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119 Publications
2022 | Published | Journal Article | IST-REx-ID: 10802 | 
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.
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| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 12161 |
P. Tomaszewska and C. Lampert, “Lightweight conditional model extrapolation for streaming data under class-prior shift,” in 26th International Conference on Pattern Recognition, Montreal, Canada, 2022, vol. 2022, pp. 2128–2134.
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| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 12299 |
E. B. Iofinova, A. Krumes, M. Kurtz, and D.-A. Alistarh, “How well do sparse ImageNet models transfer?,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, United States, 2022, pp. 12256–12266.
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| arXiv
2022 | Published | Journal Article | IST-REx-ID: 12495 |
E. B. Iofinova, N. H. Konstantinov, and C. Lampert, “FLEA: Provably robust fair multisource learning from unreliable training data,” Transactions on Machine Learning Research. ML Research Press, 2022.
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| arXiv
2022 | Submitted | Preprint | IST-REx-ID: 12660 |
J. A. Scott, M. X. Yeo, and C. Lampert, “Cross-client Label Propagation for transductive federated learning,” arXiv. .
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| arXiv
2022 | Submitted | Preprint | IST-REx-ID: 12662 |
P. Súkeník and C. Lampert, “Generalization in Multi-objective machine learning,” arXiv. .
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| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 11839 |
B. Prach and C. Lampert, “Almost-orthogonal layers for efficient general-purpose Lipschitz networks,” in Computer Vision – ECCV 2022, Tel Aviv, Israel, 2022, vol. 13681, pp. 350–365.
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| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 13241 |
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.
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 9210 |
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.
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2021 | Published | Conference Paper | IST-REx-ID: 9416 |
M. Phuong and C. Lampert, “The inductive bias of ReLU networks on orthogonally separable data,” in 9th International Conference on Learning Representations, Virtual, 2021.
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2021 | Published | Thesis | IST-REx-ID: 9418 |
M. Phuong, “Underspecification in deep learning,” Institute of Science and Technology Austria, 2021.
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2021 | Submitted | Preprint | IST-REx-ID: 10803 |
N. H. Konstantinov and C. Lampert, “Fairness through regularization for learning to rank,” arXiv. .
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| arXiv
2020 | Published | Conference Paper | IST-REx-ID: 8724 |
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.
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| arXiv
2020 | Published | Conference Paper | IST-REx-ID: 7481 |
M. Phuong and C. Lampert, “Functional vs. parametric equivalence of ReLU networks,” in 8th International Conference on Learning Representations, Online, 2020.
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2020 | Published | Conference Paper | IST-REx-ID: 7936 |
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.
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| arXiv
2020 | Published | Conference Paper | IST-REx-ID: 7937 |
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.
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| arXiv
2020 | Submitted | Preprint | IST-REx-ID: 8063 |
T. Anciukevicius, C. Lampert, and P. M. Henderson, “Object-centric image generation with factored depths, locations, and appearances,” arXiv. .
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| arXiv
2020 | Published | Book Chapter | IST-REx-ID: 8092 |
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.
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| arXiv
2020 | Published | Conference Paper | IST-REx-ID: 8186 |
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.
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| arXiv