N. H. Konstantinov and C. Lampert, “Fairness-aware PAC learning from corrupted data,” Journal of Machine Learning Research, vol. 23. ML Research Press, pp. 1–60, 2022.
LibreCat: https://research-explorer.ista.ac.at/record/10802
arXiv: 2102.06004

N. H. Konstantinov and C. Lampert, “On the impossibility of fairness-aware learning from corrupted data,” in Proceedings of Machine Learning Research, 2022, vol. 171, pp. 59–83.
LibreCat: https://research-explorer.ista.ac.at/record/13241
arXiv: 2102.06004

N. H. Konstantinov, “Robustness and fairness in machine learning,” Institute of Science and Technology Austria, 2022.
LibreCat: https://research-explorer.ista.ac.at/record/10799
DOI: 10.15479/at:ista:10799

V. Volhejn and C. Lampert, “Does SGD implicitly optimize for smoothness?,” in 42nd German Conference on Pattern Recognition, Tübingen, Germany, 2021, vol. 12544, pp. 246–259.
LibreCat: https://research-explorer.ista.ac.at/record/9210
DOI: 10.1007/978-3-030-71278-5_18

M. Phuong and C. Lampert, “The inductive bias of ReLU networks on orthogonally separable data,” in 9th International Conference on Learning Representations, Virtual, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/9416

N. H. Konstantinov and C. Lampert, “Fairness through regularization for learning to rank,” arXiv. .
LibreCat: https://research-explorer.ista.ac.at/record/10803
DOI: 10.48550/arXiv.2102.05996
arXiv: 2102.05996

M. Phuong, “Underspecification in deep learning,” Institute of Science and Technology Austria, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/9418
DOI: 10.15479/AT:ISTA:9418

C. Lampert, “Zero-Shot Learning,” in Computer Vision, 2nd ed., K. Ikeuchi, Ed. Cham: Springer, 2021, pp. 1395–1397.
LibreCat: https://research-explorer.ista.ac.at/record/14987
DOI: 10.1007/978-3-030-63416-2_874

T. Anciukevicius, C. Lampert, and P. M. Henderson, “Object-centric image generation with factored depths, locations, and appearances,” arXiv. .
LibreCat: https://research-explorer.ista.ac.at/record/8063
arXiv: 2004.00642

P. M. Henderson and C. Lampert, “Unsupervised object-centric video generation and decomposition in 3D,” in 34th Conference on Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 3106–3117.
LibreCat: https://research-explorer.ista.ac.at/record/8188
arXiv: 2007.06705

P. M. Henderson and V. Ferrari, “Learning single-image 3D reconstruction by generative modelling of shape, pose and shading,” International Journal of Computer Vision, vol. 128. Springer Nature, pp. 835–854, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/6952
DOI: 10.1007/s11263-019-01219-8
WoS: 000491042100002
arXiv: 1901.06447

A. Royer and C. Lampert, “Localizing grouped instances for efficient detection in low-resource scenarios,” in IEEE Winter Conference on Applications of Computer Vision,  Snowmass Village, CO, United States, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/7936
DOI: 10.1109/WACV45572.2020.9093288
arXiv: 2004.12623

A. Royer and C. Lampert, “A flexible selection scheme for minimum-effort transfer learning,” in 2020 IEEE Winter Conference on Applications of Computer Vision, Snowmass Village, CO, United States, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/7937
DOI: 10.1109/WACV45572.2020.9093635
arXiv: 2008.11995

A. Royer et al., “XGAN: Unsupervised image-to-image translation for many-to-many mappings,” in Domain Adaptation for Visual Understanding, R. Singh, M. Vatsa, V. M. Patel, and N. Ratha, Eds. Springer Nature, 2020, pp. 33–49.
LibreCat: https://research-explorer.ista.ac.at/record/8092
DOI: 10.1007/978-3-030-30671-7_3
arXiv: 1711.05139

M. Phuong and C. Lampert, “Functional vs. parametric equivalence of ReLU networks,” in 8th International Conference on Learning Representations, Online, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/7481

N. H. Konstantinov, E. Frantar, D.-A. Alistarh, and C. Lampert, “On the sample complexity of adversarial multi-source PAC learning,” in Proceedings of the 37th International Conference on Machine Learning, Online, 2020, vol. 119, pp. 5416–5425.
LibreCat: https://research-explorer.ista.ac.at/record/8724
arXiv: 2002.10384

