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113 Publications
2023 | Conference Paper | IST-REx-ID: 13053 | 
E.-A. Peste, A. Vladu, E. Kurtic, C. Lampert, and D.-A. Alistarh, “CrAM: A Compression-Aware Minimizer,” in 11th International Conference on Learning Representations , Kigali, Rwanda .
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2023 | Thesis | IST-REx-ID: 13074 |
E.-A. Peste, “Efficiency and generalization of sparse neural networks,” Institute of Science and Technology Austria, 2023.
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2023 | Journal Article | IST-REx-ID: 14320 |
P. M. Henderson, A. Ghazaryan, A. A. Zibrov, A. F. Young, and M. Serbyn, “Deep learning extraction of band structure parameters from density of states: A case study on trilayer graphene,” Physical Review B, vol. 108, no. 12. American Physical Society, 2023.
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2023 | Conference Paper | IST-REx-ID: 14410
P. Tomaszewska and C. Lampert, “On the implementation of baselines and lightweight conditional model extrapolation (LIMES) under class-prior shift,” in International Workshop on Reproducible Research in Pattern Recognition, Montreal, Canada, 2023, vol. 14068, pp. 67–73.
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2023 | Journal Article | IST-REx-ID: 14446 |
J. Jakubík, M. Phuong, M. Chvosteková, and A. Krakovská, “Against the flow of time with multi-output models,” Measurement Science Review, vol. 23, no. 4. Sciendo, pp. 175–183, 2023.
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2023 | Conference Paper | IST-REx-ID: 14771 |
E. B. Iofinova, E.-A. Peste, and D.-A. Alistarh, “Bias in pruned vision models: In-depth analysis and countermeasures,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 2023, pp. 24364–24373.
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2023 | Conference Paper | IST-REx-ID: 14921 |
P. Súkeník, M. Mondelli, and C. Lampert, “Deep neural collapse is provably optimal for the deep unconstrained features model,” in 37th Annual Conference on Neural Information Processing Systems, New Orleans, LA, United States.
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2023 | Preprint | IST-REx-ID: 15039 |
B. Prach and C. Lampert, “1-Lipschitz neural networks are more expressive with N-activations,” arXiv. .
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2022 | 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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2022 | Preprint | IST-REx-ID: 12662 |
P. Súkeník and C. Lampert, “Generalization in Multi-objective machine learning,” arXiv. .
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2022 | 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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2022 | 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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2022 | 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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2022 | Conference Paper | IST-REx-ID: 12299 |
E. B. Iofinova, E.-A. Peste, 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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2022 | 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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2022 | 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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2022 | Thesis | IST-REx-ID: 10799 |
N. H. Konstantinov, “Robustness and fairness in machine learning,” Institute of Science and Technology Austria, 2022.
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2021 | 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 | 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 | Preprint | IST-REx-ID: 10803 |
N. H. Konstantinov and C. Lampert, “Fairness through regularization for learning to rank,” arXiv. .
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2021 | Thesis | IST-REx-ID: 9418 |
M. Phuong, “Underspecification in deep learning,” Institute of Science and Technology Austria, 2021.
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2020 | 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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2020 | Conference Paper | IST-REx-ID: 8188 |
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.
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2020 | Journal Article | IST-REx-ID: 6952 |
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.
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2020 | 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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2020 | 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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2020 | 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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2020 | 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 | 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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2020 | Thesis | IST-REx-ID: 8390 |
A. Royer, “Leveraging structure in Computer Vision tasks for flexible Deep Learning models,” Institute of Science and Technology Austria, 2020.
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2020 | 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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2020 | Journal Article | IST-REx-ID: 6944 |
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.
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2019 | Book (Editor) | IST-REx-ID: 7171
K. Kersting, C. Lampert, and C. Rothkopf, Eds., Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt, 1st ed. Wiesbaden: Springer Nature, 2019.
