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75 Publications

2019 | Conference Paper | IST-REx-ID: 6942   OA
Ashok, P., Brázdil, T., Chatterjee, K., Křetínský, J., Lampert, C., & Toman, V. (2019). Strategy representation by decision trees with linear classifiers. In 16th International Conference on Quantitative Evaluation of Systems (Vol. 11785, pp. 109–128). Glasgow, United Kingdom: Springer Nature. https://doi.org/10.1007/978-3-030-30281-8_7
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2019 | Conference Paper | IST-REx-ID: 6569   OA
Bui Thi Mai, P., & Lampert, C. (2019). Towards understanding knowledge distillation. In Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 5142–5151). Long Beach, CA, United States: PMLR.
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2019 | Conference Paper | IST-REx-ID: 6590   OA
Konstantinov, N. H., & Lampert, C. (n.d.). Robust learning from untrusted sources. In Proceedings of the 36th International Conference on Machine Learning. Long Beach, CA, USA.
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2019 | Journal Article | IST-REx-ID: 6944   OA
Sun, R., & Lampert, C. (2019). KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications. International Journal of Computer Vision. https://doi.org/10.1007/s11263-019-01232-x
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2019 | Conference Paper | IST-REx-ID: 6482
Sun, R., & Lampert, C. (2019). KS(conf): A light-weight test if a ConvNet operates outside of Its specifications (Vol. 11269, pp. 244–259). Presented at the GCPR: Conference on Pattern Recognition, Stuttgart, Germany: Springer Nature. https://doi.org/10.1007/978-3-030-12939-2_18
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2018 | Journal Article | IST-REx-ID: 321
Darrell, T., Lampert, C., Sebe, N., Wu, Y., & Yan, Y. (2018). 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, 40(5), 1029–1031. https://doi.org/10.1109/TPAMI.2018.2804998
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2018 | Conference Paper | IST-REx-ID: 6589   OA
Alistarh, D.-A., Hoefler, T., Johansson, M., Konstantinov, N. H., Khirirat, S., & Renggli, C. (2018). The convergence of sparsified gradient methods. In Advances in Neural Information Processing Systems 31 (Vol. Volume 2018, pp. 5973–5983). Montreal, Canada: Neural information processing systems.
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2018 | Conference Paper | IST-REx-ID: 6011   OA
Kuzborskij, I., & Lampert, C. (2018). Data-dependent stability of stochastic gradient descent. In Proceedings of the 35 th International Conference on Machine Learning (Vol. 80, pp. 2815–2824). Stockholm, Sweden: International Machine Learning Society.
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2018 | Journal Article | IST-REx-ID: 6554   OA
Xian, Y., Lampert, C., Schiele, B., & Akata, Z. (2018). Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2018.2857768
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2018 | Thesis | IST-REx-ID: 68   OA
Zimin, A. (2018). Learning from dependent data. IST Austria. https://doi.org/10.15479/AT:ISTA:TH1048
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2018 | Conference Paper | IST-REx-ID: 6012   OA
Sahoo, S., Lampert, C., & Martius, G. S. (2018). Learning equations for extrapolation and control. In Proceedings of the 35th International Conference on Machine Learning (Vol. 80, pp. 4442–4450). Stockholm, Sweden: International Machine Learning Society.
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2018 | Journal Article | IST-REx-ID: 563   OA
Ringbauer, H., Kolesnikov, A., Field, D., & Barton, N. H. (2018). Estimating barriers to gene flow from distorted isolation-by-distance patterns. Genetics, 208(3), 1231–1245. https://doi.org/10.1534/genetics.117.300638
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2018 | Research Data | IST-REx-ID: 5584
Deny, S., Marre, O., Botella-Soler, V., Martius, G. S., & Tkacik, G. (2018). Nonlinear decoding of a complex movie from the mammalian retina. IST Austria. https://doi.org/10.15479/AT:ISTA:98
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2018 | Thesis | IST-REx-ID: 197   OA
Kolesnikov, A. (2018). Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images. IST Austria. https://doi.org/10.15479/AT:ISTA:th_1021
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2017 | Conference Paper | IST-REx-ID: 911   OA
Royer, A., Kolesnikov, A., & Lampert, C. (n.d.). Probabilistic image colorization. Presented at the BMVC: British Machine Vision Conference, London, United Kingdom: BMVA Press.
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2017 | Conference Paper | IST-REx-ID: 1000   OA
Kolesnikov, A., & Lampert, C. (2017). PixelCNN models with auxiliary variables for natural image modeling (Vol. 70, pp. 1905–1914). Presented at the ICML: International Conference on Machine Learning, Sydney, Australia: Omnipress.