A. Royer, “Leveraging structure in Computer Vision tasks for flexible Deep Learning models,” Institute of Science and Technology Austria, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/8390
DOI: 10.15479/AT:ISTA:8390

P. M. Henderson, V. Tsiminaki, and C. Lampert, “Leveraging 2D data to learn textured 3D mesh generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual, 2020, pp. 7498–7507.
LibreCat: https://research-explorer.ista.ac.at/record/8186
DOI: 10.1109/CVPR42600.2020.00752
arXiv: 2004.04180

R. Sun and C. Lampert, “KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications,” International Journal of Computer Vision, vol. 128, no. 4. Springer Nature, pp. 970–995, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/6944
DOI: 10.1007/s11263-019-01232-x
WoS: 000494406800001

K. Kersting, C. Lampert, and C. Rothkopf, Eds., Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt, 1st ed. Wiesbaden: Springer Nature, 2019.
LibreCat: https://research-explorer.ista.ac.at/record/7171
DOI: 10.1007/978-3-658-26763-6

P. Ashok, T. Brázdil, K. Chatterjee, J. Ketínský, C. Lampert, and V. Toman, “Strategy representation by decision trees with linear classifiers,” in 16th International Conference on Quantitative Evaluation of Systems, Glasgow, United Kingdom, 2019, vol. 11785, pp. 109–128.
LibreCat: https://research-explorer.ista.ac.at/record/6942
DOI: 10.1007/978-3-030-30281-8_7
WoS: 000679281300007
arXiv: 1906.08178

Y. Xian, C. Lampert, B. Schiele, and Z. Akata, “Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 9. Institute of Electrical and Electronics Engineers (IEEE), pp. 2251–2265, 2019.
LibreCat: https://research-explorer.ista.ac.at/record/6554
DOI: 10.1109/tpami.2018.2857768
WoS: 000480343900015
arXiv: 1707.00600

M. Phuong and C. Lampert, “Distillation-based training for multi-exit architectures,” in IEEE International Conference on Computer Vision, Seoul, Korea, 2019, vol. 2019–October, pp. 1355–1364.
LibreCat: https://research-explorer.ista.ac.at/record/7479
DOI: 10.1109/ICCV.2019.00144
WoS: 000531438101047

A. Kolesnikov, A. Kuznetsova, C. Lampert, and V. Ferrari, “Detecting visual relationships using box attention,” in Proceedings of the 2019 International Conference on Computer Vision Workshop, Seoul, South Korea, 2019.
LibreCat: https://research-explorer.ista.ac.at/record/7640
DOI: 10.1109/ICCVW.2019.00217
WoS: 000554591601098
arXiv: 1807.02136

M. Phuong and C. Lampert, “Towards understanding knowledge distillation,” in Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, United States, 2019, vol. 97, pp. 5142–5151.
LibreCat: https://research-explorer.ista.ac.at/record/6569

N. H. Konstantinov and C. Lampert, “Robust learning from untrusted sources,” in Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA, 2019, vol. 97, pp. 3488–3498.
LibreCat: https://research-explorer.ista.ac.at/record/6590
arXiv: 1901.10310

R. Sun and C. Lampert, “KS(conf): A light-weight test if a ConvNet operates outside of Its specifications,” presented at the GCPR: Conference on Pattern Recognition, Stuttgart, Germany, 2019, vol. 11269, pp. 244–259.
LibreCat: https://research-explorer.ista.ac.at/record/6482
DOI: 10.1007/978-3-030-12939-2_18
arXiv: 1804.04171

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

A. Kolesnikov, “Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images,” Institute of Science and Technology Austria, 2018.
LibreCat: https://research-explorer.ista.ac.at/record/197
DOI: 10.15479/AT:ISTA:th_1021

H. Ringbauer, A. Kolesnikov, D. Field, and N. H. Barton, “Estimating barriers to gene flow from distorted isolation-by-distance patterns,” Genetics, vol. 208, no. 3. Genetics Society of America, pp. 1231–1245, 2018.
LibreCat: https://research-explorer.ista.ac.at/record/563
DOI: 10.1534/genetics.117.300638
WoS: 000426219600025

