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113 Publications
2023 | Conference Paper | IST-REx-ID: 13053 | 
Peste, E.-A., Vladu, A., Kurtic, E., Lampert, C., & Alistarh, D.-A. (n.d.). CrAM: A Compression-Aware Minimizer. In 11th International Conference on Learning Representations . Kigali, Rwanda .
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2023 | Thesis | IST-REx-ID: 13074 |
Peste, E.-A. (2023). Efficiency and generalization of sparse neural networks. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:13074
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2023 | Journal Article | IST-REx-ID: 14320 |
Henderson, P. M., Ghazaryan, A., Zibrov, A. A., Young, A. F., & Serbyn, M. (2023). Deep learning extraction of band structure parameters from density of states: A case study on trilayer graphene. Physical Review B. American Physical Society. https://doi.org/10.1103/physrevb.108.125411
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2023 | Conference Paper | IST-REx-ID: 14410
Tomaszewska, P., & Lampert, C. (2023). On the implementation of baselines and lightweight conditional model extrapolation (LIMES) under class-prior shift. In International Workshop on Reproducible Research in Pattern Recognition (Vol. 14068, pp. 67–73). Montreal, Canada: Springer Nature. https://doi.org/10.1007/978-3-031-40773-4_6
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2023 | Journal Article | IST-REx-ID: 14446 |
Jakubík, J., Phuong, M., Chvosteková, M., & Krakovská, A. (2023). Against the flow of time with multi-output models. Measurement Science Review. Sciendo. https://doi.org/10.2478/msr-2023-0023
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2023 | Conference Paper | IST-REx-ID: 14771 |
Iofinova, E. B., Peste, E.-A., & Alistarh, D.-A. (2023). Bias in pruned vision models: In-depth analysis and countermeasures. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 24364–24373). Vancouver, BC, Canada: IEEE. https://doi.org/10.1109/cvpr52729.2023.02334
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2023 | Conference Paper | IST-REx-ID: 14921 |
Súkeník, P., Mondelli, M., & Lampert, C. (n.d.). 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 |
Prach, B., & Lampert, C. (n.d.). 1-Lipschitz neural networks are more expressive with N-activations. arXiv. https://doi.org/10.48550/ARXIV.2311.06103
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2022 | Preprint | IST-REx-ID: 12660 |
Scott, J. A., Yeo, M. X., & Lampert, C. (n.d.). Cross-client Label Propagation for transductive federated learning. arXiv. https://doi.org/10.48550/arXiv.2210.06434
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2022 | Preprint | IST-REx-ID: 12662 |
Súkeník, P., & Lampert, C. (n.d.). Generalization in Multi-objective machine learning. arXiv. https://doi.org/10.48550/arXiv.2208.13499
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2022 | Journal Article | IST-REx-ID: 12495 |
Iofinova, E. B., Konstantinov, N. H., & Lampert, C. (2022). FLEA: Provably robust fair multisource learning from unreliable training data. Transactions on Machine Learning Research. ML Research Press.
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2022 | Conference Paper | IST-REx-ID: 11839 |
Prach, B., & Lampert, C. (2022). Almost-orthogonal layers for efficient general-purpose Lipschitz networks. In Computer Vision – ECCV 2022 (Vol. 13681, pp. 350–365). Tel Aviv, Israel: Springer Nature. https://doi.org/10.1007/978-3-031-19803-8_21
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2022 | Conference Paper | IST-REx-ID: 10752
Lampert, J., & Lampert, C. (2022). Overcoming rare-language discrimination in multi-lingual sentiment analysis. In 2021 IEEE International Conference on Big Data (pp. 5185–5192). Orlando, FL, United States: IEEE. https://doi.org/10.1109/bigdata52589.2021.9672003
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2022 | Conference Paper | IST-REx-ID: 12161 |
Tomaszewska, P., & Lampert, C. (2022). Lightweight conditional model extrapolation for streaming data under class-prior shift. In 26th International Conference on Pattern Recognition (Vol. 2022, pp. 2128–2134). Montreal, Canada: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/icpr56361.2022.9956195
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2022 | Conference Paper | IST-REx-ID: 12299 |
Iofinova, E. B., Peste, E.-A., Kurtz, M., & Alistarh, D.-A. (2022). How well do sparse ImageNet models transfer? In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12256–12266). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01195
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2022 | Journal Article | IST-REx-ID: 10802 |
Konstantinov, N. H., & Lampert, C. (2022). Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. ML Research Press.