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2017 | Conference Paper | IST-REx-ID: 652
Der, R., & Martius, G. S. (2017). 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: IEEE. https://doi.org/10.1109/DEVLRN.2016.7846789
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2017 | Conference Paper | IST-REx-ID: 998   OA
Rebuffi, S. A., Kolesnikov, A., Sperl, G., & Lampert, C. (2017). iCaRL: Incremental classifier and representation learning (Vol. 2017, pp. 5533–5542). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. https://doi.org/10.1109/CVPR.2017.587
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2017 | Conference Paper | IST-REx-ID: 6841   OA
Martius, G. S., & Lampert, C. (2017). Extrapolation and learning equations. In 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings. Toulon, France: International Conference on Learning Representations.
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2017 | Journal Article | IST-REx-ID: 658   OA
Der, R., & Martius, G. S. (2017). Self organized behavior generation for musculoskeletal robots. Frontiers in Neurorobotics, 11(MAR). https://doi.org/10.3389/fnbot.2017.00008
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2017 | Conference Paper | IST-REx-ID: 999   OA
Pentina, A., & Lampert, C. (2017). Multi-task learning with labeled and unlabeled tasks (Vol. 70, pp. 2807–2816). Presented at the ICML: International Conference on Machine Learning, Sydney, Australia: Omnipress.
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2017 | Conference Paper | IST-REx-ID: 1108   OA
Zimin, A., & Lampert, C. (2017). Learning theory for conditional risk minimization (Vol. 54, pp. 213–222). Presented at the AISTATS: Artificial Intelligence and Statistics, Fort Lauderdale, FL, United States: JMLR, Inc. and Microtome Publishing.
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2017 | Conference Paper | IST-REx-ID: 750
Pielorz, J., Prandtstetter, M., Straub, M., & Lampert, C. (2017). Optimal geospatial volunteer allocation needs realistic distances. In 2017 IEEE International Conference on Big Data (pp. 3760–3763). Boston, MA, United States: IEEE. https://doi.org/10.1109/BigData.2017.8258375
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2016 | Conference Paper | IST-REx-ID: 1369   OA
Kolesnikov, A., & Lampert, C. (2016). Seed, expand and constrain: Three principles for weakly-supervised image segmentation (Vol. 9908, pp. 695–711). Presented at the ECCV: European Conference on Computer Vision, Amsterdam, The Netherlands: Springer. https://doi.org/10.1007/978-3-319-46493-0_42
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2016 | Conference Paper | IST-REx-ID: 1098   OA
Pentina, A., & Urner, R. (2016). Lifelong learning with weighted majority votes (Vol. 29, pp. 3619–3627). Presented at the NIPS: Neural Information Processing Systems, Barcelona, Spain: Neural Information Processing Systems.
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2016 | Conference Paper | IST-REx-ID: 1214
Martius, G. S., Hostettler, R., Knoll, A., & Der, R. (2016). Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm (Vol. 2016–November). Presented at the IEEE RSJ International Conference on Intelligent Robots and Systems IROS , Daejeon, Korea: IEEE. https://doi.org/10.1109/IROS.2016.7759138
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2016 | Conference Paper | IST-REx-ID: 1707
Pielorz, J., & Lampert, C. (2016). Optimal geospatial allocation of volunteers for crisis management. Presented at the ICT-DM: Information and Communication Technologies for Disaster Management, Rennes, France: IEEE. https://doi.org/10.1109/ICT-DM.2015.7402041
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2016 | Conference Paper | IST-REx-ID: 1102   OA
Kolesnikov, A., & Lampert, C. (2016). Improving weakly-supervised object localization by micro-annotation. In Proceedings of the British Machine Vision Conference 2016 (Vol. 2016–September, p. 92.1-92.12). York, United Kingdom: BMVA Press. https://doi.org/10.5244/C.30.92
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2016 | Thesis | IST-REx-ID: 1126   OA
Pentina, A. (2016). Theoretical foundations of multi-task lifelong learning. IST Austria. https://doi.org/10.15479/AT:ISTA:TH_776
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2015 | Journal Article | IST-REx-ID: 1655   OA
Martius, G. S., & Olbrich, E. (2015). Quantifying emergent behavior of autonomous robots. Entropy, 17(10), 7266–7297. https://doi.org/10.3390/e17107266
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2015 | Conference Paper | IST-REx-ID: 1706   OA
Pentina, A., & Ben David, S. (2015). Multi-task and lifelong learning of kernels (Vol. 9355, pp. 194–208). Presented at the ALT: Algorithmic Learning Theory, Banff, AB, Canada: Springer. https://doi.org/10.1007/978-3-319-24486-0_13
