Robust learning from untrusted sources
N.H. Konstantinov, C. Lampert, in:, Proceedings of the 36th International Conference on Machine Learning, ML Research Press, 2019, pp. 3488–3498.

KS(conf): A light-weight test if a ConvNet operates outside of Its specifications
R. Sun, C. Lampert, in:, Springer Nature, 2019, pp. 244–259.

Learning from dependent data
A. Zimin, Learning from Dependent Data, Institute of Science and Technology Austria, 2018.

Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images
A. Kolesnikov, Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images, Institute of Science and Technology Austria, 2018.

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.

Optimal geospatial allocation of volunteers for crisis management
J. Pielorz, C. Lampert, in:, IEEE, 2016.

Self-organized control of an tendon driven arm by differential extrinsic plasticity
G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, Proceedings of the Artificial Life Conference 2016, MIT Press, 2016, pp. 142–143.

Theoretical foundations of multi-task lifelong learning
A. Pentina, Theoretical Foundations of Multi-Task Lifelong Learning, Institute of Science and Technology Austria, 2016.

Lifelong learning with non-i.i.d. tasks
A. Pentina, C. Lampert, in:, Neural Information Processing Systems, 2015, pp. 1540–1548.

Segmentation over detection via optimal sparse reconstructions
W. Xia, C. Domokos, J. Xiong, L. Cheong, S. Yan, IEEE Transactions on Circuits and Systems for Video Technology 25 (2015) 1295–1308.

Novel plasticity rule can explain the development of sensorimotor intelligence
R. Der, G.S. Martius, PNAS 112 (2015) 24–32.

Multi-task and lifelong learning of kernels
A. Pentina, S. Ben David, in:, Springer, 2015, pp. 194–208.

A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle
N. Shah, V. Kolmogorov, C. Lampert, in:, IEEE, 2015, pp. 2737–2745.

Classifier adaptation at prediction time
A. Royer, C. Lampert, in:, IEEE, 2015, pp. 1401–1409.

Predicting the future behavior of a time-varying probability distribution
C. Lampert, in:, IEEE, 2015, pp. 942–950.

Curriculum learning of multiple tasks
A. Pentina, V. Sharmanska, C. Lampert, in:, IEEE, 2015, pp. 5492–5500.

Quantifying self-organizing behavior of autonomous robots
G.S. Martius, E. Olbrich, in:, Proceedings of the 13th European Conference on Artificial Life, MIT Press, 2015, p. 78.

Learning with attributes for object recognition: Parametric and non-parametrics views
V. Sharmanska, Learning with Attributes for Object Recognition: Parametric and Non-Parametrics Views, Institute of Science and Technology Austria, 2015.

Quantifying emergent behavior of autonomous robots
G.S. Martius, E. Olbrich, Entropy 17 (2015) 7266–7297.

Movement templates for learning of hitting and batting
K. Muelling, O. Kroemer, C. Lampert, B. Schölkopf, in:, J. Kober, J. Peters (Eds.), Learning Motor Skills, Springer, 2014, pp. 69–82.

Mind the nuisance: Gaussian process classification using privileged noise
D. Hernandez Lobato, V. Sharmanska, K. Kersting, C. Lampert, N. Quadrianto, in:, Advances in Neural Information Processing Systems, Neural Information Processing Systems, 2014, pp. 837–845.

Majority vote of diverse classifiers for late fusion
E. Morvant, A. Habrard, S. Ayache, in:, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2014, pp. 153–162.

Closed-form approximate CRF training for scalable image segmentation
A. Kolesnikov, M. Guillaumin, V. Ferrari, C. Lampert, in:, D. Fleet, T. Pajdla, B. Schiele, T. Tuytelaars (Eds.), Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2014, pp. 550–565.

CoConut: Co-classification with output space regularization
S. Khamis, C. Lampert, in:, Proceedings of the British Machine Vision Conference 2014, BMVA Press, 2014.

Deep Fisher Kernels – End to end learning of the Fisher Kernel GMM parameters
V. Sydorov, M. Sakurada, C. Lampert, in:, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2014, pp. 1402–1409.

Learning a priori constrained weighted majority votes
A. Bellet, A. Habrard, E. Morvant, M. Sebban, Machine Learning 97 (2014) 129–154.

Adaptation de domaine de vote de majorité par auto-étiquetage non itératif
E. Morvant, in:, Elsevier, 2014, pp. 49–58.

A PAC-Bayesian bound for Lifelong Learning
A. Pentina, C. Lampert, in:, ML Research Press, 2014, pp. 991–999.

