KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications
R. Sun, C. Lampert, International Journal of Computer Vision 128 (2020) 970–995.

Detecting visual relationships using box attention
A. Kolesnikov, A. Kuznetsova, C. Lampert, V. Ferrari, in:, Proceedings of the 2019 International Conference on Computer Vision Workshop, IEEE, 2019.

KS(conf): A light-weight test if a ConvNet operates outside of Its specifications
R. Sun, C. Lampert, in:, Springer Nature, 2019, pp. 244–259.

Robust learning from untrusted sources
N.H. Konstantinov, C. Lampert, in:, Proceedings of the 36th International Conference on Machine Learning, ML Research Press, 2019, pp. 3488–3498.

Distillation-based training for multi-exit architectures
M. Phuong, C. Lampert, in:, IEEE International Conference on Computer Vision, IEEE, 2019, pp. 1355–1364.

Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images
A. Kolesnikov, Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images, Institute of Science and Technology Austria, 2018.

Learning from dependent data
A. Zimin, Learning from Dependent Data, Institute of Science and Technology Austria, 2018.

Data-dependent stability of stochastic gradient descent
I. Kuzborskij, C. Lampert, in:, Proceedings of the 35 Th International Conference on Machine Learning, ML Research Press, 2018, pp. 2815–2824.

Probabilistic image colorization
A. Royer, A. Kolesnikov, C. Lampert, in:, BMVA Press, 2017, p. 85.1-85.12.

PixelCNN models with auxiliary variables for natural image modeling
A. Kolesnikov, C. Lampert, in:, 34th International Conference on Machine Learning, JMLR, 2017, pp. 1905–1914.

Learning theory for conditional risk minimization
A. Zimin, C. Lampert, in:, ML Research Press, 2017, pp. 213–222.

Extrapolation and learning equations
G.S. Martius, C. Lampert, in:, 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings, International Conference on Learning Representations, 2017.

Multi-task learning with labeled and unlabeled tasks
A. Pentina, C. Lampert, in:, ML Research Press, 2017, pp. 2807–2816.

iCaRL: Incremental classifier and representation learning
S.A. Rebuffi, A. Kolesnikov, G. Sperl, C. Lampert, in:, IEEE, 2017, pp. 5533–5542.

Theoretical foundations of multi-task lifelong learning
A. Pentina, Theoretical Foundations of Multi-Task Lifelong Learning, Institute of Science and Technology Austria, 2016.

Improving weakly-supervised object localization by micro-annotation
A. Kolesnikov, C. Lampert, in:, Proceedings of the British Machine Vision Conference 2016, BMVA Press, 2016, p. 92.1-92.12.

Lifelong learning with weighted majority votes
A. Pentina, R. Urner, in:, Neural Information Processing Systems Foundation, 2016, pp. 3619–3627.

Seed, expand and constrain: Three principles for weakly-supervised image segmentation
A. Kolesnikov, C. Lampert, in:, Springer, 2016, pp. 695–711.

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

Lifelong learning with non-i.i.d. tasks
A. Pentina, C. Lampert, in:, Neural Information Processing Systems Foundation, 2015, pp. 1540–1548.