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2015 | Conference Paper | IST-REx-ID: 1857   OA
Pentina, A., Sharmanska, V., & Lampert, C. (2015). Curriculum learning of multiple tasks (pp. 5492–5500). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States: IEEE. https://doi.org/10.1109/CVPR.2015.7299188
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2015 | Conference Paper | IST-REx-ID: 1858   OA
Lampert, C. (2015). Predicting the future behavior of a time-varying probability distribution (pp. 942–950). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States: IEEE. https://doi.org/10.1109/CVPR.2015.7298696
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2015 | Conference Paper | IST-REx-ID: 1860   OA
Royer, A., & Lampert, C. (2015). Classifier adaptation at prediction time (pp. 1401–1409). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States: IEEE. https://doi.org/10.1109/CVPR.2015.7298746
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2015 | Journal Article | IST-REx-ID: 1570   OA
Der, R., & Martius, G. S. (2015). Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS, 112(45), E6224–E6232. https://doi.org/10.1073/pnas.1508400112
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2015 | Conference Paper | IST-REx-ID: 1859   OA
Shah, N., Kolmogorov, V., & Lampert, C. (2015). A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle (pp. 2737–2745). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, USA: IEEE. https://doi.org/10.1109/CVPR.2015.7298890
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2015 | Conference Paper | IST-REx-ID: 1425   OA
Pentina, A., & Lampert, C. (2015). Lifelong learning with non-i.i.d. tasks (Vol. 2015, pp. 1540–1548). Presented at the NIPS: Neural Information Processing Systems, Montreal, Canada: Neural Information Processing Systems.
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2015 | Thesis | IST-REx-ID: 1401
Sharmanska, V. (2015). Learning with attributes for object recognition: Parametric and non-parametrics views. IST Austria.
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2015 | Journal Article | IST-REx-ID: 1533
Xia, W., Domokos, C., Xiong, J., Cheong, L., & Yan, S. (2015). Segmentation over detection via optimal sparse reconstructions. IEEE Transactions on Circuits and Systems for Video Technology, 25(8), 1295–1308. https://doi.org/10.1109/TCSVT.2014.2379972
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2014 | Conference Paper | IST-REx-ID: 2171   OA
Kolesnikov, A., Guillaumin, M., Ferrari, V., & Lampert, C. (2014). Closed-form approximate CRF training for scalable image segmentation. 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) (Vol. 8691, pp. 550–565). Zurich, Switzerland: Springer. https://doi.org/10.1007/978-3-319-10578-9_36
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2014 | Conference Paper | IST-REx-ID: 2033   OA
Hernandez Lobato, D., Sharmanska, V., Kersting, K., Lampert, C., & Quadrianto, N. (2014). Mind the nuisance: Gaussian process classification using privileged noise. In Advances in Neural Information Processing Systems (Vol. 1, pp. 837–845). Montreal, Canada: Neural Information Processing Systems.
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2014 | Conference Paper | IST-REx-ID: 2057   OA
Morvant, E., Habrard, A., & Ayache, S. (2014). 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, pp. 153–162). Joensuu, Finland: Springer. https://doi.org/10.1007/978-3-662-44415-3_16
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2014 | Conference Paper | IST-REx-ID: 2172
Sydorov, V., Sakurada, M., & Lampert, C. (2014). 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 (pp. 1402–1409). Columbus, USA: IEEE. https://doi.org/10.1109/CVPR.2014.182
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2014 | Conference Paper | IST-REx-ID: 2189   OA
Morvant, E. (2014). Adaptation de domaine de vote de majorité par auto-étiquetage non itératif (Vol. 1, pp. 49–58). Presented at the CAP: Conférence Francophone sur l’Apprentissage Automatique (Machine Learning French Conference), Saint-Etienne, France: Elsevier.
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2014 | Conference Paper | IST-REx-ID: 2160   OA
Pentina, A., & Lampert, C. (2014). A PAC-Bayesian bound for Lifelong Learning. In E. Xing & T. Jebara (Eds.) (Vol. 32, pp. 991–999). Presented at the ICML: International Conference on Machine Learning, Beijing, China: Omnipress.
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2014 | Conference Paper | IST-REx-ID: 2173   OA
Khamis, S., & Lampert, C. (2014). CoConut: Co-classification with output space regularization. In Proceedings of the British Machine Vision Conference 2014. Nottingham, UK: BMVA Press.
