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Egor Zverev

Nikita Kalinin

Christoph Lampert

Jonathan Scott

Peter Sukenik

Hossein Zakerinia

Bernd Prach

Alexander Zimin

Amélie Royer

Alexandra Krumes

Anastasia Pentina

Emilie Morvant

Mary Phuong

Volhejn, Vaclav

Paul Henderson

Georg Martius

Alexander Kolesnikov

Daniel Pucher

Csaba Domokos

Johannes Dostal

Viktoriia Sharmanska

Nikola Konstantinov

Filip Korc



114 Publications

2020 | Conference Paper | IST-REx-ID: 8724 | OA
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.
[Published Version] View | Files available | arXiv
 
2020 | Thesis | IST-REx-ID: 8390 | OA
A. Royer, “Leveraging structure in Computer Vision tasks for flexible Deep Learning models,” Institute of Science and Technology Austria, 2020.
[Published Version] View | Files available | DOI
 
2020 | Conference Paper | IST-REx-ID: 8186 | OA
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.
[Submitted Version] View | Files available | DOI | Download Submitted Version (ext.) | arXiv
 
2020 | Journal Article | IST-REx-ID: 6944 | OA
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.
[Published Version] View | Files available | DOI | WoS
 
2019 | Book (Editor) | IST-REx-ID: 7171
K. Kersting, C. Lampert, and C. Rothkopf, Eds., Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt, 1st ed. Wiesbaden: Springer Nature, 2019.
View | Files available | DOI
 
2019 | Conference Paper | IST-REx-ID: 6942 | OA
P. Ashok, T. Brázdil, K. Chatterjee, J. Křetínský, 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.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
2019 | Journal Article | IST-REx-ID: 6554 | OA
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.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
2019 | Conference Paper | IST-REx-ID: 7479 | OA
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.
[Submitted Version] View | Files available | DOI | WoS
 
2019 | Conference Paper | IST-REx-ID: 7640 | OA
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.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
2019 | Conference Paper | IST-REx-ID: 6569 | OA
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.
[Published Version] View | Files available
 
2019 | Conference Paper | IST-REx-ID: 6590 | OA
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.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
2019 | Conference Paper | IST-REx-ID: 6482 | OA
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.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2018 | Thesis | IST-REx-ID: 68 | OA
A. Zimin, “Learning from dependent data,” Institute of Science and Technology Austria, 2018.
[Published Version] View | Files available | DOI
 
2018 | Thesis | IST-REx-ID: 197 | OA
A. Kolesnikov, “Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images,” Institute of Science and Technology Austria, 2018.
[Published Version] View | Files available | DOI
 
2018 | Journal Article | IST-REx-ID: 563 | OA
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.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS
 
2018 | Journal Article | IST-REx-ID: 321 | OA
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.
[Published Version] View | Files available | DOI | WoS
 
2018 | Conference Paper | IST-REx-ID: 10882 | OA
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.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
2018 | Conference Paper | IST-REx-ID: 6012 | OA
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.
[Preprint] View | Files available | Download Preprint (ext.) | WoS | arXiv
 
2018 | Conference Paper | IST-REx-ID: 6011 | OA
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.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
2018 | Conference Paper | IST-REx-ID: 6589 | OA
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.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
2018 | Research Data | IST-REx-ID: 5584 | OA
S. Deny, O. Marre, V. Botella-Soler, G. S. Martius, and G. Tkačik, “Nonlinear decoding of a complex movie from the mammalian retina.” Institute of Science and Technology Austria, 2018.
[Published Version] View | Files available | DOI
 
2017 | Conference Paper | IST-REx-ID: 652
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.
View | DOI
 
2017 | Journal Article | IST-REx-ID: 658 | OA
R. Der and G. S. Martius, “Self organized behavior generation for musculoskeletal robots,” Frontiers in Neurorobotics, vol. 11, no. MAR. Frontiers Research Foundation, 2017.
[Published Version] View | Files available | DOI
 
2017 | Conference Paper | IST-REx-ID: 6841 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2017 | Conference Paper | IST-REx-ID: 750
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.
View | DOI
 
