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 .

E.-A. Peste, “Efficiency and generalization of sparse neural networks,” Institute of Science and Technology Austria, 2023.

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.

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.

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.

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.

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.

B. Prach and C. Lampert, “-Lipschitz neural networks are more expressive with N-activations,” arXiv. .

J. A. Scott, M. X. Yeo, and C. Lampert, “Cross-client Label Propagation for transductive federated learning,” arXiv. .

P. Súkeník and C. Lampert, “Generalization in Multi-objective machine learning,” arXiv. .

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.

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.

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.

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.

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.

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.

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.

N. H. Konstantinov, “Robustness and fairness in machine learning,” Institute of Science and Technology Austria, 2022.

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.

M. Phuong and C. Lampert, “The inductive bias of ReLU networks on orthogonally separable data,” in 9th International Conference on Learning Representations, Virtual, 2021.

N. H. Konstantinov and C. Lampert, “Fairness through regularization for learning to rank,” arXiv. .

M. Phuong, “Underspecification in deep learning,” Institute of Science and Technology Austria, 2021.

C. Lampert, “Zero-Shot Learning,” in Computer Vision, 2nd ed., K. Ikeuchi, Ed. Cham: Springer, 2021, pp. 1395–1397.

T. Anciukevicius, C. Lampert, and P. M. Henderson, “Object-centric image generation with factored depths, locations, and appearances,” arXiv. .

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.

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.

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.

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.

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.

M. Phuong and C. Lampert, “Functional vs. parametric equivalence of ReLU networks,” in 8th International Conference on Learning Representations, Online, 2020.

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.

A. Royer, “Leveraging structure in Computer Vision tasks for flexible Deep Learning models,” Institute of Science and Technology Austria, 2020.

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.

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.

K. Kersting, C. Lampert, and C. Rothkopf, Eds., Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt, 1st ed. Wiesbaden: Springer Nature, 2019.

P. Ashok, T. Brázdil, K. Chatterjee, J. Ketínský, C. Lampert, and V. Toman, “Strategy representation by decision trees with linear classifiers,” in 16th International Conference on Quantitative Evaluation of Systems, Glasgow, United Kingdom, 2019, vol. 11785, pp. 109–128.

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.

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.

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.

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.

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.

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.

A. Zimin, “Learning from dependent data,” Institute of Science and Technology Austria, 2018.

A. Kolesnikov, “Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images,” Institute of Science and Technology Austria, 2018.

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.

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.

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.

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.

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.

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.

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.

R. Der and G. S. Martius, “Self organized behavior generation for musculoskeletal robots,” Frontiers in Neurorobotics, vol. 11, no. MAR. Frontiers Research Foundation, 2017.

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.

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.

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.

S. A. Rebuffi, A. Kolesnikov, G. Sperl, and C. Lampert, CaRL: Incremental classifier and representation learning,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 5533–5542.

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.

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.

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.

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.

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.

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.

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.

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.

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.

A. Pentina, “Theoretical foundations of multi-task lifelong learning,” Institute of Science and Technology Austria, 2016.

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.

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.

R. Der and G. S. Martius, “Novel plasticity rule can explain the development of sensorimotor intelligence,” PNAS, vol. 112, no. 45. National Academy of Sciences, pp. 24–32, 2015.

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.

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.

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.

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.

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.

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.

V. Sharmanska, “Learning with attributes for object recognition: Parametric and non-parametrics views,” Institute of Science and Technology Austria, 2015.

G. S. Martius and E. Olbrich, “Quantifying emergent behavior of autonomous robots,” Entropy, vol. 17, no. 10. MDPI, pp. 7266–7297, 2015.

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.

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.

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.

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.

S. Khamis and C. Lampert, “CoConut: Co-classification with output space regularization,” in Proceedings of the British Machine Vision Conference 2014, Nottingham, UK, 2014.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

F. Korc, V. Kolmogorov, and C. Lampert, Approximating marginals using discrete energy minimization. IST Austria, 2012.