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2014 | Journal Article | IST-REx-ID: 2180   OA
Bellet, A., Habrard, A., Morvant, E., & Sebban, M. (2014). Learning a priori constrained weighted majority votes. Machine Learning, 97(1–2), 129–154. https://doi.org/10.1007/s10994-014-5462-z
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2014 | Book Chapter | IST-REx-ID: 1829
Muelling, K., Kroemer, O., Lampert, C., & Schölkopf, B. (2014). Movement templates for learning of hitting and batting. In J. Kober & J. Peters (Eds.), Learning Motor Skills (Vol. 97, pp. 69–82). Springer. https://doi.org/10.1007/978-3-319-03194-1_3
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2013 | Conference Paper | IST-REx-ID: 2520   OA
Quadrianto, N., Sharmanska, V., Knowles, D., & Ghahramani, Z. (2013). The supervised IBP: Neighbourhood preserving infinite latent feature models. In Proceedings of the 29th conference uncertainty in Artificial Intelligence (pp. 527–536). Bellevue, WA, United States: AUAI Press.
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2013 | Conference Paper | IST-REx-ID: 2948   OA
Tommasi, T., Quadrianto, N., Caputo, B., & Lampert, C. (2013). Beyond dataset bias: Multi-task unaligned shared knowledge transfer. Presented at the ACCV: Asian Conference on Computer Vision, Daejeon, Korea: Springer. https://doi.org/10.1007/978-3-642-37331-2_1
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2013 | Encyclopedia Article | IST-REx-ID: 3321
Quadrianto, N., & Lampert, C. (2013). Kernel based learning. In W. Dubitzky, O. Wolkenhauer, K. Cho, & H. Yokota (Eds.), Encyclopedia of Systems Biology (Vol. 3, pp. 1069–1069). Springer. https://doi.org/10.1007/978-1-4419-9863-7_604
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2013 | Conference Paper | IST-REx-ID: 2901   OA
Chen, C., Kolmogorov, V., Yan, Z., Metaxas, D., & Lampert, C. (2013). Computing the M most probable modes of a graphical model (Vol. 31, pp. 161–169). Presented at the AISTATS: Conference on Uncertainty in Artificial Intelligence, Scottsdale, AZ, United States: JMLR.
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2013 | Conference Paper | IST-REx-ID: 2293   OA
Sharmanska, V., Quadrianto, N., & Lampert, C. (2013). Learning to rank using privileged information (pp. 825–832). Presented at the ICCV: International Conference on Computer Vision, Sydney, Australia: IEEE. https://doi.org/10.1109/ICCV.2013.107
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2013 | Journal Article | IST-REx-ID: 2516
Lampert, C., Nickisch, H., & Harmeling, S. (2013). Attribute-based classification for zero-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(3), 453–465. https://doi.org/10.1109/TPAMI.2013.140
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2013 | Conference Paper | IST-REx-ID: 2294   OA
Kazmar, T., Kvon, E., Stark, A., & Lampert, C. (2013). Drosophila Embryo Stage Annotation using Label Propagation. Presented at the ICCV: International Conference on Computer Vision, Sydney, Australia: IEEE. https://doi.org/10.1109/ICCV.2013.139
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2012 | Conference Paper | IST-REx-ID: 3124
Korc, F., Kolmogorov, V., & Lampert, C. (2012). Approximating marginals using discrete energy minimization. Presented at the ICML: International Conference on Machine Learning, Edinburgh, Scotland: ICML.
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2012 | Technical Report | IST-REx-ID: 5396   OA
Korc, F., Kolmogorov, V., & Lampert, C. (2012). Approximating marginals using discrete energy minimization. IST Austria. https://doi.org/10.15479/AT:IST-2012-0003
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2012 | Conference Paper | IST-REx-ID: 3125
Sharmanska, V., Quadrianto, N., & Lampert, C. (2012). Augmented attribute representations (Vol. 7576, pp. 242–255). Presented at the ECCV: European Conference on Computer Vision, Florence, Italy: Springer. https://doi.org/10.1007/978-3-642-33715-4_18
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2012 | Conference Paper | IST-REx-ID: 2825
Lampert, C. (2012). Dynamic pruning of factor graphs for maximum marginal prediction (Vol. 1, pp. 82–90). Presented at the NIPS: Neural Information Processing Systems, Lake Tahoe, NV, United States: Neural Information Processing Systems.
