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

Guest editors' introduction to the special section on learning with Shared information for computer vision and multimedia analysis
T. Darrell, C. Lampert, N. Sebe, Y. Wu, Y. Yan, IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (2018) 1029–1031.

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

Learning equations for extrapolation and control
S. Sahoo, C. Lampert, G.S. Martius, in:, Proceedings of the 35th International Conference on Machine Learning, ML Research Press, 2018, pp. 4442–4450.

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

The convergence of sparsified gradient methods
D.-A. Alistarh, T. Hoefler, M. Johansson, N.H. Konstantinov, S. Khirirat, C. Renggli, in:, Advances in Neural Information Processing Systems 31, Neural Information Processing Systems Foundation, 2018, pp. 5973–5983.

Nonlinear decoding of a complex movie from the mammalian retina
S. Deny, O. Marre, V. Botella-Soler, G.S. Martius, G. Tkadik, (2018).

Dynamical self consistency leads to behavioral development and emergent social interactions in robots
R. Der, G.S. Martius, in:, IEEE, 2017.

Self organized behavior generation for musculoskeletal robots
R. Der, G.S. Martius, Frontiers in Neurorobotics 11 (2017).

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

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

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

iCaRL: Incremental classifier and representation learning
S.A. Rebuffi, A. Kolesnikov, G. Sperl, C. Lampert, in:, IEEE, 2017, pp. 5533–5542.

Probabilistic image colorization
A. Royer, A. Kolesnikov, C. Lampert, in:, BMVA Press, 2017, p. 85.1-85.12.

Learning theory for conditional risk minimization
A. Zimin, C. Lampert, in:, ML Research Press, 2017, pp. 213–222.

Multi-task learning with labeled and unlabeled tasks
A. Pentina, C. Lampert, in:, ML Research Press, 2017, pp. 2807–2816.

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

Improving weakly-supervised object localization by micro-annotation
A. Kolesnikov, C. Lampert, in:, Proceedings of the British Machine Vision Conference 2016, BMVA Press, 2016, p. 92.1-92.12.

Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm
G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, IEEE, 2016.

Seed, expand and constrain: Three principles for weakly-supervised image segmentation
A. Kolesnikov, C. Lampert, in:, Springer, 2016, pp. 695–711.