Zakerinia, Hossein, Shayan Talaei, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Communication-Efficient Federated Learning with Data and Client Heterogeneity.” In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, 238:3448–56. ML Research Press, 2024.

Peste, Elena-Alexandra, Adrian Vladu, Eldar Kurtic, Christoph Lampert, and Dan-Adrian Alistarh. “CrAM: A Compression-Aware Minimizer.” In 11th International Conference on Learning Representations , n.d.

Peste, Elena-Alexandra. “Efficiency and Generalization of Sparse Neural Networks.”https://doi.org/10.15479/at:ista:13074__Institute of Science and Technology Austria, 2023.https://doi.org/10.15479/at:ista:13074 .

Henderson, Paul M, Areg Ghazaryan, Alexander A. Zibrov, Andrea F. Young, and Maksym Serbyn. “Deep Learning Extraction of Band Structure Parameters from Density of States: A Case Study on Trilayer Graphene.”https://doi.org/10.1103/physrevb.108.125411__Physical Review B. American Physical Society, 2023.https://doi.org/10.1103/physrevb.108.125411 .

Tomaszewska, Paulina, and Christoph Lampert. “On the Implementation of Baselines and Lightweight Conditional Model Extrapolation (LIMES) under Class-Prior Shift.”https://doi.org/10.1007/978-3-031-40773-4_6__In International Workshop on Reproducible Research in Pattern Recognition, 14068:67–73. Springer Nature, 2023.https://doi.org/10.1007/978-3-031-40773-4_6 .

Jakubík, Jozef, Mary Phuong, Martina Chvosteková, and Anna Krakovská. “Against the Flow of Time with Multi-Output Models.”https://doi.org/10.2478/msr-2023-0023__Measurement Science Review. Sciendo, 2023.https://doi.org/10.2478/msr-2023-0023 .

Iofinova, Eugenia B, Elena-Alexandra Peste, and Dan-Adrian Alistarh. “Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures.”https://doi.org/10.1109/cvpr52729.2023.02334__In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 24364–73. IEEE, 2023.https://doi.org/10.1109/cvpr52729.2023.02334 .

Súkeník, Peter, Marco Mondelli, and Christoph Lampert. “Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model.” In 37th Annual Conference on Neural Information Processing Systems, n.d.

Prach, Bernd, and Christoph Lampert. “-Lipschitz Neural Networks Are More Expressive with N-Activations.”https://doi.org/10.48550/ARXIV.2311.06103__ArXiv, n.d.https://doi.org/10.48550/ARXIV.2311.06103 .

Scott, Jonathan A, Michelle X Yeo, and Christoph Lampert. “Cross-Client Label Propagation for Transductive Federated Learning.”https://doi.org/10.48550/arXiv.2210.06434__ArXiv, n.d.https://doi.org/10.48550/arXiv.2210.06434 .

Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective Machine Learning.”https://doi.org/10.48550/arXiv.2208.13499__ArXiv, n.d.https://doi.org/10.48550/arXiv.2208.13499 .

Iofinova, Eugenia B, Nikola H Konstantinov, and Christoph Lampert. “FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data.” Transactions on Machine Learning Research. ML Research Press, 2022.

Prach, Bernd, and Christoph Lampert. “Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks.”https://doi.org/10.1007/978-3-031-19803-8_21__In Computer Vision – ECCV 2022, 13681:350–65. Springer Nature, 2022.https://doi.org/10.1007/978-3-031-19803-8_21 .

Lampert, Jasmin, and Christoph Lampert. “Overcoming Rare-Language Discrimination in Multi-Lingual Sentiment Analysis.”https://doi.org/10.1109/bigdata52589.2021.9672003__In 2021 IEEE International Conference on Big Data, 5185–92. IEEE, 2022.https://doi.org/10.1109/bigdata52589.2021.9672003 .

Tomaszewska, Paulina, and Christoph Lampert. “Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior Shift.”https://doi.org/10.1109/icpr56361.2022.9956195__In 26th International Conference on Pattern Recognition, 2022:2128–34. Institute of Electrical and Electronics Engineers, 2022.https://doi.org/10.1109/icpr56361.2022.9956195 .

