Martius, G. S., &; Olbrich, E. (2015). Quantifying self-organizing behavior of autonomous robots. In Proceedings of the 13th European Conference on Artificial Life (p. 78). York, United Kingdom: MIT Press.https://doi.org/10.7551/978-0-262-33027-5-ch018 

Sharmanska, V. (2015). Learning with attributes for object recognition: Parametric and non-parametrics views. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:1401 

Martius, G. S., &; Olbrich, E. (2015). Quantifying emergent behavior of autonomous robots. Entropy. MDPI.https://doi.org/10.3390/e17107266 

Muelling, K., Kroemer, O., Lampert, C., &; Schölkopf, B. (2014). Movement templates for learning of hitting and batting. In J. Kober &; J. Peters (Eds.), Learning Motor Skills (Vol. 97, pp. 69–82). Springer.https://doi.org/10.1007/978-3-319-03194-1_3 

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

Morvant, E., Habrard, A., &; Ayache, S. (2014). 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, pp. 153–162). Joensuu, Finland: Springer.https://doi.org/10.1007/978-3-662-44415-3_16 

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

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

Sydorov, V., Sakurada, M., &; Lampert, C. (2014). 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 (pp. 1402–1409). Columbus, USA: IEEE.https://doi.org/10.1109/CVPR.2014.182 

Bellet, A., Habrard, A., Morvant, E., &; Sebban, M. (2014). Learning a priori constrained weighted majority votes. Machine Learning. Springer.https://doi.org/10.1007/s10994-014-5462-z 

Morvant, E. (2014). Adaptation de domaine de vote de majorité par auto-étiquetage non itératif (Vol. 1, pp. 49–58). Presented at the CAP: Conférence Francophone sur l’Apprentissage Automatique (Machine Learning French Conference), Saint-Etienne, France: Elsevier.

Pentina, A., &; Lampert, C. (2014). A PAC-Bayesian bound for Lifelong Learning (Vol. 32, pp. 991–999). Presented at the ICML: International Conference on Machine Learning, Beijing, China: ML Research Press.

Kazmar, T., Kvon, E., Stark, A., &; Lampert, C. (2013). Drosophila Embryo Stage Annotation using Label Propagation. Presented at the ICCV: International Conference on Computer Vision, Sydney, Australia: IEEE.https://doi.org/10.1109/ICCV.2013.139 

Sharmanska, V., Quadrianto, N., &; Lampert, C. (2013). Learning to rank using privileged information (pp. 825–832). Presented at the ICCV: International Conference on Computer Vision, Sydney, Australia: IEEE.https://doi.org/10.1109/ICCV.2013.107 

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

Quadrianto, N., Sharmanska, V., Knowles, D., &; Ghahramani, Z. (2013). The supervised IBP: Neighbourhood preserving infinite latent feature models. In Proceedings of the 29th conference uncertainty in Artificial Intelligence (pp. 527–536). Bellevue, WA, United States: AUAI Press.

Chen, C., Kolmogorov, V., Yan, Z., Metaxas, D., &; Lampert, C. (2013). Computing the M most probable modes of a graphical model (Vol. 31, pp. 161–169). Presented at the  AISTATS: Conference on Uncertainty in Artificial Intelligence, Scottsdale, AZ, United States: JMLR.

Tommasi, T., Quadrianto, N., Caputo, B., &; Lampert, C. (2013). Beyond dataset bias: Multi-task unaligned shared knowledge transfer. Presented at the ACCV: Asian Conference on Computer Vision, Daejeon, Korea: Springer.https://doi.org/10.1007/978-3-642-37331-2_1 

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

Lampert, C. (2012). Dynamic pruning of factor graphs for maximum marginal prediction (Vol. 1, pp. 82–90). Presented at the NIPS: Neural Information Processing Systems, Lake Tahoe, NV, United States: Neural Information Processing Systems.

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

Sharmanska, V., Quadrianto, N., &; Lampert, C. (2012). Augmented attribute representations (Vol. 7576, pp. 242–255). Presented at the ECCV: European Conference on Computer Vision, Florence, Italy: Springer.https://doi.org/10.1007/978-3-642-33715-4_18 

Müller, A., Nowozin, S., &; Lampert, C. (2012). Information theoretic clustering using minimal spanning trees (Vol. 7476, pp. 205–215). Presented at the DAGM: German Association For Pattern Recognition, Graz, Austria: Springer.https://doi.org/10.1007/978-3-642-32717-9_21 

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

Korc, F., Kolmogorov, V., &; Lampert, C. (2012). Approximating marginals using discrete energy minimization. Presented at the ICML: International Conference on Machine Learning, Edinburgh, Scotland: ICML.

Korc, F., Kolmogorov, V., &; Lampert, C. (2012). Approximating marginals using discrete energy minimization. IST Austria.https://doi.org/10.15479/AT:IST-2012-0003 

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

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

Wang, Z., Lampert, C., Mülling, K., Schölkopf, B., &; Peters, J. (2011). Learning anticipation policies for robot table tennis (pp. 332–337). Presented at the IROS: RSJ International Conference on Intelligent Robots and Systems, San Francisco, USA: IEEE.https://doi.org/10.1109/IROS.2011.6094892 

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

Kroemer, O., Lampert, C., &; Peters, J. (2011). Learning dynamic tactile sensing with robust vision based training. IEEE Transactions on Robotics. IEEE.https://doi.org/10.1109/TRO.2011.2121130 

Chen, C., Freedman, D., &; Lampert, C. (2011). Enforcing topological constraints in random field image segmentation. IST Austria.https://doi.org/10.15479/AT:IST-2011-0002 

Chen, C., Freedman, D., &; Lampert, C. (2011). Enforcing topological constraints in random field image segmentation. In CVPR: Computer Vision and Pattern Recognition (pp. 2089–2096). Colorado Springs, CO, United States: IEEE.https://doi.org/10.1109/CVPR.2011.5995503 

Lampert, C. (2011). Maximum margin multi-label structured prediction. Presented at the NIPS: Neural Information Processing Systems, Granada, Spain: Neural Information Processing Systems.

Lampert, C. (2011). Maximum margin multi label structured prediction. NIPS: Neural Information Processing Systems. Neural Information Processing Systems Foundation.

Nowozin, S., &; Lampert, C. (2011). Structured learning and prediction in computer vision. Foundations and Trends in Computer Graphics and Vision. Now Publishers.https://doi.org/10.1561/0600000033 

Quadrianto, N., &; Lampert, C. (2011). Learning multi-view neighborhood preserving projections (pp. 425–432). Presented at the ICML: International Conference on Machine Learning, Bellevue, United States: ML Research Press.

Lampert, C., &; Krömer, O. (2010). Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning (Vol. 6312, pp. 566–579). Presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece: Springer.https://doi.org/10.1007/978-3-642-15552-9_41 

Nowozin, S., Gehler, P., &; Lampert, C. (2010). On parameter learning in CRF-based approaches to object class image segmentation (Vol. 6316, pp. 98–111). Presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece: Springer.https://doi.org/10.1007/978-3-642-15567-3_8