Fairness-aware PAC learning from corrupted data
N.H. Konstantinov, C. Lampert, Journal of Machine Learning Research 23 (2022) 1–60.

On the impossibility of fairness-aware learning from corrupted data
N.H. Konstantinov, C. Lampert, in:, Proceedings of Machine Learning Research, ML Research Press, 2022, pp. 59–83.

Robustness and fairness in machine learning
N.H. Konstantinov, Robustness and Fairness in Machine Learning, Institute of Science and Technology Austria, 2022.

Does SGD implicitly optimize for smoothness?
V. Volhejn, C. Lampert, in:, 42nd German Conference on Pattern Recognition, Springer, 2021, pp. 246–259.

The inductive bias of ReLU networks on orthogonally separable data
M. Phuong, C. Lampert, in:, 9th International Conference on Learning Representations, 2021.

Fairness through regularization for learning to rank
N.H. Konstantinov, C. Lampert, ArXiv (n.d.).

Underspecification in deep learning
M. Phuong, Underspecification in Deep Learning, Institute of Science and Technology Austria, 2021.

Zero-Shot Learning
C. Lampert, in:, K. Ikeuchi (Ed.), Computer Vision, 2nd ed., Springer, Cham, 2021, pp. 1395–1397.

Object-centric image generation with factored depths, locations, and appearances
T. Anciukevicius, C. Lampert, P.M. Henderson, ArXiv (n.d.).

Unsupervised object-centric video generation and decomposition in 3D
P.M. Henderson, C. Lampert, in:, 34th Conference on Neural Information Processing Systems, Curran Associates, 2020, pp. 3106–3117.

Learning single-image 3D reconstruction by generative modelling of shape, pose and shading
P.M. Henderson, V. Ferrari, International Journal of Computer Vision 128 (2020) 835–854.

Localizing grouped instances for efficient detection in low-resource scenarios
A. Royer, C. Lampert, in:, IEEE Winter Conference on Applications of Computer Vision, IEEE, 2020.

A flexible selection scheme for minimum-effort transfer learning
A. Royer, C. Lampert, in:, 2020 IEEE Winter Conference on Applications of Computer Vision, IEEE, 2020.

XGAN: Unsupervised image-to-image translation for many-to-many mappings
A. Royer, K. Bousmalis, S. Gouws, F. Bertsch, I. Mosseri, F. Cole, K. Murphy, in:, R. Singh, M. Vatsa, V.M. Patel, N. Ratha (Eds.), Domain Adaptation for Visual Understanding, Springer Nature, 2020, pp. 33–49.

Functional vs. parametric equivalence of ReLU networks
M. Phuong, C. Lampert, in:, 8th International Conference on Learning Representations, 2020.

On the sample complexity of adversarial multi-source PAC learning
N.H. Konstantinov, E. Frantar, D.-A. Alistarh, C. Lampert, in:, Proceedings of the 37th International Conference on Machine Learning, ML Research Press, 2020, pp. 5416–5425.

Leveraging structure in Computer Vision tasks for flexible Deep Learning models
A. Royer, Leveraging Structure in Computer Vision Tasks for Flexible Deep Learning Models, Institute of Science and Technology Austria, 2020.

Leveraging 2D data to learn textured 3D mesh generation
P.M. Henderson, V. Tsiminaki, C. Lampert, in:, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2020, pp. 7498–7507.

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.

Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt
K. Kersting, C. Lampert, C. Rothkopf, eds., Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt, 1st ed., Springer Nature, Wiesbaden, 2019.