Iofinova, Eugenia B, Elena-Alexandra Peste, Mark Kurtz, and Dan-Adrian Alistarh. “How Well Do Sparse ImageNet Models Transfer?”https://doi.org/10.1109/cvpr52688.2022.01195__In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 12256–66. Institute of Electrical and Electronics Engineers, 2022.https://doi.org/10.1109/cvpr52688.2022.01195 .

Konstantinov, Nikola H, and Christoph Lampert. “Fairness-Aware PAC Learning from Corrupted Data.” Journal of Machine Learning Research. ML Research Press, 2022.

Konstantinov, Nikola H, and Christoph Lampert. “On the Impossibility of Fairness-Aware Learning from Corrupted Data.” In Proceedings of Machine Learning Research, 171:59–83. ML Research Press, 2022.

Konstantinov, Nikola H. “Robustness and Fairness in Machine Learning.”https://doi.org/10.15479/at:ista:10799__Institute of Science and Technology Austria, 2022.https://doi.org/10.15479/at:ista:10799 .

Volhejn, Vaclav, and Christoph Lampert. “Does SGD Implicitly Optimize for Smoothness?”https://doi.org/10.1007/978-3-030-71278-5_18__In 42nd German Conference on Pattern Recognition, 12544:246–59. LNCS. Springer, 2021.https://doi.org/10.1007/978-3-030-71278-5_18 .

Phuong, Mary, and Christoph Lampert. “The Inductive Bias of ReLU Networks on Orthogonally Separable Data.” In 9th International Conference on Learning Representations, 2021.

Konstantinov, Nikola H, and Christoph Lampert. “Fairness through Regularization for Learning to Rank.”https://doi.org/10.48550/arXiv.2102.05996__ArXiv, n.d.https://doi.org/10.48550/arXiv.2102.05996 .

Phuong, Mary. “Underspecification in Deep Learning.”https://doi.org/10.15479/AT:ISTA:9418__Institute of Science and Technology Austria, 2021.https://doi.org/10.15479/AT:ISTA:9418 .

Lampert, Christoph. “Zero-Shot Learning.”https://doi.org/10.1007/978-3-030-63416-2_874__In Computer Vision, edited by Katsushi Ikeuchi, 2nd ed., 1395–97. Cham: Springer, 2021.https://doi.org/10.1007/978-3-030-63416-2_874 .

Anciukevicius, Titas, Christoph Lampert, and Paul M Henderson. “Object-Centric Image Generation with Factored Depths, Locations, and Appearances.” ArXiv, n.d.

Henderson, Paul M, and Christoph Lampert. “Unsupervised Object-Centric Video Generation and Decomposition in 3D.” In 34th Conference on Neural Information Processing Systems, 33:3106–3117. Curran Associates, 2020.

Henderson, Paul M, and Vittorio Ferrari. “Learning Single-Image 3D Reconstruction by Generative Modelling of Shape, Pose and Shading.”https://doi.org/10.1007/s11263-019-01219-8__International Journal of Computer Vision. Springer Nature, 2020.https://doi.org/10.1007/s11263-019-01219-8 .

Royer, Amélie, and Christoph Lampert. “Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios.”https://doi.org/10.1109/WACV45572.2020.9093288__In IEEE Winter Conference on Applications of Computer Vision. IEEE, 2020.https://doi.org/10.1109/WACV45572.2020.9093288 .

Royer, Amélie, and Christoph Lampert. “A Flexible Selection Scheme for Minimum-Effort Transfer Learning.”https://doi.org/10.1109/WACV45572.2020.9093635__In 2020 IEEE Winter Conference on Applications of Computer Vision. IEEE, 2020.https://doi.org/10.1109/WACV45572.2020.9093635 .

Royer, Amélie, Konstantinos Bousmalis, Stephan Gouws, Fred Bertsch, Inbar Mosseri, Forrester Cole, and Kevin Murphy. “XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings.”https://doi.org/10.1007/978-3-030-30671-7_3__In Domain Adaptation for Visual Understanding, edited by Richa Singh, Mayank Vatsa, Vishal M. Patel, and Nalini Ratha, 33–49. Springer Nature, 2020.https://doi.org/10.1007/978-3-030-30671-7_3 .

Phuong, Mary, and Christoph Lampert. “Functional vs. Parametric Equivalence of ReLU Networks.” In 8th International Conference on Learning Representations, 2020.