Drosophila Embryo Stage Annotation using Label Propagation
T. Kazmar, E. Kvon, A. Stark, C. Lampert, in:, IEEE, 2013.

Learning to rank using privileged information
V. Sharmanska, N. Quadrianto, C. Lampert, in:, IEEE, 2013, pp. 825–832.

Attribute-based classification for zero-shot learning of object categories
C. Lampert, H. Nickisch, S. Harmeling, IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (2013) 453–465.

The supervised IBP: Neighbourhood preserving infinite latent feature models
N. Quadrianto, V. Sharmanska, D. Knowles, Z. Ghahramani, in:, Proceedings of the 29th Conference Uncertainty in Artificial Intelligence, AUAI Press, 2013, pp. 527–536.

Computing the M most probable modes of a graphical model
C. Chen, V. Kolmogorov, Z. Yan, D. Metaxas, C. Lampert, in:, JMLR, 2013, pp. 161–169.

Beyond dataset bias: Multi-task unaligned shared knowledge transfer
T. Tommasi, N. Quadrianto, B. Caputo, C. Lampert, 7724 (2013) 1–15.

Kernel based learning
N. Quadrianto, C. Lampert, in:, W. Dubitzky, O. Wolkenhauer, K. Cho, H. Yokota (Eds.), Encyclopedia of Systems Biology, Springer, 2013, pp. 1069–1069.

Dynamic pruning of factor graphs for maximum marginal prediction
C. Lampert, in:, Neural Information Processing Systems, 2012, pp. 82–90.

Guest editorial: Special issue on structured prediction and inference
M. Blaschko, C. Lampert, International Journal of Computer Vision 99 (2012) 257–258.

Augmented attribute representations
V. Sharmanska, N. Quadrianto, C. Lampert, in:, Springer, 2012, pp. 242–255.

Information theoretic clustering using minimal spanning trees
A. Müller, S. Nowozin, C. Lampert, in:, Springer, 2012, pp. 205–215.

Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components
C. Lampert, J. Peters, Journal of Real-Time Image Processing 7 (2012) 31–41.

Approximating marginals using discrete energy minimization
F. Korc, V. Kolmogorov, C. Lampert, in:, ICML, 2012.

Approximating marginals using discrete energy minimization
F. Korc, V. Kolmogorov, C. Lampert, Approximating Marginals Using Discrete Energy Minimization, IST Austria, 2012.

Multi-modal learning for dynamic tactile sensing
O. Kroemer, C. Lampert, J. Peters, in:, Deutsches Zentrum für Luft und Raumfahrt, 2012.

The most persistent soft-clique in a set of sampled graphs
N. Quadrianto, C. Lampert, C. Chen, in:, Proceedings of the 29th International Conference on Machine Learning, ML Research Press, 2012, pp. 211–218.

Learning anticipation policies for robot table tennis
Z. Wang, C. Lampert, K. Mülling, B. Schölkopf, J. Peters, in:, IEEE, 2011, pp. 332–337.

Semi supervised kernel canonical correlation analysis with application to human fMRI
M. Blaschko, J. Shelton, A. Bartels, C. Lampert, A. Gretton, Pattern Recognition Letters 32 (2011) 1572–1583.

Learning dynamic tactile sensing with robust vision based training
O. Kroemer, C. Lampert, J. Peters, IEEE Transactions on Robotics 27 (2011) 545–557.

Enforcing topological constraints in random field image segmentation
C. Chen, D. Freedman, C. Lampert, Enforcing Topological Constraints in Random Field Image Segmentation, IST Austria, 2011.

Enforcing topological constraints in random field image segmentation
C. Chen, D. Freedman, C. Lampert, in:, CVPR: Computer Vision and Pattern Recognition, IEEE, 2011, pp. 2089–2096.

Maximum margin multi-label structured prediction
C. Lampert, in:, Neural Information Processing Systems, 2011.

Maximum margin multi label structured prediction
C. Lampert, Maximum Margin Multi Label Structured Prediction, Neural Information Processing Systems Foundation, 2011.

Structured learning and prediction in computer vision
S. Nowozin, C. Lampert, Foundations and Trends in Computer Graphics and Vision 6 (2011) 185–365.

Learning multi-view neighborhood preserving projections
N. Quadrianto, C. Lampert, in:, ML Research Press, 2011, pp. 425–432.

Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning
C. Lampert, O. Krömer, in:, Springer, 2010, pp. 566–579.

On parameter learning in CRF-based approaches to object class image segmentation
S. Nowozin, P. Gehler, C. Lampert, in:, Springer, 2010, pp. 98–111.