N. H. Konstantinov and C. Lampert, “Fairness-aware PAC learning from corrupted data,” Journal of Machine Learning Research, vol. 23. ML Research Press, pp. 1–60, 2022.

N. H. Konstantinov and C. Lampert, “On the impossibility of fairness-aware learning from corrupted data,” in Proceedings of Machine Learning Research, 2022, vol. 171, pp. 59–83.

N. H. Konstantinov, “Robustness and fairness in machine learning,” Institute of Science and Technology Austria, 2022.

V. Volhejn and C. Lampert, “Does SGD implicitly optimize for smoothness?,” in 42nd German Conference on Pattern Recognition, Tübingen, Germany, 2021, vol. 12544, pp. 246–259.

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

N. H. Konstantinov and C. Lampert, “Fairness through regularization for learning to rank,” arXiv. .

M. Phuong, “Underspecification in deep learning,” Institute of Science and Technology Austria, 2021.

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

T. Anciukevicius, C. Lampert, and P. M. Henderson, “Object-centric image generation with factored depths, locations, and appearances,” arXiv. .

P. M. Henderson and C. Lampert, “Unsupervised object-centric video generation and decomposition in 3D,” in 34th Conference on Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 3106–3117.

P. M. Henderson and V. Ferrari, “Learning single-image 3D reconstruction by generative modelling of shape, pose and shading,” International Journal of Computer Vision, vol. 128. Springer Nature, pp. 835–854, 2020.

A. Royer and C. Lampert, “Localizing grouped instances for efficient detection in low-resource scenarios,” in IEEE Winter Conference on Applications of Computer Vision,  Snowmass Village, CO, United States, 2020.

A. Royer and C. Lampert, “A flexible selection scheme for minimum-effort transfer learning,” in 2020 IEEE Winter Conference on Applications of Computer Vision, Snowmass Village, CO, United States, 2020.

A. Royer et al., “XGAN: Unsupervised image-to-image translation for many-to-many mappings,” in Domain Adaptation for Visual Understanding, R. Singh, M. Vatsa, V. M. Patel, and N. Ratha, Eds. Springer Nature, 2020, pp. 33–49.

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

N. H. Konstantinov, E. Frantar, D.-A. Alistarh, and C. Lampert, “On the sample complexity of adversarial multi-source PAC learning,” in Proceedings of the 37th International Conference on Machine Learning, Online, 2020, vol. 119, pp. 5416–5425.

A. Royer, “Leveraging structure in Computer Vision tasks for flexible Deep Learning models,” Institute of Science and Technology Austria, 2020.

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

R. Sun and C. Lampert, “KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications,” International Journal of Computer Vision, vol. 128, no. 4. Springer Nature, pp. 970–995, 2020.

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