Konstantinov, Nikola H, Elias Frantar, Dan-Adrian Alistarh, and Christoph Lampert. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” In Proceedings of the 37th International Conference on Machine Learning, 119:5416–25. ML Research Press, 2020.

Royer, Amélie. “Leveraging Structure in Computer Vision Tasks for Flexible Deep Learning Models.”https://doi.org/10.15479/AT:ISTA:8390__Institute of Science and Technology Austria, 2020.https://doi.org/10.15479/AT:ISTA:8390 .

Henderson, Paul M, Vagia Tsiminaki, and Christoph Lampert. “Leveraging 2D Data to Learn Textured 3D Mesh Generation.”https://doi.org/10.1109/CVPR42600.2020.00752__In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7498–7507. IEEE, 2020.https://doi.org/10.1109/CVPR42600.2020.00752 .

Sun, Rémy, and Christoph Lampert. “KS(Conf): A Light-Weight Test If a Multiclass Classifier Operates Outside of Its Specifications.”https://doi.org/10.1007/s11263-019-01232-x__International Journal of Computer Vision. Springer Nature, 2020.https://doi.org/10.1007/s11263-019-01232-x .

Kersting, Kristian, Christoph Lampert, and Constantin Rothkopf, eds. Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt. 1st ed. Wiesbaden: Springer Nature, 2019.https://doi.org/10.1007/978-3-658-26763-6 .

Ashok, Pranav, Tomás Brázdil, Krishnendu Chatterjee, Jan Ketínský, Christoph Lampert, and Viktor Toman. “Strategy Representation by Decision Trees with Linear Classifiers.”https://doi.org/10.1007/978-3-030-30281-8_7__In 16th International Conference on Quantitative Evaluation of Systems, 11785:109–28. Springer Nature, 2019.https://doi.org/10.1007/978-3-030-30281-8_7 .

Xian, Yongqin, Christoph Lampert, Bernt Schiele, and Zeynep Akata. “Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly.”https://doi.org/10.1109/tpami.2018.2857768__IEEE Transactions on Pattern Analysis and Machine Intelligence. Institute of Electrical and Electronics Engineers (IEEE), 2019.https://doi.org/10.1109/tpami.2018.2857768 .

Phuong, Mary, and Christoph Lampert. “Distillation-Based Training for Multi-Exit Architectures.”https://doi.org/10.1109/ICCV.2019.00144__In IEEE International Conference on Computer Vision, 2019–October:1355–64. IEEE, 2019.https://doi.org/10.1109/ICCV.2019.00144 .

Kolesnikov, Alexander, Alina Kuznetsova, Christoph Lampert, and Vittorio Ferrari. “Detecting Visual Relationships Using Box Attention.”https://doi.org/10.1109/ICCVW.2019.00217__In Proceedings of the 2019 International Conference on Computer Vision Workshop. IEEE, 2019.https://doi.org/10.1109/ICCVW.2019.00217 .

Phuong, Mary, and Christoph Lampert. “Towards Understanding Knowledge Distillation.” In Proceedings of the 36th International Conference on Machine Learning, 97:5142–51. ML Research Press, 2019.

Konstantinov, Nikola H, and Christoph Lampert. “Robust Learning from Untrusted Sources.” In Proceedings of the 36th International Conference on Machine Learning, 97:3488–98. ML Research Press, 2019.

Sun, Rémy, and Christoph Lampert. “KS(Conf): A Light-Weight Test If a ConvNet Operates Outside of Its Specifications,”https://doi.org/10.1007/978-3-030-12939-2_18__11269:244–59. Springer Nature, 2019.https://doi.org/10.1007/978-3-030-12939-2_18 .

Zimin, Alexander. “Learning from Dependent Data.”https://doi.org/10.15479/AT:ISTA:TH1048__Institute of Science and Technology Austria, 2018.https://doi.org/10.15479/AT:ISTA:TH1048 .

Kolesnikov, Alexander. “Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images.”https://doi.org/10.15479/AT:ISTA:th_1021__Institute of Science and Technology Austria, 2018.https://doi.org/10.15479/AT:ISTA:th_1021 .

