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114 Publications

2023 | Conference Paper | IST-REx-ID: 13053 | OA
E.-A. Peste, A. Vladu, E. Kurtic, C. Lampert, and D.-A. Alistarh, “CrAM: A Compression-Aware Minimizer,” in 11th International Conference on Learning Representations , Kigali, Rwanda .
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
2023 | Thesis | IST-REx-ID: 13074 | OA
E.-A. Peste, “Efficiency and generalization of sparse neural networks,” Institute of Science and Technology Austria, 2023.
[Published Version] View | Files available | DOI
 
2023 | Journal Article | IST-REx-ID: 14320 | OA
P. M. Henderson, A. Ghazaryan, A. A. Zibrov, A. F. Young, and M. Serbyn, “Deep learning extraction of band structure parameters from density of states: A case study on trilayer graphene,” Physical Review B, vol. 108, no. 12. American Physical Society, 2023.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2023 | Conference Paper | IST-REx-ID: 14410
P. Tomaszewska and C. Lampert, “On the implementation of baselines and lightweight conditional model extrapolation (LIMES) under class-prior shift,” in International Workshop on Reproducible Research in Pattern Recognition, Montreal, Canada, 2023, vol. 14068, pp. 67–73.
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2023 | Journal Article | IST-REx-ID: 14446 | OA
J. Jakubík, M. Phuong, M. Chvosteková, and A. Krakovská, “Against the flow of time with multi-output models,” Measurement Science Review, vol. 23, no. 4. Sciendo, pp. 175–183, 2023.
[Published Version] View | Files available | DOI
 
2023 | Conference Paper | IST-REx-ID: 14771 | OA
E. B. Iofinova, E.-A. Peste, and D.-A. Alistarh, “Bias in pruned vision models: In-depth analysis and countermeasures,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 2023, pp. 24364–24373.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
2023 | Conference Paper | IST-REx-ID: 14921 | OA
P. Súkeník, M. Mondelli, and C. Lampert, “Deep neural collapse is provably optimal for the deep unconstrained features model,” in 37th Annual Conference on Neural Information Processing Systems, New Orleans, LA, United States.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2023 | Preprint | IST-REx-ID: 15039 | OA
B. Prach and C. Lampert, “1-Lipschitz neural networks are more expressive with N-activations,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2022 | Preprint | IST-REx-ID: 12660 | OA
J. A. Scott, M. X. Yeo, and C. Lampert, “Cross-client Label Propagation for transductive federated learning,” arXiv. .
[Preprint] View | Files available | DOI | arXiv
 
2022 | Preprint | IST-REx-ID: 12662 | OA
P. Súkeník and C. Lampert, “Generalization in Multi-objective machine learning,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2022 | Journal Article | IST-REx-ID: 12495 | OA
E. B. Iofinova, N. H. Konstantinov, and C. Lampert, “FLEA: Provably robust fair multisource learning from unreliable training data,” Transactions on Machine Learning Research. ML Research Press, 2022.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
2022 | Conference Paper | IST-REx-ID: 11839 | OA
B. Prach and C. Lampert, “Almost-orthogonal layers for efficient general-purpose Lipschitz networks,” in Computer Vision – ECCV 2022, Tel Aviv, Israel, 2022, vol. 13681, pp. 350–365.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2022 | Conference Paper | IST-REx-ID: 10752
J. Lampert and C. Lampert, “Overcoming rare-language discrimination in multi-lingual sentiment analysis,” in 2021 IEEE International Conference on Big Data, Orlando, FL, United States, 2022, pp. 5185–5192.
View | DOI | WoS
 
2022 | Conference Paper | IST-REx-ID: 12161 | OA
P. Tomaszewska and C. Lampert, “Lightweight conditional model extrapolation for streaming data under class-prior shift,” in 26th International Conference on Pattern Recognition, Montreal, Canada, 2022, vol. 2022, pp. 2128–2134.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
2022 | Conference Paper | IST-REx-ID: 12299 | OA
E. B. Iofinova, E.-A. Peste, M. Kurtz, and D.-A. Alistarh, “How well do sparse ImageNet models transfer?,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, United States, 2022, pp. 12256–12266.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
2022 | Journal Article | IST-REx-ID: 10802 | OA
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.
[Published Version] View | Files available | arXiv
 
2022 | Conference Paper | IST-REx-ID: 13241 | OA
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.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
2022 | Thesis | IST-REx-ID: 10799 | OA
N. H. Konstantinov, “Robustness and fairness in machine learning,” Institute of Science and Technology Austria, 2022.
[Published Version] View | Files available | DOI
 
2021 | Conference Paper | IST-REx-ID: 9210 | OA
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.
[Submitted Version] View | Files available | DOI
 
2021 | Conference Paper | IST-REx-ID: 9416 | OA
M. Phuong and C. Lampert, “The inductive bias of ReLU networks on orthogonally separable data,” in 9th International Conference on Learning Representations, Virtual, 2021.
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2021 | Preprint | IST-REx-ID: 10803 | OA
N. H. Konstantinov and C. Lampert, “Fairness through regularization for learning to rank,” arXiv. .
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2021 | Thesis | IST-REx-ID: 9418 | OA
M. Phuong, “Underspecification in deep learning,” Institute of Science and Technology Austria, 2021.
[Published Version] View | Files available | DOI
 
2021 | Book Chapter | IST-REx-ID: 14987
C. Lampert, “Zero-Shot Learning,” in Computer Vision, 2nd ed., K. Ikeuchi, Ed. Cham: Springer, 2021, pp. 1395–1397.
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2020 | Preprint | IST-REx-ID: 8063 | OA
T. Anciukevicius, C. Lampert, and P. M. Henderson, “Object-centric image generation with factored depths, locations, and appearances,” arXiv. .
[Preprint] View | Download Preprint (ext.) | arXiv
 
2020 | Conference Paper | IST-REx-ID: 8188 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2020 | Journal Article | IST-REx-ID: 6952 | OA
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.
[Published Version] View | Files available | DOI | WoS | arXiv
 
2020 | Conference Paper | IST-REx-ID: 7936 | OA
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.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2020 | Conference Paper | IST-REx-ID: 7937 | OA
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.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2020 | Book Chapter | IST-REx-ID: 8092 | OA
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.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2020 | Conference Paper | IST-REx-ID: 7481 | OA
M. Phuong and C. Lampert, “Functional vs. parametric equivalence of ReLU networks,” in 8th International Conference on Learning Representations, Online, 2020.
[Published Version] View | Files available
 
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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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