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

Royer A, Lampert C. Localizing grouped instances for efficient detection in low-resource scenarios. In: IEEE Winter Conference on Applications of Computer Vision. IEEE; 2020. doi:10.1109/WACV45572.2020.9093288
LibreCat: https://research-explorer.ista.ac.at/record/7936
DOI: 10.1109/WACV45572.2020.9093288
arXiv: 2004.12623

Royer A, Lampert C. A flexible selection scheme for minimum-effort transfer learning. In: 2020 IEEE Winter Conference on Applications of Computer Vision. IEEE; 2020. doi:10.1109/WACV45572.2020.9093635
LibreCat: https://research-explorer.ista.ac.at/record/7937
DOI: 10.1109/WACV45572.2020.9093635
arXiv: 2008.11995

Royer A, Bousmalis K, Gouws S, et al. XGAN: Unsupervised image-to-image translation for many-to-many mappings. In: Singh R, Vatsa M, Patel VM, Ratha N, eds. Domain Adaptation for Visual Understanding. Springer Nature; 2020:33-49. doi:10.1007/978-3-030-30671-7_3
LibreCat: https://research-explorer.ista.ac.at/record/8092
DOI: 10.1007/978-3-030-30671-7_3
arXiv: 1711.05139

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

Konstantinov NH, Frantar E, Alistarh D-A, Lampert C. On the sample complexity of adversarial multi-source PAC learning. In: Proceedings of the 37th International Conference on Machine Learning. Vol 119. ML Research Press; 2020:5416-5425.
LibreCat: https://research-explorer.ista.ac.at/record/8724
arXiv: 2002.10384

Royer A. Leveraging structure in Computer Vision tasks for flexible Deep Learning models. 2020. doi:10.15479/AT:ISTA:8390
LibreCat: https://research-explorer.ista.ac.at/record/8390
DOI: 10.15479/AT:ISTA:8390

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

Sun R, Lampert C. KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications. International Journal of Computer Vision. 2020;128(4):970-995. doi:10.1007/s11263-019-01232-x
LibreCat: https://research-explorer.ista.ac.at/record/6944
DOI: 10.1007/s11263-019-01232-x
WoS: 000494406800001

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

Ashok P, Brázdil T, Chatterjee K, Ketínský J, Lampert C, Toman V. Strategy representation by decision trees with linear classifiers. In: 16th International Conference on Quantitative Evaluation of Systems. Vol 11785. Springer Nature; 2019:109-128. doi:10.1007/978-3-030-30281-8_7
LibreCat: https://research-explorer.ista.ac.at/record/6942
DOI: 10.1007/978-3-030-30281-8_7
WoS: 000679281300007
arXiv: 1906.08178

Xian Y, Lampert C, Schiele B, Akata Z. Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2019;41(9):2251-2265. doi:10.1109/tpami.2018.2857768
LibreCat: https://research-explorer.ista.ac.at/record/6554
DOI: 10.1109/tpami.2018.2857768
WoS: 000480343900015
arXiv: 1707.00600

Phuong M, Lampert C. Distillation-based training for multi-exit architectures. In: IEEE International Conference on Computer Vision. Vol 2019-October. IEEE; 2019:1355-1364. doi:10.1109/ICCV.2019.00144
LibreCat: https://research-explorer.ista.ac.at/record/7479
DOI: 10.1109/ICCV.2019.00144
WoS: 000531438101047

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

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.
LibreCat: https://research-explorer.ista.ac.at/record/6569

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.
LibreCat: https://research-explorer.ista.ac.at/record/6590
arXiv: 1901.10310

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
LibreCat: https://research-explorer.ista.ac.at/record/6482
DOI: 10.1007/978-3-030-12939-2_18
arXiv: 1804.04171

Zimin A. Learning from dependent data. 2018. doi:10.15479/AT:ISTA:TH1048
LibreCat: https://research-explorer.ista.ac.at/record/68
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
LibreCat: https://research-explorer.ista.ac.at/record/197
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
LibreCat: https://research-explorer.ista.ac.at/record/563
DOI: 10.1534/genetics.117.300638
WoS: 000426219600025

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
LibreCat: https://research-explorer.ista.ac.at/record/321
DOI: 10.1109/TPAMI.2018.2804998
WoS: 000428901200001

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
LibreCat: https://research-explorer.ista.ac.at/record/10882
DOI: 10.1109/cvpr.2018.00956
WoS: 000457843609036
arXiv: 1712.08087