Ringbauer, Harald, Alexander Kolesnikov, David Field, and Nicholas H Barton. “Estimating Barriers to Gene Flow from Distorted Isolation-by-Distance Patterns.”https://doi.org/10.1534/genetics.117.300638__Genetics. Genetics Society of America, 2018.https://doi.org/10.1534/genetics.117.300638 .

Darrell, Trevor, Christoph Lampert, Nico Sebe, Ying Wu, and Yan Yan. “Guest Editors’ Introduction to the Special Section on Learning with Shared Information for Computer Vision and Multimedia Analysis.”https://doi.org/10.1109/TPAMI.2018.2804998__IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE, 2018.https://doi.org/10.1109/TPAMI.2018.2804998 .

Uijlings, Jasper, Ksenia Konyushkova, Christoph Lampert, and Vittorio Ferrari. “Learning Intelligent Dialogs for Bounding Box Annotation.”https://doi.org/10.1109/cvpr.2018.00956__In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9175–84. IEEE, 2018.https://doi.org/10.1109/cvpr.2018.00956 .

Sahoo, Subham, Christoph Lampert, and Georg S Martius. “Learning Equations for Extrapolation and Control.” In Proceedings of the 35th International Conference on Machine Learning, 80:4442–50. ML Research Press, 2018.

Kuzborskij, Ilja, and Christoph Lampert. “Data-Dependent Stability of Stochastic Gradient Descent.” In Proceedings of the 35 Th International Conference on Machine Learning, 80:2815–24. ML Research Press, 2018.

Alistarh, Dan-Adrian, Torsten Hoefler, Mikael Johansson, Nikola H Konstantinov, Sarit Khirirat, and Cedric Renggli. “The Convergence of Sparsified Gradient Methods.” In Advances in Neural Information Processing Systems 31, Volume 2018:5973–83. Neural Information Processing Systems Foundation, 2018.

Deny, Stephane, Olivier Marre, Vicente Botella-Soler, Georg S Martius, and Gasper Tkadik. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.”https://doi.org/10.15479/AT:ISTA:98__Institute of Science and Technology Austria, 2018.https://doi.org/10.15479/AT:ISTA:98 .

Der, Ralf, and Georg S Martius. “Dynamical Self Consistency Leads to Behavioral Development and Emergent Social Interactions in Robots.”https://doi.org/10.1109/DEVLRN.2016.7846789__IEEE, 2017.https://doi.org/10.1109/DEVLRN.2016.7846789 .

Der, Ralf, and Georg S Martius. “Self Organized Behavior Generation for Musculoskeletal Robots.”https://doi.org/10.3389/fnbot.2017.00008__Frontiers in Neurorobotics. Frontiers Research Foundation, 2017.https://doi.org/10.3389/fnbot.2017.00008 .

Martius, Georg S, and Christoph Lampert. “Extrapolation and Learning Equations.” In 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings. International Conference on Learning Representations, 2017.

Pielorz, Jasmin, Matthias Prandtstetter, Markus Straub, and Christoph Lampert. “Optimal Geospatial Volunteer Allocation Needs Realistic Distances.”https://doi.org/10.1109/BigData.2017.8258375__In 2017 IEEE International Conference on Big Data, 3760–63. IEEE, 2017.https://doi.org/10.1109/BigData.2017.8258375 .

Kolesnikov, Alexander, and Christoph Lampert. “PixelCNN Models with Auxiliary Variables for Natural Image Modeling.” In 34th International Conference on Machine Learning, 70:1905–14. JMLR, 2017.

Rebuffi, Sylvestre Alvise, Alexander Kolesnikov, Georg Sperl, and Christoph Lampert. “ICaRL: Incremental Classifier and Representation Learning,”https://doi.org/10.1109/CVPR.2017.587__2017:5533–42. IEEE, 2017.https://doi.org/10.1109/CVPR.2017.587 .

Royer, Amélie, Alexander Kolesnikov, and Christoph Lampert. “Probabilistic Image Colorization,”https://doi.org/10.5244/c.31.85__85.1-85.12. BMVA Press, 2017.https://doi.org/10.5244/c.31.85 .

Zimin, Alexander, and Christoph Lampert. “Learning Theory for Conditional Risk Minimization,” 54:213–22. ML Research Press, 2017.

