hernandez Lobato D, Sharmanska V, Kersting K, Lampert C, Quadrianto N. Mind the nuisance: Gaussian process classification using privileged noise. In: Advances in Neural Information Processing Systems. Vol 1. Neural Information Processing Systems; 2014:837-845.

Morvant E, Habrard A, Ayache S. Majority vote of diverse classifiers for late fusion. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol 8621. Springer; 2014:153-162. doi:10.1007/978-3-662-44415-3_16

Kolesnikov A, Guillaumin M, Ferrari V, Lampert C. Closed-form approximate CRF training for scalable image segmentation. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T, eds. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol 8691. Springer; 2014:550-565. doi:10.1007/978-3-319-10578-9_36

Khamis S, Lampert C. CoConut: Co-classification with output space regularization. In: Proceedings of the British Machine Vision Conference 2014. BMVA Press; 2014.

Sydorov V, Sakurada M, Lampert C. Deep Fisher Kernels – End to end learning of the Fisher Kernel GMM parameters. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE; 2014:1402-1409. doi:10.1109/CVPR.2014.182

Bellet A, Habrard A, Morvant E, Sebban M. Learning a priori constrained weighted majority votes. Machine Learning. 2014;97(1-2):129-154. doi:10.1007/s10994-014-5462-z

Morvant E. Adaptation de domaine de vote de majorité par auto-étiquetage non itératif. In: Vol 1. Elsevier; 2014:49-58.

Pentina A, Lampert C. A PAC-Bayesian bound for Lifelong Learning. In: Vol 32. ML Research Press; 2014:991-999.

Kazmar T, Kvon E, Stark A, Lampert C. Drosophila Embryo Stage Annotation using Label Propagation. In: IEEE; 2013. doi:10.1109/ICCV.2013.139

Sharmanska V, Quadrianto N, Lampert C. Learning to rank using privileged information. In: IEEE; 2013:825-832. doi:10.1109/ICCV.2013.107

Lampert C, Nickisch H, Harmeling S. Attribute-based classification for zero-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2013;36(3):453-465. doi:10.1109/TPAMI.2013.140

Quadrianto N, Sharmanska V, Knowles D, Ghahramani Z. The supervised IBP: Neighbourhood preserving infinite latent feature models. In: Proceedings of the 29th Conference Uncertainty in Artificial Intelligence. AUAI Press; 2013:527-536.

Chen C, Kolmogorov V, Yan Z, Metaxas D, Lampert C. Computing the M most probable modes of a graphical model. In: Vol 31. JMLR; 2013:161-169.

Tommasi T, Quadrianto N, Caputo B, Lampert C. Beyond dataset bias: Multi-task unaligned shared knowledge transfer. 2013;7724:1-15. doi:10.1007/978-3-642-37331-2_1

Quadrianto N, Lampert C. Kernel based learning. In: Dubitzky W, Wolkenhauer O, Cho K, Yokota H, eds. Encyclopedia of Systems Biology. Vol 3. Springer; 2013:1069-1069. doi:10.1007/978-1-4419-9863-7_604

Lampert C. Dynamic pruning of factor graphs for maximum marginal prediction. In: Vol 1. Neural Information Processing Systems; 2012:82-90.

Blaschko M, Lampert C. Guest editorial: Special issue on structured prediction and inference. International Journal of Computer Vision. 2012;99(3):257-258. doi:10.1007/s11263-012-0530-y

Sharmanska V, Quadrianto N, Lampert C. Augmented attribute representations. In: Vol 7576. Springer; 2012:242-255. doi:10.1007/978-3-642-33715-4_18

Müller A, Nowozin S, Lampert C. Information theoretic clustering using minimal spanning trees. In: Vol 7476. Springer; 2012:205-215. doi:10.1007/978-3-642-32717-9_21

Lampert C, Peters J. Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components. Journal of Real-Time Image Processing. 2012;7(1):31-41. doi:10.1007/s11554-010-0168-3

Korc F, Kolmogorov V, Lampert C. Approximating marginals using discrete energy minimization. In: ICML; 2012.

Korc F, Kolmogorov V, Lampert C. Approximating Marginals Using Discrete Energy Minimization. IST Austria; 2012. doi:10.15479/AT:IST-2012-0003

Kroemer O, Lampert C, Peters J. Multi-modal learning for dynamic tactile sensing. In: Deutsches Zentrum für Luft und Raumfahrt; 2012.

Quadrianto N, Lampert C, Chen C. The most persistent soft-clique in a set of sampled graphs. In: Proceedings of the 29th International Conference on Machine Learning. ML Research Press; 2012:211-218.

Wang Z, Lampert C, Mülling K, Schölkopf B, Peters J. Learning anticipation policies for robot table tennis. In: IEEE; 2011:332-337. doi:10.1109/IROS.2011.6094892

Blaschko M, Shelton J, Bartels A, Lampert C, Gretton A. Semi supervised kernel canonical correlation analysis with application to human fMRI. Pattern Recognition Letters. 2011;32(11):1572-1583. doi:10.1016/j.patrec.2011.02.011

Kroemer O, Lampert C, Peters J. Learning dynamic tactile sensing with robust vision based training. IEEE Transactions on Robotics. 2011;27(3):545-557. doi:10.1109/TRO.2011.2121130

Chen C, Freedman D, Lampert C. Enforcing Topological Constraints in Random Field Image Segmentation. IST Austria; 2011. doi:10.15479/AT:IST-2011-0002

Chen C, Freedman D, Lampert C. Enforcing topological constraints in random field image segmentation. In: CVPR: Computer Vision and Pattern Recognition. IEEE; 2011:2089-2096. doi:10.1109/CVPR.2011.5995503

Lampert C. Maximum margin multi-label structured prediction. In: Neural Information Processing Systems; 2011.

Lampert C. Maximum Margin Multi Label Structured Prediction. Neural Information Processing Systems Foundation; 2011.

Nowozin S, Lampert C. Structured learning and prediction in computer vision. Foundations and Trends in Computer Graphics and Vision. 2011;6(3-4):185-365. doi:10.1561/0600000033

Quadrianto N, Lampert C. Learning multi-view neighborhood preserving projections. In: ML Research Press; 2011:425-432.

Lampert C, Krömer O. Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning. In: Vol 6312. Springer; 2010:566-579. doi:10.1007/978-3-642-15552-9_41

Nowozin S, Gehler P, Lampert C. On parameter learning in CRF-based approaches to object class image segmentation. In: Vol 6316. Springer; 2010:98-111. doi:10.1007/978-3-642-15567-3_8