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.
LibreCat: https://research-explorer.ista.ac.at/record/6012
WoS: 000683379204058
arXiv: 1806.07259

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.
LibreCat: https://research-explorer.ista.ac.at/record/6011
WoS: 000683379202095
arXiv: 1703.01678

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.
LibreCat: https://research-explorer.ista.ac.at/record/6589
WoS: 000461852000047
arXiv: 1809.10505

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

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
LibreCat: https://research-explorer.ista.ac.at/record/652
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
LibreCat: https://research-explorer.ista.ac.at/record/658
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.
LibreCat: https://research-explorer.ista.ac.at/record/6841
arXiv: 1610.02995

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
LibreCat: https://research-explorer.ista.ac.at/record/750
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.
LibreCat: https://research-explorer.ista.ac.at/record/1000
WoS: 000683309501102
arXiv: 1612.08185

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
LibreCat: https://research-explorer.ista.ac.at/record/998
DOI: 10.1109/CVPR.2017.587
WoS: 000418371405066

Royer A, Kolesnikov A, Lampert C. Probabilistic image colorization. In: BMVA Press; 2017:85.1-85.12. doi:10.5244/c.31.85
LibreCat: https://research-explorer.ista.ac.at/record/911
DOI: 10.5244/c.31.85
arXiv: 1705.04258

Zimin A, Lampert C. Learning theory for conditional risk minimization. In: Vol 54. ML Research Press; 2017:213-222.
LibreCat: https://research-explorer.ista.ac.at/record/1108
WoS: 000509368500024

Pentina A, Lampert C. Multi-task learning with labeled and unlabeled tasks. In: Vol 70. ML Research Press; 2017:2807-2816.
LibreCat: https://research-explorer.ista.ac.at/record/999
WoS: 000683309502093

Pentina A, Urner R. Lifelong learning with weighted majority votes. In: Vol 29. Neural Information Processing Systems; 2016:3619-3627.
LibreCat: https://research-explorer.ista.ac.at/record/1098

Kolesnikov A, Lampert C. Improving weakly-supervised object localization by micro-annotation. In: Proceedings of the British Machine Vision Conference 2016. Vol 2016-September. BMVA Press; 2016:92.1-92.12. doi:10.5244/C.30.92
LibreCat: https://research-explorer.ista.ac.at/record/1102
DOI: 10.5244/C.30.92

Martius GS, Hostettler R, Knoll A, Der R. Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm. In: Vol 2016-November. IEEE; 2016. doi:10.1109/IROS.2016.7759138
LibreCat: https://research-explorer.ista.ac.at/record/1214
DOI: 10.1109/IROS.2016.7759138

Kolesnikov A, Lampert C. Seed, expand and constrain: Three principles for weakly-supervised image segmentation. In: Vol 9908. Springer; 2016:695-711. doi:10.1007/978-3-319-46493-0_42
LibreCat: https://research-explorer.ista.ac.at/record/1369
DOI: 10.1007/978-3-319-46493-0_42

Pielorz J, Lampert C. Optimal geospatial allocation of volunteers for crisis management. In: IEEE; 2016. doi:10.1109/ICT-DM.2015.7402041
LibreCat: https://research-explorer.ista.ac.at/record/1707
DOI: 10.1109/ICT-DM.2015.7402041

Martius GS, Hostettler R, Knoll A, Der R. Self-organized control of an tendon driven arm by differential extrinsic plasticity. In: Proceedings of the Artificial Life Conference 2016. Vol 28. MIT Press; 2016:142-143. doi:10.7551/978-0-262-33936-0-ch029
LibreCat: https://research-explorer.ista.ac.at/record/8094
DOI: 10.7551/978-0-262-33936-0-ch029

Pentina A. Theoretical foundations of multi-task lifelong learning. 2016. doi:10.15479/AT:ISTA:TH_776
LibreCat: https://research-explorer.ista.ac.at/record/1126
DOI: 10.15479/AT:ISTA:TH_776

Pentina A, Lampert C. Lifelong learning with non-i.i.d. tasks. In: Vol 2015. Neural Information Processing Systems; 2015:1540-1548.
LibreCat: https://research-explorer.ista.ac.at/record/1425