Pentina, Anastasia, and Christoph Lampert. “Multi-Task Learning with Labeled and Unlabeled Tasks,” 70:2807–16. ML Research Press, 2017.

Pentina, Anastasia, and Ruth Urner. “Lifelong Learning with Weighted Majority Votes,” 29:3619–27. Neural Information Processing Systems, 2016.

Kolesnikov, Alexander, and Christoph Lampert. “Improving Weakly-Supervised Object Localization by Micro-Annotation.”https://doi.org/10.5244/C.30.92__In Proceedings of the British Machine Vision Conference 2016, 2016–September:92.1-92.12. BMVA Press, 2016.https://doi.org/10.5244/C.30.92 .

Martius, Georg S, Raphael Hostettler, Alois Knoll, and Ralf Der. “Compliant Control for Soft Robots: Emergent Behavior of a Tendon Driven Anthropomorphic Arm,”https://doi.org/10.1109/IROS.2016.7759138__Vol. 2016–November. IEEE, 2016.https://doi.org/10.1109/IROS.2016.7759138 .

Kolesnikov, Alexander, and Christoph Lampert. “Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation,”https://doi.org/10.1007/978-3-319-46493-0_42__9908:695–711. Springer, 2016.https://doi.org/10.1007/978-3-319-46493-0_42 .

Pielorz, Jasmin, and Christoph Lampert. “Optimal Geospatial Allocation of Volunteers for Crisis Management.”https://doi.org/10.1109/ICT-DM.2015.7402041__IEEE, 2016.https://doi.org/10.1109/ICT-DM.2015.7402041 .

Martius, Georg S, Rafael Hostettler, Alois Knoll, and Ralf Der. “Self-Organized Control of an Tendon Driven Arm by Differential Extrinsic Plasticity.”https://doi.org/10.7551/978-0-262-33936-0-ch029__In Proceedings of the Artificial Life Conference 2016, 28:142–43. MIT Press, 2016.https://doi.org/10.7551/978-0-262-33936-0-ch029 .

Pentina, Anastasia. “Theoretical Foundations of Multi-Task Lifelong Learning.”https://doi.org/10.15479/AT:ISTA:TH_776__Institute of Science and Technology Austria, 2016.https://doi.org/10.15479/AT:ISTA:TH_776 .

Pentina, Anastasia, and Christoph Lampert. “Lifelong Learning with Non-i.i.d. Tasks,” 2015:1540–48. Neural Information Processing Systems, 2015.

Xia, Wei, Csaba Domokos, Junjun Xiong, Loongfah Cheong, and Shuicheng Yan. “Segmentation over Detection via Optimal Sparse Reconstructions.”https://doi.org/10.1109/TCSVT.2014.2379972__IEEE Transactions on Circuits and Systems for Video Technology. IEEE, 2015.https://doi.org/10.1109/TCSVT.2014.2379972 .

Der, Ralf, and Georg S Martius. “Novel Plasticity Rule Can Explain the Development of Sensorimotor Intelligence.”https://doi.org/10.1073/pnas.1508400112__PNAS. National Academy of Sciences, 2015.https://doi.org/10.1073/pnas.1508400112 .

Pentina, Anastasia, and Shai Ben David. “Multi-Task and Lifelong Learning of Kernels,”https://doi.org/10.1007/978-3-319-24486-0_13__9355:194–208. Springer, 2015.https://doi.org/10.1007/978-3-319-24486-0_13 .

Shah, Neel, Vladimir Kolmogorov, and Christoph Lampert. “A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly Max-Oracle,”https://doi.org/10.1109/CVPR.2015.7298890__2737–45. IEEE, 2015.https://doi.org/10.1109/CVPR.2015.7298890 .

Royer, Amélie, and Christoph Lampert. “Classifier Adaptation at Prediction Time,”https://doi.org/10.1109/CVPR.2015.7298746__1401–9. IEEE, 2015.https://doi.org/10.1109/CVPR.2015.7298746 .

Lampert, Christoph. “Predicting the Future Behavior of a Time-Varying Probability Distribution,”https://doi.org/10.1109/CVPR.2015.7298696__942–50. IEEE, 2015.https://doi.org/10.1109/CVPR.2015.7298696 .

