Phuong M, Lampert C. Towards understanding knowledge distillation. In: Proceedings of the 36th International Conference on Machine Learning. Vol 97. ML Research Press; 2019:5142-5151.

Konstantinov NH, Lampert C. Robust learning from untrusted sources. In: Proceedings of the 36th International Conference on Machine Learning. Vol 97. ML Research Press; 2019:3488-3498.

Sun R, Lampert C. KS(conf): A light-weight test if a ConvNet operates outside of Its specifications. In: Vol 11269. Springer Nature; 2019:244-259. doi:10.1007/978-3-030-12939-2_18

Zimin A. Learning from dependent data. 2018. doi:10.15479/AT:ISTA:TH1048

Kolesnikov A. Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images. 2018. doi:10.15479/AT:ISTA:th_1021

Ringbauer H, Kolesnikov A, Field D, Barton NH. Estimating barriers to gene flow from distorted isolation-by-distance patterns. Genetics. 2018;208(3):1231-1245. doi:10.1534/genetics.117.300638

Darrell T, Lampert C, Sebe N, Wu Y, Yan Y. 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. 2018;40(5):1029-1031. doi:10.1109/TPAMI.2018.2804998

Uijlings J, Konyushkova K, Lampert C, Ferrari V. Learning intelligent dialogs for bounding box annotation. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE; 2018:9175-9184. doi:10.1109/cvpr.2018.00956

Sahoo S, Lampert C, Martius GS. Learning equations for extrapolation and control. In: Proceedings of the 35th International Conference on Machine Learning. Vol 80. ML Research Press; 2018:4442-4450.

Kuzborskij I, Lampert C. Data-dependent stability of stochastic gradient descent. In: Proceedings of the 35 Th International Conference on Machine Learning. Vol 80. ML Research Press; 2018:2815-2824.

Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli C. The convergence of sparsified gradient methods. In: Advances in Neural Information Processing Systems 31. Vol Volume 2018. Neural Information Processing Systems Foundation; 2018:5973-5983.

Der R, Martius GS. Dynamical self consistency leads to behavioral development and emergent social interactions in robots. In: IEEE; 2017. doi:10.1109/DEVLRN.2016.7846789

Der R, Martius GS. Self organized behavior generation for musculoskeletal robots. Frontiers in Neurorobotics. 2017;11(MAR). doi:10.3389/fnbot.2017.00008

Martius GS, Lampert C. Extrapolation and learning equations. In: 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings. International Conference on Learning Representations; 2017.

Pielorz J, Prandtstetter M, Straub M, Lampert C. Optimal geospatial volunteer allocation needs realistic distances. In: 2017 IEEE International Conference on Big Data. IEEE; 2017:3760-3763. doi:10.1109/BigData.2017.8258375

Kolesnikov A, Lampert C. PixelCNN models with auxiliary variables for natural image modeling. In: 34th International Conference on Machine Learning. Vol 70. JMLR; 2017:1905-1914.

Rebuffi SA, Kolesnikov A, Sperl G, Lampert C. iCaRL: Incremental classifier and representation learning. In: Vol 2017. IEEE; 2017:5533-5542. doi:10.1109/CVPR.2017.587

Royer A, Kolesnikov A, Lampert C. Probabilistic image colorization. In: BMVA Press; 2017:85.1-85.12. doi:10.5244/c.31.85

Zimin A, Lampert C. Learning theory for conditional risk minimization. In: Vol 54. ML Research Press; 2017:213-222.

Pentina A, Lampert C. Multi-task learning with labeled and unlabeled tasks. In: Vol 70. ML Research Press; 2017:2807-2816.