Xia W, Domokos C, Xiong J, Cheong L, Yan S. Segmentation over detection via optimal sparse reconstructions. IEEE Transactions on Circuits and Systems for Video Technology. 2015;25(8):1295-1308. doi:10.1109/TCSVT.2014.2379972
LibreCat: https://research-explorer.ista.ac.at/record/1533
DOI: 10.1109/TCSVT.2014.2379972

Der R, Martius GS. Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. 2015;112(45):24-32. doi:10.1073/pnas.1508400112
LibreCat: https://research-explorer.ista.ac.at/record/1570
DOI: 10.1073/pnas.1508400112
PMID: 26504200

Pentina A, Ben David S. Multi-task and lifelong learning of kernels. In: Vol 9355. Springer; 2015:194-208. doi:10.1007/978-3-319-24486-0_13
LibreCat: https://research-explorer.ista.ac.at/record/1706
DOI: 10.1007/978-3-319-24486-0_13

Shah N, Kolmogorov V, Lampert C. A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle. In: IEEE; 2015:2737-2745. doi:10.1109/CVPR.2015.7298890
LibreCat: https://research-explorer.ista.ac.at/record/1859
DOI: 10.1109/CVPR.2015.7298890

Royer A, Lampert C. Classifier adaptation at prediction time. In: IEEE; 2015:1401-1409. doi:10.1109/CVPR.2015.7298746
LibreCat: https://research-explorer.ista.ac.at/record/1860
DOI: 10.1109/CVPR.2015.7298746

Lampert C. Predicting the future behavior of a time-varying probability distribution. In: IEEE; 2015:942-950. doi:10.1109/CVPR.2015.7298696
LibreCat: https://research-explorer.ista.ac.at/record/1858
DOI: 10.1109/CVPR.2015.7298696
arXiv: 1406.5362

Pentina A, Sharmanska V, Lampert C. Curriculum learning of multiple tasks. In: IEEE; 2015:5492-5500. doi:10.1109/CVPR.2015.7299188
LibreCat: https://research-explorer.ista.ac.at/record/1857
DOI: 10.1109/CVPR.2015.7299188

Martius GS, Olbrich E. Quantifying self-organizing behavior of autonomous robots. In: Proceedings of the 13th European Conference on Artificial Life. MIT Press; 2015:78. doi:10.7551/978-0-262-33027-5-ch018
LibreCat: https://research-explorer.ista.ac.at/record/12881
DOI: 10.7551/978-0-262-33027-5-ch018

Sharmanska V. Learning with attributes for object recognition: Parametric and non-parametrics views. 2015. doi:10.15479/at:ista:1401
LibreCat: https://research-explorer.ista.ac.at/record/1401
DOI: 10.15479/at:ista:1401

Martius GS, Olbrich E. Quantifying emergent behavior of autonomous robots. Entropy. 2015;17(10):7266-7297. doi:10.3390/e17107266
LibreCat: https://research-explorer.ista.ac.at/record/1655
DOI: 10.3390/e17107266

Muelling K, Kroemer O, Lampert C, Schölkopf B. Movement templates for learning of hitting and batting. In: Kober J, Peters J, eds. Learning Motor Skills. Vol 97. From Algorithms to Robot Experiments. Springer; 2014:69-82. doi:10.1007/978-3-319-03194-1_3
LibreCat: https://research-explorer.ista.ac.at/record/1829
DOI: 10.1007/978-3-319-03194-1_3

Hernandez Lobato D, Sharmanska V, Kersting K, Lampert C, Quadrianto N. Mind the nuisance: Gaussian process classification using privileged noise. In: Advances in Neural Information Processing Systems. Vol 1. Neural Information Processing Systems; 2014:837-845.
LibreCat: https://research-explorer.ista.ac.at/record/2033

Morvant E, Habrard A, Ayache S. 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). Vol 8621. Springer; 2014:153-162. doi:10.1007/978-3-662-44415-3_16
LibreCat: https://research-explorer.ista.ac.at/record/2057
DOI: 10.1007/978-3-662-44415-3_16
arXiv: 1404.7796

Kolesnikov A, Guillaumin M, Ferrari V, Lampert C. Closed-form approximate CRF training for scalable image segmentation. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T, eds. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol 8691. Springer; 2014:550-565. doi:10.1007/978-3-319-10578-9_36
LibreCat: https://research-explorer.ista.ac.at/record/2171
DOI: 10.1007/978-3-319-10578-9_36

