Martius, G. S., Hostettler, R., Knoll, A., &; Der, R. (2016). Self-organized control of an tendon driven arm by differential extrinsic plasticity. In Proceedings of the Artificial Life Conference 2016 (Vol. 28, pp. 142–143). Cancun, Mexico: MIT Press.https://doi.org/10.7551/978-0-262-33936-0-ch029 
LibreCat: https://research-explorer.ista.ac.at/record/8094
DOI: 10.7551/978-0-262-33936-0-ch029

Pentina, A. (2016). Theoretical foundations of multi-task lifelong learning. Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:TH_776 
LibreCat: https://research-explorer.ista.ac.at/record/1126
DOI: 10.15479/AT:ISTA:TH_776

Pentina, A., &; Lampert, C. (2015). Lifelong learning with non-i.i.d. tasks (Vol. 2015, pp. 1540–1548). Presented at the NIPS: Neural Information Processing Systems, Montreal, Canada: Neural Information Processing Systems.
LibreCat: https://research-explorer.ista.ac.at/record/1425

Xia, W., Domokos, C., Xiong, J., Cheong, L., &; Yan, S. (2015). Segmentation over detection via optimal sparse reconstructions. IEEE Transactions on Circuits and Systems for Video Technology. IEEE.https://doi.org/10.1109/TCSVT.2014.2379972 
LibreCat: https://research-explorer.ista.ac.at/record/1533
DOI: 10.1109/TCSVT.2014.2379972

Der, R., &; Martius, G. S. (2015). Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. National Academy of Sciences.https://doi.org/10.1073/pnas.1508400112 
LibreCat: https://research-explorer.ista.ac.at/record/1570
DOI: 10.1073/pnas.1508400112
PMID: 26504200

Pentina, A., &; Ben David, S. (2015). Multi-task and lifelong learning of kernels (Vol. 9355, pp. 194–208). Presented at the ALT: Algorithmic Learning Theory, Banff, AB, Canada: Springer.https://doi.org/10.1007/978-3-319-24486-0_13 
LibreCat: https://research-explorer.ista.ac.at/record/1706
DOI: 10.1007/978-3-319-24486-0_13

Shah, N., Kolmogorov, V., &; Lampert, C. (2015). A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle (pp. 2737–2745). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, USA: IEEE.https://doi.org/10.1109/CVPR.2015.7298890 
LibreCat: https://research-explorer.ista.ac.at/record/1859
DOI: 10.1109/CVPR.2015.7298890

Royer, A., &; Lampert, C. (2015). Classifier adaptation at prediction time (pp. 1401–1409). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States: IEEE.https://doi.org/10.1109/CVPR.2015.7298746 
LibreCat: https://research-explorer.ista.ac.at/record/1860
DOI: 10.1109/CVPR.2015.7298746

Lampert, C. (2015). Predicting the future behavior of a time-varying probability distribution (pp. 942–950). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States: IEEE.https://doi.org/10.1109/CVPR.2015.7298696 
LibreCat: https://research-explorer.ista.ac.at/record/1858
DOI: 10.1109/CVPR.2015.7298696
arXiv: 1406.5362

Pentina, A., Sharmanska, V., &; Lampert, C. (2015). Curriculum learning of multiple tasks (pp. 5492–5500). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States: IEEE.https://doi.org/10.1109/CVPR.2015.7299188 
LibreCat: https://research-explorer.ista.ac.at/record/1857
DOI: 10.1109/CVPR.2015.7299188

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 
LibreCat: https://research-explorer.ista.ac.at/record/12881
DOI: 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 
LibreCat: https://research-explorer.ista.ac.at/record/1401
DOI: 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 
LibreCat: https://research-explorer.ista.ac.at/record/1655
DOI: 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 
LibreCat: https://research-explorer.ista.ac.at/record/1829
DOI: 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.
LibreCat: https://research-explorer.ista.ac.at/record/2033

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 
LibreCat: https://research-explorer.ista.ac.at/record/2057
DOI: 10.1007/978-3-662-44415-3_16
arXiv: 1404.7796

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 
LibreCat: https://research-explorer.ista.ac.at/record/2171
DOI: 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.
LibreCat: https://research-explorer.ista.ac.at/record/2173

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 
LibreCat: https://research-explorer.ista.ac.at/record/2172
DOI: 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 
LibreCat: https://research-explorer.ista.ac.at/record/2180
DOI: 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.
LibreCat: https://research-explorer.ista.ac.at/record/2189

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.
LibreCat: https://research-explorer.ista.ac.at/record/2160

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 
LibreCat: https://research-explorer.ista.ac.at/record/2294
DOI: 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 
LibreCat: https://research-explorer.ista.ac.at/record/2293
DOI: 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 
LibreCat: https://research-explorer.ista.ac.at/record/2516
DOI: 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.
LibreCat: https://research-explorer.ista.ac.at/record/2520

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.
LibreCat: https://research-explorer.ista.ac.at/record/2901

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 
LibreCat: https://research-explorer.ista.ac.at/record/2948
DOI: 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 
LibreCat: https://research-explorer.ista.ac.at/record/3321
DOI: 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.
LibreCat: https://research-explorer.ista.ac.at/record/2825

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 
LibreCat: https://research-explorer.ista.ac.at/record/3164
DOI: 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 
LibreCat: https://research-explorer.ista.ac.at/record/3125
DOI: 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 
LibreCat: https://research-explorer.ista.ac.at/record/3126
DOI: 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 
LibreCat: https://research-explorer.ista.ac.at/record/3248
DOI: 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.
LibreCat: https://research-explorer.ista.ac.at/record/3124

Korc, F., Kolmogorov, V., &; Lampert, C. (2012). Approximating marginals using discrete energy minimization. IST Austria.https://doi.org/10.15479/AT:IST-2012-0003 
LibreCat: https://research-explorer.ista.ac.at/record/5396
DOI: 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.
LibreCat: https://research-explorer.ista.ac.at/record/2915

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.
LibreCat: https://research-explorer.ista.ac.at/record/3127

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 
LibreCat: https://research-explorer.ista.ac.at/record/3337
DOI: 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 
LibreCat: https://research-explorer.ista.ac.at/record/3389
DOI: 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 
LibreCat: https://research-explorer.ista.ac.at/record/3382
DOI: 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 
LibreCat: https://research-explorer.ista.ac.at/record/5386
DOI: 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 
LibreCat: https://research-explorer.ista.ac.at/record/3336
DOI: 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.
LibreCat: https://research-explorer.ista.ac.at/record/3163

Lampert, C. (2011). Maximum margin multi label structured prediction. NIPS: Neural Information Processing Systems. Neural Information Processing Systems Foundation.
LibreCat: https://research-explorer.ista.ac.at/record/3322

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 
LibreCat: https://research-explorer.ista.ac.at/record/3320
DOI: 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.
LibreCat: https://research-explorer.ista.ac.at/record/3319

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 
LibreCat: https://research-explorer.ista.ac.at/record/3794
DOI: 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 
LibreCat: https://research-explorer.ista.ac.at/record/3793
DOI: 10.1007/978-3-642-15567-3_8