T. Darrell, C. Lampert, N. Sebe, Y. Wu, and Y. Yan, “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, vol. 40, no. 5. IEEE, pp. 1029–1031, 2018.
LibreCat: https://research-explorer.ista.ac.at/record/321
DOI: 10.1109/TPAMI.2018.2804998
WoS: 000428901200001

J. Uijlings, K. Konyushkova, C. Lampert, and V. Ferrari, “Learning intelligent dialogs for bounding box annotation,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, United States, 2018, pp. 9175–9184.
LibreCat: https://research-explorer.ista.ac.at/record/10882
DOI: 10.1109/cvpr.2018.00956
WoS: 000457843609036
arXiv: 1712.08087

S. Sahoo, C. Lampert, and G. S. Martius, “Learning equations for extrapolation and control,” in Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, 2018, vol. 80, pp. 4442–4450.
LibreCat: https://research-explorer.ista.ac.at/record/6012
WoS: 000683379204058
arXiv: 1806.07259

I. Kuzborskij and C. Lampert, “Data-dependent stability of stochastic gradient descent,” in Proceedings of the 35 th International Conference on Machine Learning, Stockholm, Sweden, 2018, vol. 80, pp. 2815–2824.
LibreCat: https://research-explorer.ista.ac.at/record/6011
WoS: 000683379202095
arXiv: 1703.01678

D.-A. Alistarh, T. Hoefler, M. Johansson, N. H. Konstantinov, S. Khirirat, and C. Renggli, “The convergence of sparsified gradient methods,” in Advances in Neural Information Processing Systems 31, Montreal, Canada, 2018, vol. Volume 2018, pp. 5973–5983.
LibreCat: https://research-explorer.ista.ac.at/record/6589
WoS: 000461852000047
arXiv: 1809.10505

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

R. Der and G. S. Martius, “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, 2017.
LibreCat: https://research-explorer.ista.ac.at/record/652
DOI: 10.1109/DEVLRN.2016.7846789

R. Der and G. S. Martius, “Self organized behavior generation for musculoskeletal robots,” Frontiers in Neurorobotics, vol. 11, no. MAR. Frontiers Research Foundation, 2017.
LibreCat: https://research-explorer.ista.ac.at/record/658
DOI: 10.3389/fnbot.2017.00008

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

J. Pielorz, M. Prandtstetter, M. Straub, and C. Lampert, “Optimal geospatial volunteer allocation needs realistic distances,” in 2017 IEEE International Conference on Big Data, Boston, MA, United States, 2017, pp. 3760–3763.
LibreCat: https://research-explorer.ista.ac.at/record/750
DOI: 10.1109/BigData.2017.8258375

A. Kolesnikov and C. Lampert, “PixelCNN models with auxiliary variables for natural image modeling,” in 34th International Conference on Machine Learning, Sydney, Australia, 2017, vol. 70, pp. 1905–1914.
LibreCat: https://research-explorer.ista.ac.at/record/1000
WoS: 000683309501102
arXiv: 1612.08185

S. A. Rebuffi, A. Kolesnikov, G. Sperl, and C. Lampert, CaRL: Incremental classifier and representation learning,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 5533–5542.
LibreCat: https://research-explorer.ista.ac.at/record/998
DOI: 10.1109/CVPR.2017.587
WoS: 000418371405066

A. Royer, A. Kolesnikov, and C. Lampert, “Probabilistic image colorization,” presented at the BMVC: British Machine Vision Conference, London, United Kingdom, 2017, p. 85.1-85.12.
LibreCat: https://research-explorer.ista.ac.at/record/911
DOI: 10.5244/c.31.85
arXiv: 1705.04258

A. Zimin and C. Lampert, “Learning theory for conditional risk minimization,” presented at the AISTATS: Artificial Intelligence and Statistics, Fort Lauderdale, FL, United States, 2017, vol. 54, pp. 213–222.
LibreCat: https://research-explorer.ista.ac.at/record/1108
WoS: 000509368500024

