Novel plasticity rule can explain the development of sensorimotor intelligence
R. Der, G.S. Martius, PNAS 112 (2015) 24–32.

Multi-task and lifelong learning of kernels
A. Pentina, S. Ben David, in:, Springer, 2015, pp. 194–208.

A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle
N. Shah, V. Kolmogorov, C. Lampert, in:, IEEE, 2015, pp. 2737–2745.

Classifier adaptation at prediction time
A. Royer, C. Lampert, in:, IEEE, 2015, pp. 1401–1409.

Predicting the future behavior of a time-varying probability distribution
C. Lampert, in:, IEEE, 2015, pp. 942–950.

Curriculum learning of multiple tasks
A. Pentina, V. Sharmanska, C. Lampert, in:, IEEE, 2015, pp. 5492–5500.

Quantifying self-organizing behavior of autonomous robots
G.S. Martius, E. Olbrich, in:, Proceedings of the 13th European Conference on Artificial Life, MIT Press, 2015, p. 78.

Learning with attributes for object recognition: Parametric and non-parametrics views
V. Sharmanska, Learning with Attributes for Object Recognition: Parametric and Non-Parametrics Views, Institute of Science and Technology Austria, 2015.

Quantifying emergent behavior of autonomous robots
G.S. Martius, E. Olbrich, Entropy 17 (2015) 7266–7297.

Movement templates for learning of hitting and batting
K. Muelling, O. Kroemer, C. Lampert, B. Schölkopf, in:, J. Kober, J. Peters (Eds.), Learning Motor Skills, Springer, 2014, pp. 69–82.

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

Majority vote of diverse classifiers for late fusion
E. Morvant, A. Habrard, S. Ayache, in:, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2014, pp. 153–162.

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.