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2022 | Conference Paper | IST-REx-ID: 13241 |
Konstantinov, N. H., & Lampert, C. (2022). On the impossibility of fairness-aware learning from corrupted data. In Proceedings of Machine Learning Research (Vol. 171, pp. 59–83). ML Research Press.
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2022 | Thesis | IST-REx-ID: 10799 |
Konstantinov, N. H. (2022). Robustness and fairness in machine learning. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10799
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2021 | Conference Paper | IST-REx-ID: 9210 |
Volhejn, V., & Lampert, C. (2021). Does SGD implicitly optimize for smoothness? In 42nd German Conference on Pattern Recognition (Vol. 12544, pp. 246–259). Tübingen, Germany: Springer. https://doi.org/10.1007/978-3-030-71278-5_18
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2021 | Conference Paper | IST-REx-ID: 9416 |
Phuong, M., & Lampert, C. (2021). The inductive bias of ReLU networks on orthogonally separable data. In 9th International Conference on Learning Representations. Virtual.
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2021 | Preprint | IST-REx-ID: 10803 |
Konstantinov, N. H., & Lampert, C. (n.d.). Fairness through regularization for learning to rank. arXiv. https://doi.org/10.48550/arXiv.2102.05996
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2021 | Thesis | IST-REx-ID: 9418 |
Phuong, M. (2021). Underspecification in deep learning. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:9418
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2021 | Book Chapter | IST-REx-ID: 14987
Lampert, C. (2021). Zero-Shot Learning. In K. Ikeuchi (Ed.), Computer Vision (2nd ed., pp. 1395–1397). Cham: Springer. https://doi.org/10.1007/978-3-030-63416-2_874
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2020 | Preprint | IST-REx-ID: 8063 |
Anciukevicius, T., Lampert, C., & Henderson, P. M. (n.d.). Object-centric image generation with factored depths, locations, and appearances. arXiv.
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2020 | Conference Paper | IST-REx-ID: 8188 |
Henderson, P. M., & Lampert, C. (2020). Unsupervised object-centric video generation and decomposition in 3D. In 34th Conference on Neural Information Processing Systems (Vol. 33, pp. 3106–3117). Vancouver, Canada: Curran Associates.
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2020 | Journal Article | IST-REx-ID: 6952 |
Henderson, P. M., & Ferrari, V. (2020). Learning single-image 3D reconstruction by generative modelling of shape, pose and shading. International Journal of Computer Vision. Springer Nature. https://doi.org/10.1007/s11263-019-01219-8
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2020 | Conference Paper | IST-REx-ID: 7936 |
Royer, A., & Lampert, C. (2020). Localizing grouped instances for efficient detection in low-resource scenarios. In IEEE Winter Conference on Applications of Computer Vision. Snowmass Village, CO, United States: IEEE. https://doi.org/10.1109/WACV45572.2020.9093288
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2020 | Conference Paper | IST-REx-ID: 7937 |
Royer, A., & Lampert, C. (2020). A flexible selection scheme for minimum-effort transfer learning. In 2020 IEEE Winter Conference on Applications of Computer Vision. Snowmass Village, CO, United States: IEEE. https://doi.org/10.1109/WACV45572.2020.9093635
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2020 | Book Chapter | IST-REx-ID: 8092 |
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 R. Singh, M. Vatsa, V. M. Patel, & N. Ratha (Eds.), Domain Adaptation for Visual Understanding (pp. 33–49). Springer Nature. https://doi.org/10.1007/978-3-030-30671-7_3
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2020 | Conference Paper | IST-REx-ID: 7481 |
Phuong, M., & Lampert, C. (2020). Functional vs. parametric equivalence of ReLU networks. In 8th International Conference on Learning Representations. Online.
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2020 | Conference Paper | IST-REx-ID: 8724 |
Konstantinov, N. H., Frantar, E., Alistarh, D.-A., & Lampert, C. (2020). On the sample complexity of adversarial multi-source PAC learning. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 5416–5425). Online: ML Research Press.