2017 | Conference Paper | IST-REx-ID: 1000 | OA
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.
[Submitted Version] View | Download Submitted Version (ext.) | WoS | arXiv
 
2017 | Conference Paper | IST-REx-ID: 998 | OA
S. A. Rebuffi, A. Kolesnikov, G. Sperl, and C. Lampert, “iCaRL: Incremental classifier and representation learning,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 5533–5542.
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
2017 | Conference Paper | IST-REx-ID: 911 | OA
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.
[Published Version] View | Files available | DOI | arXiv
 
2017 | Conference Paper | IST-REx-ID: 1108 | OA
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.
[Submitted Version] View | Download Submitted Version (ext.) | WoS
 
2017 | Conference Paper | IST-REx-ID: 999 | OA
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.
[Submitted Version] View | Download Submitted Version (ext.) | WoS
 
2016 | Conference Paper | IST-REx-ID: 1098 | OA
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.
[Published Version] View | Files available
 
2016 | Conference Paper | IST-REx-ID: 1102 | OA
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.
[Published Version] View | DOI | Download Published Version (ext.)
 
2016 | Conference Paper | IST-REx-ID: 1214
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.
View | DOI
 
2016 | Conference Paper | IST-REx-ID: 1369 | OA
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.
[Preprint] View | DOI | Download Preprint (ext.)
 
2016 | Conference Paper | IST-REx-ID: 1707
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.
View | DOI
 
2016 | Conference Paper | IST-REx-ID: 8094 | OA
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.
[Published Version] View | Files available | DOI
 
2016 | Thesis | IST-REx-ID: 1126 | OA
A. Pentina, “Theoretical foundations of multi-task lifelong learning,” Institute of Science and Technology Austria, 2016.
[Published Version] View | Files available | DOI
 
2015 | Conference Paper | IST-REx-ID: 1425 | OA
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.
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2015 | Journal Article | IST-REx-ID: 1533
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.
View | DOI
 
2015 | Journal Article | IST-REx-ID: 1570 | OA
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. E6224–E6232, 2015.
[Submitted Version] View | DOI | Download Submitted Version (ext.) | PubMed | Europe PMC
 
2015 | Conference Paper | IST-REx-ID: 1706 | OA
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.
[Preprint] View | DOI | Download Preprint (ext.)
 
2015 | Conference Paper | IST-REx-ID: 1859 | OA
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.
[Preprint] View | DOI | Download Preprint (ext.)
 
2015 | Conference Paper | IST-REx-ID: 1860 | OA
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.
[Submitted Version] View | DOI | Download Submitted Version (ext.)
 
2015 | Conference Paper | IST-REx-ID: 1858 | OA
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.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2015 | Conference Paper | IST-REx-ID: 1857 | OA
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.
[Preprint] View | DOI | Download Preprint (ext.)
 
2015 | Conference Paper | IST-REx-ID: 12881 | OA
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.
[Published Version] View | Files available | DOI
 
2015 | Thesis | IST-REx-ID: 1401 | OA
V. Sharmanska, “Learning with attributes for object recognition: Parametric and non-parametrics views,” Institute of Science and Technology Austria, 2015.
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
2015 | Journal Article | IST-REx-ID: 1655 | OA
G. S. Martius and E. Olbrich, “Quantifying emergent behavior of autonomous robots,” Entropy, vol. 17, no. 10. MDPI, pp. 7266–7297, 2015.
[Published Version] View | Files available | DOI
 
2014 | Book Chapter | IST-REx-ID: 1829
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.
View | DOI
 
2014 | Conference Paper | IST-REx-ID: 2033 | OA
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.
[Submitted Version] View | Download Submitted Version (ext.)
 
2014 | Conference Paper | IST-REx-ID: 2057 | OA
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.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2014 | Conference Paper | IST-REx-ID: 2171 | OA
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.
[Submitted Version] View | DOI | Download Submitted Version (ext.)
 