Khamis S, Lampert C. CoConut: Co-classification with output space regularization. In: Proceedings of the British Machine Vision Conference 2014. BMVA Press; 2014.
LibreCat: https://research-explorer.ista.ac.at/record/2173

Sydorov V, Sakurada M, Lampert C. 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. IEEE; 2014:1402-1409. doi:10.1109/CVPR.2014.182
LibreCat: https://research-explorer.ista.ac.at/record/2172
DOI: 10.1109/CVPR.2014.182

Bellet A, Habrard A, Morvant E, Sebban M. Learning a priori constrained weighted majority votes. Machine Learning. 2014;97(1-2):129-154. doi:10.1007/s10994-014-5462-z
LibreCat: https://research-explorer.ista.ac.at/record/2180
DOI: 10.1007/s10994-014-5462-z

Morvant E. Adaptation de domaine de vote de majorité par auto-étiquetage non itératif. In: Vol 1. Elsevier; 2014:49-58.
LibreCat: https://research-explorer.ista.ac.at/record/2189

Pentina A, Lampert C. A PAC-Bayesian bound for Lifelong Learning. In: Vol 32. ML Research Press; 2014:991-999.
LibreCat: https://research-explorer.ista.ac.at/record/2160

Kazmar T, Kvon E, Stark A, Lampert C. Drosophila Embryo Stage Annotation using Label Propagation. In: IEEE; 2013. doi:10.1109/ICCV.2013.139
LibreCat: https://research-explorer.ista.ac.at/record/2294
DOI: 10.1109/ICCV.2013.139

Sharmanska V, Quadrianto N, Lampert C. Learning to rank using privileged information. In: IEEE; 2013:825-832. doi:10.1109/ICCV.2013.107
LibreCat: https://research-explorer.ista.ac.at/record/2293
DOI: 10.1109/ICCV.2013.107

Lampert C, Nickisch H, Harmeling S. Attribute-based classification for zero-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2013;36(3):453-465. doi:10.1109/TPAMI.2013.140
LibreCat: https://research-explorer.ista.ac.at/record/2516
DOI: 10.1109/TPAMI.2013.140

Quadrianto N, Sharmanska V, Knowles D, Ghahramani Z. The supervised IBP: Neighbourhood preserving infinite latent feature models. In: Proceedings of the 29th Conference Uncertainty in Artificial Intelligence. AUAI Press; 2013:527-536.
LibreCat: https://research-explorer.ista.ac.at/record/2520

Chen C, Kolmogorov V, Yan Z, Metaxas D, Lampert C. Computing the M most probable modes of a graphical model. In: Vol 31. JMLR; 2013:161-169.
LibreCat: https://research-explorer.ista.ac.at/record/2901

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

Quadrianto N, Lampert C. Kernel based learning. In: Dubitzky W, Wolkenhauer O, Cho K, Yokota H, eds. Encyclopedia of Systems Biology. Vol 3. Springer; 2013:1069-1069. doi:10.1007/978-1-4419-9863-7_604
LibreCat: https://research-explorer.ista.ac.at/record/3321
DOI: 10.1007/978-1-4419-9863-7_604

Lampert C. Dynamic pruning of factor graphs for maximum marginal prediction. In: Vol 1. Neural Information Processing Systems; 2012:82-90.
LibreCat: https://research-explorer.ista.ac.at/record/2825

Blaschko M, Lampert C. Guest editorial: Special issue on structured prediction and inference. International Journal of Computer Vision. 2012;99(3):257-258. doi:10.1007/s11263-012-0530-y
LibreCat: https://research-explorer.ista.ac.at/record/3164
DOI: 10.1007/s11263-012-0530-y

Sharmanska V, Quadrianto N, Lampert C. Augmented attribute representations. In: Vol 7576. Springer; 2012:242-255. doi:10.1007/978-3-642-33715-4_18
LibreCat: https://research-explorer.ista.ac.at/record/3125
DOI: 10.1007/978-3-642-33715-4_18

Müller A, Nowozin S, Lampert C. Information theoretic clustering using minimal spanning trees. In: Vol 7476. Springer; 2012:205-215. doi:10.1007/978-3-642-32717-9_21
LibreCat: https://research-explorer.ista.ac.at/record/3126
DOI: 10.1007/978-3-642-32717-9_21