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2020 | Thesis | IST-REx-ID: 8390 |
Royer, A. (2020). Leveraging structure in Computer Vision tasks for flexible Deep Learning models. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:8390
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2020 | Conference Paper | IST-REx-ID: 8186 |
Henderson, P. M., Tsiminaki, V., & Lampert, C. (2020). Leveraging 2D data to learn textured 3D mesh generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7498–7507). Virtual: IEEE. https://doi.org/10.1109/CVPR42600.2020.00752
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2020 | Journal Article | IST-REx-ID: 6944 |
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. Springer Nature. https://doi.org/10.1007/s11263-019-01232-x
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2019 | Book (Editor) | IST-REx-ID: 7171
Kersting, K., Lampert, C., & Rothkopf, C. (Eds.). (2019). Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt (1st ed.). Wiesbaden: Springer Nature. https://doi.org/10.1007/978-3-658-26763-6
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2019 | Conference Paper | IST-REx-ID: 6942 |
Ashok, P., Brázdil, T., Chatterjee, K., Křetínský, J., Lampert, C., & Toman, V. (2019). Strategy representation by decision trees with linear classifiers. In 16th International Conference on Quantitative Evaluation of Systems (Vol. 11785, pp. 109–128). Glasgow, United Kingdom: Springer Nature. https://doi.org/10.1007/978-3-030-30281-8_7
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2019 | Journal Article | IST-REx-ID: 6554 |
Xian, Y., Lampert, C., Schiele, B., & Akata, Z. (2019). Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis and Machine Intelligence. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/tpami.2018.2857768
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2019 | Conference Paper | IST-REx-ID: 7479 |
Phuong, M., & Lampert, C. (2019). Distillation-based training for multi-exit architectures. In IEEE International Conference on Computer Vision (Vol. 2019–October, pp. 1355–1364). Seoul, Korea: IEEE. https://doi.org/10.1109/ICCV.2019.00144
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2019 | Conference Paper | IST-REx-ID: 7640 |
Kolesnikov, A., Kuznetsova, A., Lampert, C., & Ferrari, V. (2019). Detecting visual relationships using box attention. In Proceedings of the 2019 International Conference on Computer Vision Workshop. Seoul, South Korea: IEEE. https://doi.org/10.1109/ICCVW.2019.00217
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2019 | Conference Paper | IST-REx-ID: 6569 |
Phuong, M., & Lampert, C. (2019). Towards understanding knowledge distillation. In Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 5142–5151). Long Beach, CA, United States: ML Research Press.
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2019 | Conference Paper | IST-REx-ID: 6590 |
Konstantinov, N. H., & Lampert, C. (2019). Robust learning from untrusted sources. In Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 3488–3498). Long Beach, CA, USA: ML Research Press.
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2019 | Conference Paper | IST-REx-ID: 6482 |
Sun, R., & Lampert, C. (2019). KS(conf): A light-weight test if a ConvNet operates outside of Its specifications (Vol. 11269, pp. 244–259). Presented at the GCPR: Conference on Pattern Recognition, Stuttgart, Germany: Springer Nature. https://doi.org/10.1007/978-3-030-12939-2_18
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2018 | Thesis | IST-REx-ID: 68 |
Zimin, A. (2018). Learning from dependent data. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:TH1048
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2018 | Thesis | IST-REx-ID: 197 |
Kolesnikov, A. (2018). Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:th_1021
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2018 | Journal Article | IST-REx-ID: 563 |
Ringbauer, H., Kolesnikov, A., Field, D., & Barton, N. H. (2018). Estimating barriers to gene flow from distorted isolation-by-distance patterns. Genetics. Genetics Society of America. https://doi.org/10.1534/genetics.117.300638
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2018 | Journal Article | IST-REx-ID: 321 |
Darrell, T., Lampert, C., Sebe, N., Wu, Y., & Yan, Y. (2018). 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. IEEE. https://doi.org/10.1109/TPAMI.2018.2804998
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2018 | Conference Paper | IST-REx-ID: 10882 |
Uijlings, J., Konyushkova, K., Lampert, C., & Ferrari, V. (2018). Learning intelligent dialogs for bounding box annotation. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9175–9184). Salt Lake City, UT, United States: IEEE. https://doi.org/10.1109/cvpr.2018.00956
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2018 | Conference Paper | IST-REx-ID: 6012 |
Sahoo, S., Lampert, C., & Martius, G. S. (2018). Learning equations for extrapolation and control. In Proceedings of the 35th International Conference on Machine Learning (Vol. 80, pp. 4442–4450). Stockholm, Sweden: ML Research Press.
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2018 | Conference Paper | IST-REx-ID: 6011 |
Kuzborskij, I., & Lampert, C. (2018). Data-dependent stability of stochastic gradient descent. In Proceedings of the 35 th International Conference on Machine Learning (Vol. 80, pp. 2815–2824). Stockholm, Sweden: ML Research Press.
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2018 | Conference Paper | IST-REx-ID: 6589 |
Alistarh, D.-A., Hoefler, T., Johansson, M., Konstantinov, N. H., Khirirat, S., & Renggli, C. (2018). The convergence of sparsified gradient methods. In Advances in Neural Information Processing Systems 31 (Vol. Volume 2018, pp. 5973–5983). Montreal, Canada: Neural Information Processing Systems Foundation.
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