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2019 | Conference Paper | IST-REx-ID: 6942 |
P. Ashok, T. Brázdil, K. Chatterjee, J. Křetínský, C. Lampert, and V. Toman, “Strategy representation by decision trees with linear classifiers,” in 16th International Conference on Quantitative Evaluation of Systems, Glasgow, United Kingdom, 2019, vol. 11785, pp. 109–128.
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2019 | Journal Article | IST-REx-ID: 6554 |
Y. Xian, C. Lampert, B. Schiele, and Z. Akata, “Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 9. Institute of Electrical and Electronics Engineers (IEEE), pp. 2251–2265, 2019.
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2019 | Conference Paper | IST-REx-ID: 7479 |
M. Phuong and C. Lampert, “Distillation-based training for multi-exit architectures,” in IEEE International Conference on Computer Vision, Seoul, Korea, 2019, vol. 2019–October, pp. 1355–1364.
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2019 | Conference Paper | IST-REx-ID: 7640 |
A. Kolesnikov, A. Kuznetsova, C. Lampert, and V. Ferrari, “Detecting visual relationships using box attention,” in Proceedings of the 2019 International Conference on Computer Vision Workshop, Seoul, South Korea, 2019.
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2019 | Conference Paper | IST-REx-ID: 6569 |
M. Phuong and C. Lampert, “Towards understanding knowledge distillation,” in Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, United States, 2019, vol. 97, pp. 5142–5151.
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2019 | Conference Paper | IST-REx-ID: 6590 |
N. H. Konstantinov and C. Lampert, “Robust learning from untrusted sources,” in Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA, 2019, vol. 97, pp. 3488–3498.
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2019 | Conference Paper | IST-REx-ID: 6482 |
R. Sun and C. Lampert, “KS(conf): A light-weight test if a ConvNet operates outside of Its specifications,” presented at the GCPR: Conference on Pattern Recognition, Stuttgart, Germany, 2019, vol. 11269, pp. 244–259.
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2018 | Thesis | IST-REx-ID: 68 |
A. Zimin, “Learning from dependent data,” Institute of Science and Technology Austria, 2018.
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2018 | Thesis | IST-REx-ID: 197 |
A. Kolesnikov, “Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images,” Institute of Science and Technology Austria, 2018.
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2018 | Journal Article | IST-REx-ID: 563 |
H. Ringbauer, A. Kolesnikov, D. Field, and N. H. Barton, “Estimating barriers to gene flow from distorted isolation-by-distance patterns,” Genetics, vol. 208, no. 3. Genetics Society of America, pp. 1231–1245, 2018.
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2018 | Journal Article | IST-REx-ID: 321 |
T. Darrell, C. Lampert, N. Sebe, Y. Wu, and Y. Yan, “Guest editors’ introduction to the special section on learning with Shared information for computer vision and multimedia analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 5. IEEE, pp. 1029–1031, 2018.
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2018 | Conference Paper | IST-REx-ID: 10882 |
J. Uijlings, K. Konyushkova, C. Lampert, and V. Ferrari, “Learning intelligent dialogs for bounding box annotation,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, United States, 2018, pp. 9175–9184.
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2018 | Conference Paper | IST-REx-ID: 6012 |
S. Sahoo, C. Lampert, and G. S. Martius, “Learning equations for extrapolation and control,” in Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, 2018, vol. 80, pp. 4442–4450.
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2018 | Conference Paper | IST-REx-ID: 6011 |
I. Kuzborskij and C. Lampert, “Data-dependent stability of stochastic gradient descent,” in Proceedings of the 35 th International Conference on Machine Learning, Stockholm, Sweden, 2018, vol. 80, pp. 2815–2824.
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2018 | Conference Paper | IST-REx-ID: 6589 |
D.-A. Alistarh, T. Hoefler, M. Johansson, N. H. Konstantinov, S. Khirirat, and C. Renggli, “The convergence of sparsified gradient methods,” in Advances in Neural Information Processing Systems 31, Montreal, Canada, 2018, vol. Volume 2018, pp. 5973–5983.
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