Pentina, Anastasia, Viktoriia Sharmanska, and Christoph Lampert. “Curriculum Learning of Multiple Tasks,”https://doi.org/10.1109/CVPR.2015.7299188__5492–5500. IEEE, 2015.https://doi.org/10.1109/CVPR.2015.7299188 .

Martius, Georg S, and Eckehard Olbrich. “Quantifying Self-Organizing Behavior of Autonomous Robots.”https://doi.org/10.7551/978-0-262-33027-5-ch018__In Proceedings of the 13th European Conference on Artificial Life, 78. MIT Press, 2015.https://doi.org/10.7551/978-0-262-33027-5-ch018 .

Sharmanska, Viktoriia. “Learning with Attributes for Object Recognition: Parametric and Non-Parametrics Views.”https://doi.org/10.15479/at:ista:1401__Institute of Science and Technology Austria, 2015.https://doi.org/10.15479/at:ista:1401 .

Martius, Georg S, and Eckehard Olbrich. “Quantifying Emergent Behavior of Autonomous Robots.”https://doi.org/10.3390/e17107266__Entropy. MDPI, 2015.https://doi.org/10.3390/e17107266 .

Muelling, Katharina, Oliver Kroemer, Christoph Lampert, and Bernhard Schölkopf. “Movement Templates for Learning of Hitting and Batting.”https://doi.org/10.1007/978-3-319-03194-1_3__In Learning Motor Skills, edited by Jens Kober and Jan Peters, 97:69–82. From Algorithms to Robot Experiments. Springer, 2014.https://doi.org/10.1007/978-3-319-03194-1_3 .

Hernandez Lobato, Daniel, Viktoriia Sharmanska, Kristian Kersting, Christoph Lampert, and Novi Quadrianto. “Mind the Nuisance: Gaussian Process Classification Using Privileged Noise.” In Advances in Neural Information Processing Systems, 1:837–45. Neural Information Processing Systems, 2014.

Morvant, Emilie, Amaury Habrard, and Stéphane Ayache. “Majority Vote of Diverse Classifiers for Late Fusion.”https://doi.org/10.1007/978-3-662-44415-3_16__In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8621:153–62. Springer, 2014.https://doi.org/10.1007/978-3-662-44415-3_16 .

Kolesnikov, Alexander, Matthieu Guillaumin, Vittorio Ferrari, and Christoph Lampert. “Closed-Form Approximate CRF Training for Scalable Image Segmentation.”https://doi.org/10.1007/978-3-319-10578-9_36__In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), edited by David Fleet, Tomas Pajdla, Bernt Schiele, and Tinne Tuytelaars, 8691:550–65. Springer, 2014.https://doi.org/10.1007/978-3-319-10578-9_36 .

Khamis, Sameh, and Christoph Lampert. “CoConut: Co-Classification with Output Space Regularization.” In Proceedings of the British Machine Vision Conference 2014. BMVA Press, 2014.

Sydorov, Vladyslav, Mayu Sakurada, and Christoph Lampert. “Deep Fisher Kernels – End to End Learning of the Fisher Kernel GMM Parameters.”https://doi.org/10.1109/CVPR.2014.182__In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1402–9. IEEE, 2014.https://doi.org/10.1109/CVPR.2014.182 .

Bellet, Aurélien, Amaury Habrard, Emilie Morvant, and Marc Sebban. “Learning a Priori Constrained Weighted Majority Votes.”https://doi.org/10.1007/s10994-014-5462-z__Machine Learning. Springer, 2014.https://doi.org/10.1007/s10994-014-5462-z .

Morvant, Emilie. “Adaptation de Domaine de Vote de Majorité Par Auto-Étiquetage Non Itératif,” 1:49–58. Elsevier, 2014.

Pentina, Anastasia, and Christoph Lampert. “A PAC-Bayesian Bound for Lifelong Learning,” 32:991–99. ML Research Press, 2014.

Kazmar, Tomas, Evgeny Kvon, Alexander Stark, and Christoph Lampert. “Drosophila Embryo Stage Annotation Using Label Propagation.”https://doi.org/10.1109/ICCV.2013.139__IEEE, 2013.https://doi.org/10.1109/ICCV.2013.139 .