A. Pentina and C. Lampert, “Multi-task learning with labeled and unlabeled tasks,” presented at the ICML: International Conference on Machine Learning, Sydney, Australia, 2017, vol. 70, pp. 2807–2816.
LibreCat: https://research-explorer.ista.ac.at/record/999
WoS: 000683309502093

A. Pentina and R. Urner, “Lifelong learning with weighted majority votes,” presented at the NIPS: Neural Information Processing Systems, Barcelona, Spain, 2016, vol. 29, pp. 3619–3627.
LibreCat: https://research-explorer.ista.ac.at/record/1098

A. Kolesnikov and C. Lampert, “Improving weakly-supervised object localization by micro-annotation,” in Proceedings of the British Machine Vision Conference 2016, York, United Kingdom, 2016, vol. 2016–September, p. 92.1-92.12.
LibreCat: https://research-explorer.ista.ac.at/record/1102
DOI: 10.5244/C.30.92

G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm,” presented at the IEEE RSJ International Conference on Intelligent Robots and Systems IROS , Daejeon, Korea, 2016, vol. 2016–November.
LibreCat: https://research-explorer.ista.ac.at/record/1214
DOI: 10.1109/IROS.2016.7759138

A. Kolesnikov and C. Lampert, “Seed, expand and constrain: Three principles for weakly-supervised image segmentation,” presented at the ECCV: European Conference on Computer Vision, Amsterdam, The Netherlands, 2016, vol. 9908, pp. 695–711.
LibreCat: https://research-explorer.ista.ac.at/record/1369
DOI: 10.1007/978-3-319-46493-0_42

J. Pielorz and C. Lampert, “Optimal geospatial allocation of volunteers for crisis management,” presented at the ICT-DM: Information and Communication Technologies for Disaster Management, Rennes, France, 2016.
LibreCat: https://research-explorer.ista.ac.at/record/1707
DOI: 10.1109/ICT-DM.2015.7402041

G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Self-organized control of an tendon driven arm by differential extrinsic plasticity,” in Proceedings of the Artificial Life Conference 2016, Cancun, Mexico, 2016, vol. 28, pp. 142–143.
LibreCat: https://research-explorer.ista.ac.at/record/8094
DOI: 10.7551/978-0-262-33936-0-ch029

A. Pentina, “Theoretical foundations of multi-task lifelong learning,” Institute of Science and Technology Austria, 2016.
LibreCat: https://research-explorer.ista.ac.at/record/1126
DOI: 10.15479/AT:ISTA:TH_776

A. Pentina and C. Lampert, “Lifelong learning with non-i.i.d. tasks,” presented at the NIPS: Neural Information Processing Systems, Montreal, Canada, 2015, vol. 2015, pp. 1540–1548.
LibreCat: https://research-explorer.ista.ac.at/record/1425

W. Xia, C. Domokos, J. Xiong, L. Cheong, and S. Yan, “Segmentation over detection via optimal sparse reconstructions,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 8. IEEE, pp. 1295–1308, 2015.
LibreCat: https://research-explorer.ista.ac.at/record/1533
DOI: 10.1109/TCSVT.2014.2379972

R. Der and G. S. Martius, “Novel plasticity rule can explain the development of sensorimotor intelligence,” PNAS, vol. 112, no. 45. National Academy of Sciences, pp. 24–32, 2015.
LibreCat: https://research-explorer.ista.ac.at/record/1570
DOI: 10.1073/pnas.1508400112
PMID: 26504200

A. Pentina and S. Ben David, “Multi-task and lifelong learning of kernels,” presented at the ALT: Algorithmic Learning Theory, Banff, AB, Canada, 2015, vol. 9355, pp. 194–208.
LibreCat: https://research-explorer.ista.ac.at/record/1706
DOI: 10.1007/978-3-319-24486-0_13

N. Shah, V. Kolmogorov, and C. Lampert, “A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, USA, 2015, pp. 2737–2745.
LibreCat: https://research-explorer.ista.ac.at/record/1859
DOI: 10.1109/CVPR.2015.7298890

A. Royer and C. Lampert, “Classifier adaptation at prediction time,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States, 2015, pp. 1401–1409.
LibreCat: https://research-explorer.ista.ac.at/record/1860
DOI: 10.1109/CVPR.2015.7298746

