Closed-form approximate CRF training for scalable image segmentation
A. Kolesnikov, M. Guillaumin, V. Ferrari, C. Lampert, 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), Springer, 2014, pp. 550–565.

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

Deep Fisher Kernels – End to end learning of the Fisher Kernel GMM parameters
V. Sydorov, M. Sakurada, C. Lampert, in:, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2014, pp. 1402–1409.

Learning a priori constrained weighted majority votes
A. Bellet, A. Habrard, E. Morvant, M. Sebban, Machine Learning 97 (2014) 129–154.

Adaptation de domaine de vote de majorité par auto-étiquetage non itératif
E. Morvant, in:, Elsevier, 2014, pp. 49–58.

A PAC-Bayesian bound for Lifelong Learning
A. Pentina, C. Lampert, in:, ML Research Press, 2014, pp. 991–999.

Drosophila Embryo Stage Annotation using Label Propagation
T. Kazmar, E. Kvon, A. Stark, C. Lampert, in:, IEEE, 2013.

Learning to rank using privileged information
V. Sharmanska, N. Quadrianto, C. Lampert, in:, IEEE, 2013, pp. 825–832.

Attribute-based classification for zero-shot learning of object categories
C. Lampert, H. Nickisch, S. Harmeling, IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (2013) 453–465.

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

Computing the M most probable modes of a graphical model
C. Chen, V. Kolmogorov, Z. Yan, D. Metaxas, C. Lampert, in:, JMLR, 2013, pp. 161–169.

Beyond dataset bias: Multi-task unaligned shared knowledge transfer
T. Tommasi, N. Quadrianto, B. Caputo, C. Lampert, 7724 (2013) 1–15.

Kernel based learning
N. Quadrianto, C. Lampert, in:, W. Dubitzky, O. Wolkenhauer, K. Cho, H. Yokota (Eds.), Encyclopedia of Systems Biology, Springer, 2013, pp. 1069–1069.

Dynamic pruning of factor graphs for maximum marginal prediction
C. Lampert, in:, Neural Information Processing Systems, 2012, pp. 82–90.

Guest editorial: Special issue on structured prediction and inference
M. Blaschko, C. Lampert, International Journal of Computer Vision 99 (2012) 257–258.

Augmented attribute representations
V. Sharmanska, N. Quadrianto, C. Lampert, in:, Springer, 2012, pp. 242–255.

Information theoretic clustering using minimal spanning trees
A. Müller, S. Nowozin, C. Lampert, in:, Springer, 2012, pp. 205–215.

Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components
C. Lampert, J. Peters, Journal of Real-Time Image Processing 7 (2012) 31–41.

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

Approximating marginals using discrete energy minimization
F. Korc, V. Kolmogorov, C. Lampert, Approximating Marginals Using Discrete Energy Minimization, IST Austria, 2012.

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

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

Learning anticipation policies for robot table tennis
Z. Wang, C. Lampert, K. Mülling, B. Schölkopf, J. Peters, in:, IEEE, 2011, pp. 332–337.

Semi supervised kernel canonical correlation analysis with application to human fMRI
M. Blaschko, J. Shelton, A. Bartels, C. Lampert, A. Gretton, Pattern Recognition Letters 32 (2011) 1572–1583.

Learning dynamic tactile sensing with robust vision based training
O. Kroemer, C. Lampert, J. Peters, IEEE Transactions on Robotics 27 (2011) 545–557.

Enforcing topological constraints in random field image segmentation
C. Chen, D. Freedman, C. Lampert, Enforcing Topological Constraints in Random Field Image Segmentation, IST Austria, 2011.

Enforcing topological constraints in random field image segmentation
C. Chen, D. Freedman, C. Lampert, in:, CVPR: Computer Vision and Pattern Recognition, IEEE, 2011, pp. 2089–2096.

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

Maximum margin multi label structured prediction
C. Lampert, Maximum Margin Multi Label Structured Prediction, Neural Information Processing Systems Foundation, 2011.

Structured learning and prediction in computer vision
S. Nowozin, C. Lampert, Foundations and Trends in Computer Graphics and Vision 6 (2011) 185–365.

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

Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning
C. Lampert, O. Krömer, in:, Springer, 2010, pp. 566–579.

On parameter learning in CRF-based approaches to object class image segmentation
S. Nowozin, P. Gehler, C. Lampert, in:, Springer, 2010, pp. 98–111.