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 
LibreCat: https://research-explorer.ista.ac.at/record/7171
DOI: 10.1007/978-3-658-26763-6

Ashok, P., Brázdil, T., Chatterjee, K., Ketínský, 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 
LibreCat: https://research-explorer.ista.ac.at/record/6942
DOI: 10.1007/978-3-030-30281-8_7
WoS: 000679281300007
arXiv: 1906.08178

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 
LibreCat: https://research-explorer.ista.ac.at/record/6554
DOI: 10.1109/tpami.2018.2857768
WoS: 000480343900015
arXiv: 1707.00600

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 
LibreCat: https://research-explorer.ista.ac.at/record/7479
DOI: 10.1109/ICCV.2019.00144
WoS: 000531438101047

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 
LibreCat: https://research-explorer.ista.ac.at/record/7640
DOI: 10.1109/ICCVW.2019.00217
WoS: 000554591601098
arXiv: 1807.02136

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.
LibreCat: https://research-explorer.ista.ac.at/record/6569

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.
LibreCat: https://research-explorer.ista.ac.at/record/6590
arXiv: 1901.10310

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 
LibreCat: https://research-explorer.ista.ac.at/record/6482
DOI: 10.1007/978-3-030-12939-2_18
arXiv: 1804.04171

Zimin, A. (2018). Learning from dependent data. Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:TH1048 
LibreCat: https://research-explorer.ista.ac.at/record/68
DOI: 10.15479/AT:ISTA:TH1048

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 
LibreCat: https://research-explorer.ista.ac.at/record/197
DOI: 10.15479/AT:ISTA:th_1021

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 
LibreCat: https://research-explorer.ista.ac.at/record/563
DOI: 10.1534/genetics.117.300638
WoS: 000426219600025

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 
LibreCat: https://research-explorer.ista.ac.at/record/321
DOI: 10.1109/TPAMI.2018.2804998
WoS: 000428901200001

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 
LibreCat: https://research-explorer.ista.ac.at/record/10882
DOI: 10.1109/cvpr.2018.00956
WoS: 000457843609036
arXiv: 1712.08087

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.
LibreCat: https://research-explorer.ista.ac.at/record/6012
WoS: 000683379204058
arXiv: 1806.07259

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.
LibreCat: https://research-explorer.ista.ac.at/record/6011
WoS: 000683379202095
arXiv: 1703.01678

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.
LibreCat: https://research-explorer.ista.ac.at/record/6589
WoS: 000461852000047
arXiv: 1809.10505

Deny, S., Marre, O., Botella-Soler, V., Martius, G. S., &; Tkadik, G. (2018). Nonlinear decoding of a complex movie from the mammalian retina. Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:98 
LibreCat: https://research-explorer.ista.ac.at/record/5584
DOI: 10.15479/AT:ISTA:98

Der, R., &; Martius, G. S. (2017). Dynamical self consistency leads to behavioral development and emergent social interactions in robots. Presented at the ICDL EpiRob: International Conference on Development and Learning and Epigenetic Robotics , Cergy-Pontoise, France: IEEE.https://doi.org/10.1109/DEVLRN.2016.7846789 
LibreCat: https://research-explorer.ista.ac.at/record/652
DOI: 10.1109/DEVLRN.2016.7846789

Der, R., &; Martius, G. S. (2017). Self organized behavior generation for musculoskeletal robots. Frontiers in Neurorobotics. Frontiers Research Foundation.https://doi.org/10.3389/fnbot.2017.00008 
LibreCat: https://research-explorer.ista.ac.at/record/658
DOI: 10.3389/fnbot.2017.00008

Martius, G. S., &; Lampert, C. (2017). Extrapolation and learning equations. In 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings. Toulon, France: International Conference on Learning Representations.
LibreCat: https://research-explorer.ista.ac.at/record/6841
arXiv: 1610.02995