C. Lampert, “Predicting the future behavior of a time-varying probability distribution,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States, 2015, pp. 942–950.
LibreCat: https://research-explorer.ista.ac.at/record/1858
DOI: 10.1109/CVPR.2015.7298696
arXiv: 1406.5362

A. Pentina, V. Sharmanska, and C. Lampert, “Curriculum learning of multiple tasks,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States, 2015, pp. 5492–5500.
LibreCat: https://research-explorer.ista.ac.at/record/1857
DOI: 10.1109/CVPR.2015.7299188

G. S. Martius and E. Olbrich, “Quantifying self-organizing behavior of autonomous robots,” in Proceedings of the 13th European Conference on Artificial Life, York, United Kingdom, 2015, p. 78.
LibreCat: https://research-explorer.ista.ac.at/record/12881
DOI: 10.7551/978-0-262-33027-5-ch018

V. Sharmanska, “Learning with attributes for object recognition: Parametric and non-parametrics views,” Institute of Science and Technology Austria, 2015.
LibreCat: https://research-explorer.ista.ac.at/record/1401
DOI: 10.15479/at:ista:1401

G. S. Martius and E. Olbrich, “Quantifying emergent behavior of autonomous robots,” Entropy, vol. 17, no. 10. MDPI, pp. 7266–7297, 2015.
LibreCat: https://research-explorer.ista.ac.at/record/1655
DOI: 10.3390/e17107266

K. Muelling, O. Kroemer, C. Lampert, and B. Schölkopf, “Movement templates for learning of hitting and batting,” in Learning Motor Skills, vol. 97, J. Kober and J. Peters, Eds. Springer, 2014, pp. 69–82.
LibreCat: https://research-explorer.ista.ac.at/record/1829
DOI: 10.1007/978-3-319-03194-1_3

D. Hernandez Lobato, V. Sharmanska, K. Kersting, C. Lampert, and N. Quadrianto, “Mind the nuisance: Gaussian process classification using privileged noise,” in Advances in Neural Information Processing Systems, Montreal, Canada, 2014, vol. 1, no. January, pp. 837–845.
LibreCat: https://research-explorer.ista.ac.at/record/2033

E. Morvant, A. Habrard, and S. Ayache, “Majority vote of diverse classifiers for late fusion,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Joensuu, Finland, 2014, vol. 8621, pp. 153–162.
LibreCat: https://research-explorer.ista.ac.at/record/2057
DOI: 10.1007/978-3-662-44415-3_16
arXiv: 1404.7796

A. Kolesnikov, M. Guillaumin, V. Ferrari, and C. Lampert, “Closed-form approximate CRF training for scalable image segmentation,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Zurich, Switzerland, 2014, vol. 8691, no. PART 3, pp. 550–565.
LibreCat: https://research-explorer.ista.ac.at/record/2171
DOI: 10.1007/978-3-319-10578-9_36

S. Khamis and C. Lampert, “CoConut: Co-classification with output space regularization,” in Proceedings of the British Machine Vision Conference 2014, Nottingham, UK, 2014.
LibreCat: https://research-explorer.ista.ac.at/record/2173

V. Sydorov, M. Sakurada, and C. Lampert, “Deep Fisher Kernels – End to end learning of the Fisher Kernel GMM parameters,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014, pp. 1402–1409.
LibreCat: https://research-explorer.ista.ac.at/record/2172
DOI: 10.1109/CVPR.2014.182

A. Bellet, A. Habrard, E. Morvant, and M. Sebban, “Learning a priori constrained weighted majority votes,” Machine Learning, vol. 97, no. 1–2. Springer, pp. 129–154, 2014.
LibreCat: https://research-explorer.ista.ac.at/record/2180
DOI: 10.1007/s10994-014-5462-z

E. Morvant, “Adaptation de domaine de vote de majorité par auto-étiquetage non itératif,” presented at the CAP: Conférence Francophone sur l’Apprentissage Automatique (Machine Learning French Conference), Saint-Etienne, France, 2014, vol. 1, pp. 49–58.
LibreCat: https://research-explorer.ista.ac.at/record/2189

A. Pentina and C. Lampert, “A PAC-Bayesian bound for Lifelong Learning,” presented at the ICML: International Conference on Machine Learning, Beijing, China, 2014, vol. 32, pp. 991–999.
LibreCat: https://research-explorer.ista.ac.at/record/2160

