Ringbauer H, Kolesnikov A, Field D, Barton NH. 2018. Estimating barriers to gene flow from distorted isolation-by-distance patterns. Genetics. 208(3), 1231–1245.

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. 40(5), 1029–1031.

Uijlings J, Konyushkova K, Lampert C, Ferrari V. 2018. Learning intelligent dialogs for bounding box annotation. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVF: Conference on Computer Vision and Pattern Recognition, 9175–9184.

Sahoo S, Lampert C, Martius GS. 2018. Learning equations for extrapolation and control. Proceedings of the 35th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 80, 4442–4450.

Kuzborskij I, Lampert C. 2018. Data-dependent stability of stochastic gradient descent. Proceedings of the 35 th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 80, 2815–2824.

Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli C. 2018. The convergence of sparsified gradient methods. Advances in Neural Information Processing Systems 31. NeurIPS: Conference on Neural Information Processing Systems vol. Volume 2018, 5973–5983.

Der R, Martius GS. 2017. Dynamical self consistency leads to behavioral development and emergent social interactions in robots. ICDL EpiRob: International Conference on Development and Learning and Epigenetic Robotics , 7846789.

Der R, Martius GS. 2017. Self organized behavior generation for musculoskeletal robots. Frontiers in Neurorobotics. 11(MAR), 00008.

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

Pielorz J, Prandtstetter M, Straub M, Lampert C. 2017. Optimal geospatial volunteer allocation needs realistic distances. 2017 IEEE International Conference on Big Data. Big Data, 3760–3763.

Kolesnikov A, Lampert C. 2017. PixelCNN models with auxiliary variables for natural image modeling. 34th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 70, 1905–1914.

Rebuffi SA, Kolesnikov A, Sperl G, Lampert C. 2017. iCaRL: Incremental classifier and representation learning. CVPR: Computer Vision and Pattern Recognition vol. 2017, 5533–5542.

Royer A, Kolesnikov A, Lampert C. 2017. Probabilistic image colorization. BMVC: British Machine Vision Conference, 85.1-85.12.

Zimin A, Lampert C. 2017. Learning theory for conditional risk minimization. AISTATS: Artificial Intelligence and Statistics, PMLR, vol. 54, 213–222.

Pentina A, Lampert C. 2017. Multi-task learning with labeled and unlabeled tasks. ICML: International Conference on Machine Learning, PMLR, vol. 70, 2807–2816.

Pentina A, Urner R. 2016. Lifelong learning with weighted majority votes. NIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 29, 3619–3627.

Kolesnikov A, Lampert C. 2016. Improving weakly-supervised object localization by micro-annotation. Proceedings of the British Machine Vision Conference 2016. BMVC: British Machine Vision Conference vol. 2016–September, 92.1-92.12.

Martius GS, Hostettler R, Knoll A, Der R. 2016. Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm. IEEE RSJ International Conference on Intelligent Robots and Systems IROS  vol. 2016–November, 7759138.

Kolesnikov A, Lampert C. 2016. Seed, expand and constrain: Three principles for weakly-supervised image segmentation. ECCV: European Conference on Computer Vision, LNCS, vol. 9908, 695–711.

Pielorz J, Lampert C. 2016. Optimal geospatial allocation of volunteers for crisis management. ICT-DM: Information and Communication Technologies for Disaster Management, 7402041.