2014 | Conference Paper | IST-REx-ID: 2173 | OA
S. Khamis and C. Lampert, “CoConut: Co-classification with output space regularization,” in Proceedings of the British Machine Vision Conference 2014, Nottingham, UK, 2014.
[Published Version] View | Files available
 
2014 | Conference Paper | IST-REx-ID: 2172
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.
View | DOI
 
2014 | Journal Article | IST-REx-ID: 2180 | OA
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.
[Submitted Version] View | DOI | Download Submitted Version (ext.)
 
2014 | Conference Paper | IST-REx-ID: 2189 | OA
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.
[Preprint] View | Download Preprint (ext.)
 
2014 | Conference Paper | IST-REx-ID: 2160 | OA
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.
[Submitted Version] View | Download Submitted Version (ext.)
 
2013 | Conference Paper | IST-REx-ID: 2294 | OA
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.
[Submitted Version] View | DOI | Download Submitted Version (ext.)
 
2013 | Conference Paper | IST-REx-ID: 2293 | OA
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.
[Submitted Version] View | DOI | Download Submitted Version (ext.)
 
2013 | Journal Article | IST-REx-ID: 2516
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.
View | DOI
 
2013 | Conference Paper | IST-REx-ID: 2520 | OA
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.
[Submitted Version] View | Files available
 
2013 | Conference Paper | IST-REx-ID: 2901 | OA
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.
View | Download None (ext.)
 
2013 | Conference Paper | IST-REx-ID: 2948 | OA
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.
[Submitted Version] View | Files available | DOI
 
2013 | Encyclopedia Article | IST-REx-ID: 3321
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.
View | DOI
 
2012 | Conference Paper | IST-REx-ID: 2825
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.
View
 
2012 | Journal Article | IST-REx-ID: 3164
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.
View | DOI
 
2012 | Conference Paper | IST-REx-ID: 3125 | OA
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.
[Submitted Version] View | Files available | DOI
 
2012 | Conference Paper | IST-REx-ID: 3126
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.
View | DOI
 
2012 | Journal Article | IST-REx-ID: 3248 | OA
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.
[Submitted Version] View | Files available | DOI
 
2012 | Conference Paper | IST-REx-ID: 3124 | OA
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.
[Submitted Version] View | Files available
 
2012 | Technical Report | IST-REx-ID: 5396 | OA
F. Korc, V. Kolmogorov, and C. Lampert, Approximating marginals using discrete energy minimization. IST Austria, 2012.
[Published Version] View | Files available | DOI
 
2012 | Conference Paper | IST-REx-ID: 2915
O. Kroemer, C. Lampert, and J. Peters, “Multi-modal learning for dynamic tactile sensing,” 2012.
View
 
2012 | Conference Paper | IST-REx-ID: 3127 | OA
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.
[Preprint] View | Download Preprint (ext.)
 
2011 | Conference Paper | IST-REx-ID: 3337
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.
View | DOI
 
2011 | Journal Article | IST-REx-ID: 3389
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.
View | DOI
 
2011 | Journal Article | IST-REx-ID: 3382
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.
View | DOI
 
2011 | Technical Report | IST-REx-ID: 5386 | OA
C. Chen, D. Freedman, and C. Lampert, Enforcing topological constraints in random field image segmentation. IST Austria, 2011.
[Published Version] View | Files available | DOI
 
2011 | Conference Paper | IST-REx-ID: 3336
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.
View | Files available | DOI
 
2011 | Conference Paper | IST-REx-ID: 3163
C. Lampert, “Maximum margin multi-label structured prediction,” presented at the NIPS: Neural Information Processing Systems, Granada, Spain, 2011.
View | Files available
 
2011 | Conference Poster | IST-REx-ID: 3322
C. Lampert, Maximum margin multi label structured prediction. Neural Information Processing Systems Foundation, 2011.
View | Files available
 
2011 | Journal Article | IST-REx-ID: 3320 | OA
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.
[Published Version] View | Files available | DOI
 
2011 | Conference Paper | IST-REx-ID: 3319
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.
View
 
2010 | Conference Paper | IST-REx-ID: 3794
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.
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2010 | Conference Paper | IST-REx-ID: 3793 | OA
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.
[Submitted Version] View | Files available | DOI
 

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