T. Kazmar, E. Kvon, A. Stark, and C. Lampert, “Drosophila Embryo Stage Annotation using Label Propagation,” presented at the ICCV: International Conference on Computer Vision, Sydney, Australia, 2013.
LibreCat: https://research-explorer.ista.ac.at/record/2294
DOI: 10.1109/ICCV.2013.139

V. Sharmanska, N. Quadrianto, and C. Lampert, “Learning to rank using privileged information,” presented at the ICCV: International Conference on Computer Vision, Sydney, Australia, 2013, pp. 825–832.
LibreCat: https://research-explorer.ista.ac.at/record/2293
DOI: 10.1109/ICCV.2013.107

C. Lampert, H. Nickisch, and S. Harmeling, “Attribute-based classification for zero-shot learning of object categories,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 3. IEEE, pp. 453–465, 2013.
LibreCat: https://research-explorer.ista.ac.at/record/2516
DOI: 10.1109/TPAMI.2013.140

N. Quadrianto, V. Sharmanska, D. Knowles, and Z. Ghahramani, “The supervised IBP: Neighbourhood preserving infinite latent feature models,” in Proceedings of the 29th conference uncertainty in Artificial Intelligence, Bellevue, WA, United States, 2013, pp. 527–536.
LibreCat: https://research-explorer.ista.ac.at/record/2520

C. Chen, V. Kolmogorov, Z. Yan, D. Metaxas, and C. Lampert, “Computing the M most probable modes of a graphical model,” presented at the  AISTATS: Conference on Uncertainty in Artificial Intelligence, Scottsdale, AZ, United States, 2013, vol. 31, pp. 161–169.
LibreCat: https://research-explorer.ista.ac.at/record/2901

T. Tommasi, N. Quadrianto, B. Caputo, and C. Lampert, “Beyond dataset bias: Multi-task unaligned shared knowledge transfer,” vol. 7724. Springer, pp. 1–15, 2013.
LibreCat: https://research-explorer.ista.ac.at/record/2948
DOI: 10.1007/978-3-642-37331-2_1

N. Quadrianto and C. Lampert, “Kernel based learning,” in Encyclopedia of Systems Biology, vol. 3, W. Dubitzky, O. Wolkenhauer, K. Cho, and H. Yokota, Eds. Springer, 2013, pp. 1069–1069.
LibreCat: https://research-explorer.ista.ac.at/record/3321
DOI: 10.1007/978-1-4419-9863-7_604

C. Lampert, “Dynamic pruning of factor graphs for maximum marginal prediction,” presented at the NIPS: Neural Information Processing Systems, Lake Tahoe, NV, United States, 2012, vol. 1, pp. 82–90.
LibreCat: https://research-explorer.ista.ac.at/record/2825

M. Blaschko and C. Lampert, “Guest editorial: Special issue on structured prediction and inference,” International Journal of Computer Vision, vol. 99, no. 3. Springer, pp. 257–258, 2012.
LibreCat: https://research-explorer.ista.ac.at/record/3164
DOI: 10.1007/s11263-012-0530-y

V. Sharmanska, N. Quadrianto, and C. Lampert, “Augmented attribute representations,” presented at the ECCV: European Conference on Computer Vision, Florence, Italy, 2012, vol. 7576, no. PART 5, pp. 242–255.
LibreCat: https://research-explorer.ista.ac.at/record/3125
DOI: 10.1007/978-3-642-33715-4_18

A. Müller, S. Nowozin, and C. Lampert, “Information theoretic clustering using minimal spanning trees,” presented at the DAGM: German Association For Pattern Recognition, Graz, Austria, 2012, vol. 7476, pp. 205–215.
LibreCat: https://research-explorer.ista.ac.at/record/3126
DOI: 10.1007/978-3-642-32717-9_21

C. Lampert and J. Peters, “Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components,” Journal of Real-Time Image Processing, vol. 7, no. 1. Springer, pp. 31–41, 2012.
LibreCat: https://research-explorer.ista.ac.at/record/3248
DOI: 10.1007/s11554-010-0168-3