Lampert C, Peters J. Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components. Journal of Real-Time Image Processing. 2012;7(1):31-41. doi:10.1007/s11554-010-0168-3
LibreCat: https://research-explorer.ista.ac.at/record/3248
DOI: 10.1007/s11554-010-0168-3

Korc F, Kolmogorov V, Lampert C. Approximating marginals using discrete energy minimization. In: ICML; 2012.
LibreCat: https://research-explorer.ista.ac.at/record/3124

Korc F, Kolmogorov V, Lampert C. Approximating Marginals Using Discrete Energy Minimization. IST Austria; 2012. doi:10.15479/AT:IST-2012-0003
LibreCat: https://research-explorer.ista.ac.at/record/5396
DOI: 10.15479/AT:IST-2012-0003

Kroemer O, Lampert C, Peters J. Multi-modal learning for dynamic tactile sensing. In: Deutsches Zentrum für Luft und Raumfahrt; 2012.
LibreCat: https://research-explorer.ista.ac.at/record/2915

Quadrianto N, Lampert C, Chen C. The most persistent soft-clique in a set of sampled graphs. In: Proceedings of the 29th International Conference on Machine Learning. ML Research Press; 2012:211-218.
LibreCat: https://research-explorer.ista.ac.at/record/3127

Wang Z, Lampert C, Mülling K, Schölkopf B, Peters J. Learning anticipation policies for robot table tennis. In: IEEE; 2011:332-337. doi:10.1109/IROS.2011.6094892
LibreCat: https://research-explorer.ista.ac.at/record/3337
DOI: 10.1109/IROS.2011.6094892

Blaschko M, Shelton J, Bartels A, Lampert C, Gretton A. Semi supervised kernel canonical correlation analysis with application to human fMRI. Pattern Recognition Letters. 2011;32(11):1572-1583. doi:10.1016/j.patrec.2011.02.011
LibreCat: https://research-explorer.ista.ac.at/record/3389
DOI: 10.1016/j.patrec.2011.02.011

Kroemer O, Lampert C, Peters J. Learning dynamic tactile sensing with robust vision based training. IEEE Transactions on Robotics. 2011;27(3):545-557. doi:10.1109/TRO.2011.2121130
LibreCat: https://research-explorer.ista.ac.at/record/3382
DOI: 10.1109/TRO.2011.2121130

Chen C, Freedman D, Lampert C. Enforcing Topological Constraints in Random Field Image Segmentation. IST Austria; 2011. doi:10.15479/AT:IST-2011-0002
LibreCat: https://research-explorer.ista.ac.at/record/5386
DOI: 10.15479/AT:IST-2011-0002

Chen C, Freedman D, Lampert C. Enforcing topological constraints in random field image segmentation. In: CVPR: Computer Vision and Pattern Recognition. IEEE; 2011:2089-2096. doi:10.1109/CVPR.2011.5995503
LibreCat: https://research-explorer.ista.ac.at/record/3336
DOI: 10.1109/CVPR.2011.5995503

Lampert C. Maximum margin multi-label structured prediction. In: Neural Information Processing Systems; 2011.
LibreCat: https://research-explorer.ista.ac.at/record/3163

Lampert C. Maximum Margin Multi Label Structured Prediction. Neural Information Processing Systems Foundation; 2011.
LibreCat: https://research-explorer.ista.ac.at/record/3322

Nowozin S, Lampert C. Structured learning and prediction in computer vision. Foundations and Trends in Computer Graphics and Vision. 2011;6(3-4):185-365. doi:10.1561/0600000033
LibreCat: https://research-explorer.ista.ac.at/record/3320
DOI: 10.1561/0600000033

Quadrianto N, Lampert C. Learning multi-view neighborhood preserving projections. In: ML Research Press; 2011:425-432.
LibreCat: https://research-explorer.ista.ac.at/record/3319

Lampert C, Krömer O. Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning. In: Vol 6312. Springer; 2010:566-579. doi:10.1007/978-3-642-15552-9_41
LibreCat: https://research-explorer.ista.ac.at/record/3794
DOI: 10.1007/978-3-642-15552-9_41

Nowozin S, Gehler P, Lampert C. On parameter learning in CRF-based approaches to object class image segmentation. In: Vol 6316. Springer; 2010:98-111. doi:10.1007/978-3-642-15567-3_8
LibreCat: https://research-explorer.ista.ac.at/record/3793
DOI: 10.1007/978-3-642-15567-3_8