Konstantinov NH, Lampert C. 2022. Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. 23, 1–60.
LibreCat: https://research-explorer.ista.ac.at/record/10802
arXiv: 2102.06004

Konstantinov NH, Lampert C. 2022. On the impossibility of fairness-aware learning from corrupted data. Proceedings of Machine Learning Research. vol. 171, 59–83.
LibreCat: https://research-explorer.ista.ac.at/record/13241
arXiv: 2102.06004

Konstantinov NH. 2022. Robustness and fairness in machine learning. Institute of Science and Technology Austria.
LibreCat: https://research-explorer.ista.ac.at/record/10799
DOI: 10.15479/at:ista:10799

Volhejn V, Lampert C. 2021. Does SGD implicitly optimize for smoothness? 42nd German Conference on Pattern Recognition. DAGM GCPR: German Conference on Pattern Recognition LNCS vol. 12544, 246–259.
LibreCat: https://research-explorer.ista.ac.at/record/9210
DOI: 10.1007/978-3-030-71278-5_18

Phuong M, Lampert C. 2021. The inductive bias of ReLU networks on orthogonally separable data. 9th International Conference on Learning Representations.  ICLR: International Conference on Learning Representations.
LibreCat: https://research-explorer.ista.ac.at/record/9416

Konstantinov NH, Lampert C. Fairness through regularization for learning to rank. arXiv, 2102.05996.
LibreCat: https://research-explorer.ista.ac.at/record/10803
DOI: 10.48550/arXiv.2102.05996
arXiv: 2102.05996

Phuong M. 2021. Underspecification in deep learning. Institute of Science and Technology Austria.
LibreCat: https://research-explorer.ista.ac.at/record/9418
DOI: 10.15479/AT:ISTA:9418

Lampert C. 2021.Zero-Shot Learning. In: Computer Vision. , 1395–1397.
LibreCat: https://research-explorer.ista.ac.at/record/14987
DOI: 10.1007/978-3-030-63416-2_874

Anciukevicius T, Lampert C, Henderson PM. Object-centric image generation with factored depths, locations, and appearances. arXiv, 2004.00642.
LibreCat: https://research-explorer.ista.ac.at/record/8063
arXiv: 2004.00642

Henderson PM, Lampert C. 2020. Unsupervised object-centric video generation and decomposition in 3D. 34th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 33, 3106–3117.
LibreCat: https://research-explorer.ista.ac.at/record/8188
arXiv: 2007.06705

Henderson PM, Ferrari V. 2020. Learning single-image 3D reconstruction by generative modelling of shape, pose and shading. International Journal of Computer Vision. 128, 835–854.
LibreCat: https://research-explorer.ista.ac.at/record/6952
DOI: 10.1007/s11263-019-01219-8
WoS: 000491042100002
arXiv: 1901.06447

Royer A, Lampert C. 2020. Localizing grouped instances for efficient detection in low-resource scenarios. IEEE Winter Conference on Applications of Computer Vision. WACV: Winter Conference on Applications of Computer Vision, 1716–1725.
LibreCat: https://research-explorer.ista.ac.at/record/7936
DOI: 10.1109/WACV45572.2020.9093288
arXiv: 2004.12623

Royer A, Lampert C. 2020. A flexible selection scheme for minimum-effort transfer learning. 2020 IEEE Winter Conference on Applications of Computer Vision. WACV: Winter Conference on Applications of Computer Vision, 2180–2189.
LibreCat: https://research-explorer.ista.ac.at/record/7937
DOI: 10.1109/WACV45572.2020.9093635
arXiv: 2008.11995

Royer A, Bousmalis K, Gouws S, Bertsch F, Mosseri I, Cole F, Murphy K. 2020.XGAN: Unsupervised image-to-image translation for many-to-many mappings. In: Domain Adaptation for Visual Understanding. , 33–49.
LibreCat: https://research-explorer.ista.ac.at/record/8092
DOI: 10.1007/978-3-030-30671-7_3
arXiv: 1711.05139

Phuong M, Lampert C. 2020. Functional vs. parametric equivalence of ReLU networks. 8th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.
LibreCat: https://research-explorer.ista.ac.at/record/7481

Konstantinov NH, Frantar E, Alistarh D-A, Lampert C. 2020. On the sample complexity of adversarial multi-source PAC learning. Proceedings of the 37th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 119, 5416–5425.
LibreCat: https://research-explorer.ista.ac.at/record/8724
arXiv: 2002.10384

Royer A. 2020. Leveraging structure in Computer Vision tasks for flexible Deep Learning models. Institute of Science and Technology Austria.
LibreCat: https://research-explorer.ista.ac.at/record/8390
DOI: 10.15479/AT:ISTA:8390

Henderson PM, Tsiminaki V, Lampert C. 2020. Leveraging 2D data to learn textured 3D mesh generation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 7498–7507.
LibreCat: https://research-explorer.ista.ac.at/record/8186
DOI: 10.1109/CVPR42600.2020.00752
arXiv: 2004.04180

Sun R, Lampert C. 2020. KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications. International Journal of Computer Vision. 128(4), 970–995.
LibreCat: https://research-explorer.ista.ac.at/record/6944
DOI: 10.1007/s11263-019-01232-x
WoS: 000494406800001

Kersting K, Lampert C, Rothkopf C eds. 2019. Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt 1st ed., Wiesbaden: Springer Nature, XIV, 245p.
LibreCat: https://research-explorer.ista.ac.at/record/7171
DOI: 10.1007/978-3-658-26763-6