F. Korc, V. Kolmogorov, and C. Lampert, “Approximating marginals using discrete energy minimization,” presented at the ICML: International Conference on Machine Learning, Edinburgh, Scotland, 2012.
LibreCat: https://research-explorer.ista.ac.at/record/3124

F. Korc, V. Kolmogorov, and C. Lampert, Approximating marginals using discrete energy minimization. IST Austria, 2012.
LibreCat: https://research-explorer.ista.ac.at/record/5396
DOI: 10.15479/AT:IST-2012-0003

O. Kroemer, C. Lampert, and J. Peters, “Multi-modal learning for dynamic tactile sensing,” 2012.
LibreCat: https://research-explorer.ista.ac.at/record/2915

N. Quadrianto, C. Lampert, and C. Chen, “The most persistent soft-clique in a set of sampled graphs,” in Proceedings of the 29th International Conference on Machine Learning, Edinburgh, United Kingdom, 2012, pp. 211–218.
LibreCat: https://research-explorer.ista.ac.at/record/3127

Z. Wang, C. Lampert, K. Mülling, B. Schölkopf, and J. Peters, “Learning anticipation policies for robot table tennis,” presented at the IROS: RSJ International Conference on Intelligent Robots and Systems, San Francisco, USA, 2011, pp. 332–337.
LibreCat: https://research-explorer.ista.ac.at/record/3337
DOI: 10.1109/IROS.2011.6094892

M. Blaschko, J. Shelton, A. Bartels, C. Lampert, and A. Gretton, “Semi supervised kernel canonical correlation analysis with application to human fMRI,” Pattern Recognition Letters, vol. 32, no. 11. Elsevier, pp. 1572–1583, 2011.
LibreCat: https://research-explorer.ista.ac.at/record/3389
DOI: 10.1016/j.patrec.2011.02.011

O. Kroemer, C. Lampert, and J. Peters, “Learning dynamic tactile sensing with robust vision based training,” IEEE Transactions on Robotics, vol. 27, no. 3. IEEE, pp. 545–557, 2011.
LibreCat: https://research-explorer.ista.ac.at/record/3382
DOI: 10.1109/TRO.2011.2121130

C. Chen, D. Freedman, and C. Lampert, Enforcing topological constraints in random field image segmentation. IST Austria, 2011.
LibreCat: https://research-explorer.ista.ac.at/record/5386
DOI: 10.15479/AT:IST-2011-0002

C. Chen, D. Freedman, and C. Lampert, “Enforcing topological constraints in random field image segmentation,” in CVPR: Computer Vision and Pattern Recognition, Colorado Springs, CO, United States, 2011, pp. 2089–2096.
LibreCat: https://research-explorer.ista.ac.at/record/3336
DOI: 10.1109/CVPR.2011.5995503

C. Lampert, “Maximum margin multi-label structured prediction,” presented at the NIPS: Neural Information Processing Systems, Granada, Spain, 2011.
LibreCat: https://research-explorer.ista.ac.at/record/3163

C. Lampert, Maximum margin multi label structured prediction. Neural Information Processing Systems Foundation, 2011.
LibreCat: https://research-explorer.ista.ac.at/record/3322

S. Nowozin and C. Lampert, “Structured learning and prediction in computer vision,” Foundations and Trends in Computer Graphics and Vision, vol. 6, no. 3–4. Now Publishers, pp. 185–365, 2011.
LibreCat: https://research-explorer.ista.ac.at/record/3320
DOI: 10.1561/0600000033

N. Quadrianto and C. Lampert, “Learning multi-view neighborhood preserving projections,” presented at the ICML: International Conference on Machine Learning, Bellevue, United States, 2011, pp. 425–432.
LibreCat: https://research-explorer.ista.ac.at/record/3319

C. Lampert and O. Krömer, “Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning,” presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece, 2010, vol. 6312, pp. 566–579.
LibreCat: https://research-explorer.ista.ac.at/record/3794
DOI: 10.1007/978-3-642-15552-9_41

S. Nowozin, P. Gehler, and C. Lampert, “On parameter learning in CRF-based approaches to object class image segmentation,” presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece, 2010, vol. 6316, pp. 98–111.
LibreCat: https://research-explorer.ista.ac.at/record/3793
DOI: 10.1007/978-3-642-15567-3_8