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2012 | Conference Paper | IST-REx-ID: 3126
Müller, A., Nowozin, S., & Lampert, C. (2012). Information theoretic clustering using minimal spanning trees (Vol. 7476, pp. 205–215). Presented at the DAGM: German Association For Pattern Recognition, Graz, Austria: Springer. https://doi.org/10.1007/978-3-642-32717-9_21
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2012 | Journal Article | IST-REx-ID: 3164
Blaschko, M., & Lampert, C. (2012). Guest editorial: Special issue on structured prediction and inference. International Journal of Computer Vision, 99(3), 257–258. https://doi.org/10.1007/s11263-012-0530-y
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2012 | Conference Paper | IST-REx-ID: 2915
Kroemer, O., Lampert, C., & Peters, J. (2012). Multi-modal learning for dynamic tactile sensing. Deutsches Zentrum für Luft und Raumfahrt.
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2012 | Conference Paper | IST-REx-ID: 3127   OA
Quadrianto, N., Lampert, C., & Chen, C. (2012). The most persistent soft-clique in a set of sampled graphs. In Proceedings of the 29th International Conference on Machine Learning (pp. 211–218). Edinburgh, United Kingdom: Omnipress.
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2012 | Journal Article | IST-REx-ID: 3248   OA
Lampert, C., & Peters, J. (2012). Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components. Journal of Real-Time Image Processing, 7(1), 31–41. https://doi.org/10.1007/s11554-010-0168-3
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2011 | Conference Paper | IST-REx-ID: 3319
Quadrianto, N., & Lampert, C. (2011). Learning multi-view neighborhood preserving projections (pp. 425–432). Presented at the ICML: International Conference on Machine Learning, Bellevue, USA: Omnipress.
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2011 | Conference Paper | IST-REx-ID: 3163
Lampert, C. (2011). Maximum margin multi-label structured prediction. Presented at the NIPS: Neural Information Processing Systems, Granada, Spain: Neural Information Processing Systems.
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2011 | Conference Poster | IST-REx-ID: 3322
Lampert, C. (2011). Maximum margin multi label structured prediction. NIPS: Neural Information Processing Systems. Neural Information Processing Systems.
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2011 | Journal Article | IST-REx-ID: 3389
Blaschko, M., Shelton, J., Bartels, A., Lampert, C., & Gretton, A. (2011). Semi supervised kernel canonical correlation analysis with application to human fMRI. Pattern Recognition Letters, 32(11), 1572–1583. https://doi.org/10.1016/j.patrec.2011.02.011
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2011 | Technical Report | IST-REx-ID: 5386
Chen, C., Freedman, D., & Lampert, C. (2011). Enforcing topological constraints in random field image segmentation. IST Austria. https://doi.org/10.15479/AT:IST-2011-0002
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2011 | Conference Paper | IST-REx-ID: 3336
Chen, C., Freedman, D., & Lampert, C. (2011). Enforcing topological constraints in random field image segmentation. In CVPR: Computer Vision and Pattern Recognition (pp. 2089–2096). Colorado Springs, CO, USA: IEEE. https://doi.org/10.1109/CVPR.2011.5995503
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2011 | Journal Article | IST-REx-ID: 3320
Nowozin, S., & Lampert, C. (2011). Structured learning and prediction in computer vision. Foundations and Trends in Computer Graphics and Vision, 6(3–4), 185–365. https://doi.org/10.1561/0600000033
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2011 | Journal Article | IST-REx-ID: 3382
Kroemer, O., Lampert, C., & Peters, J. (2011). Learning dynamic tactile sensing with robust vision based training. IEEE Transactions on Robotics, 27(3), 545–557. https://doi.org/10.1109/TRO.2011.2121130
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2011 | Conference Paper | IST-REx-ID: 3337
Wang, Z., Lampert, C., Mülling, K., Schölkopf, B., & Peters, J. (2011). Learning anticipation policies for robot table tennis (pp. 332–337). Presented at the IROS: RSJ International Conference on Intelligent Robots and Systems, San Francisco, USA: IEEE. https://doi.org/10.1109/IROS.2011.6094892
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2010 | Conference Paper | IST-REx-ID: 3794
Lampert, C., & Krömer, O. (2010). Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning (Vol. 6312, pp. 566–579). Presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece: Springer. https://doi.org/10.1007/978-3-642-15552-9_41
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2010 | Conference Paper | IST-REx-ID: 3793
Nowozin, S., Gehler, P., & Lampert, C. (2010). On parameter learning in CRF-based approaches to object class image segmentation (Vol. 6316, pp. 98–111). Presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece: Springer. https://doi.org/10.1007/978-3-642-15567-3_8
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