Sharmanska, Viktoriia, Novi Quadrianto, and Christoph Lampert. “Learning to Rank Using Privileged Information,”https://doi.org/10.1109/ICCV.2013.107__825–32. IEEE, 2013.https://doi.org/10.1109/ICCV.2013.107 .

Lampert, Christoph, Hannes Nickisch, and Stefan Harmeling. “Attribute-Based Classification for Zero-Shot Learning of Object Categories.”https://doi.org/10.1109/TPAMI.2013.140__IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE, 2013.https://doi.org/10.1109/TPAMI.2013.140 .

Quadrianto, Novi, Viktoriia Sharmanska, David Knowles, and Zoubin Ghahramani. “The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models.” In Proceedings of the 29th Conference Uncertainty in Artificial Intelligence, 527–36. AUAI Press, 2013.

Chen, Chao, Vladimir Kolmogorov, Zhu Yan, Dimitris Metaxas, and Christoph Lampert. “Computing the M Most Probable Modes of a Graphical Model,” 31:161–69. JMLR, 2013.

Tommasi, Tatiana, Novi Quadrianto, Barbara Caputo, and Christoph Lampert. “Beyond Dataset Bias: Multi-Task Unaligned Shared Knowledge Transfer.”https://doi.org/10.1007/978-3-642-37331-2_1__Lecture Notes in Computer Science. Springer, 2013.https://doi.org/10.1007/978-3-642-37331-2_1 .

Quadrianto, Novi, and Christoph Lampert. “Kernel Based Learning.”https://doi.org/10.1007/978-1-4419-9863-7_604__In Encyclopedia of Systems Biology, edited by Werner Dubitzky, Olaf Wolkenhauer, Kwang Cho, and Hiroki Yokota, 3:1069–1069. Springer, 2013.https://doi.org/10.1007/978-1-4419-9863-7_604 .

Lampert, Christoph. “Dynamic Pruning of Factor Graphs for Maximum Marginal Prediction,” 1:82–90. Neural Information Processing Systems, 2012.

Blaschko, Matthew, and Christoph Lampert. “Guest Editorial: Special Issue on Structured Prediction and Inference.”https://doi.org/10.1007/s11263-012-0530-y__International Journal of Computer Vision. Springer, 2012.https://doi.org/10.1007/s11263-012-0530-y .

Sharmanska, Viktoriia, Novi Quadrianto, and Christoph Lampert. “Augmented Attribute Representations,”https://doi.org/10.1007/978-3-642-33715-4_18__7576:242–55. Springer, 2012.https://doi.org/10.1007/978-3-642-33715-4_18 .

Müller, Andreas, Sebastian Nowozin, and Christoph Lampert. “Information Theoretic Clustering Using Minimal Spanning Trees,”https://doi.org/10.1007/978-3-642-32717-9_21__7476:205–15. Springer, 2012.https://doi.org/10.1007/978-3-642-32717-9_21 .

Lampert, Christoph, and Jan Peters. “Real-Time Detection of Colored Objects in Multiple Camera Streams with off-the-Shelf Hardware Components.”https://doi.org/10.1007/s11554-010-0168-3__Journal of Real-Time Image Processing. Springer, 2012.https://doi.org/10.1007/s11554-010-0168-3 .

Korc, Filip, Vladimir Kolmogorov, and Christoph Lampert. “Approximating Marginals Using Discrete Energy Minimization.” ICML, 2012.

Korc, Filip, Vladimir Kolmogorov, and Christoph Lampert. Approximating Marginals Using Discrete Energy Minimization. IST Austria, 2012.https://doi.org/10.15479/AT:IST-2012-0003 .

Kroemer, Oliver, Christoph Lampert, and Jan Peters. “Multi-Modal Learning for Dynamic Tactile Sensing.” Deutsches Zentrum für Luft und Raumfahrt, 2012.

Quadrianto, Novi, Christoph Lampert, and Chao Chen. “The Most Persistent Soft-Clique in a Set of Sampled Graphs.” In Proceedings of the 29th International Conference on Machine Learning, 211–18. ML Research Press, 2012.

Wang, Zhikun, Christoph Lampert, Katharina Mülling, Bernhard Schölkopf, and Jan Peters. “Learning Anticipation Policies for Robot Table Tennis,”https://doi.org/10.1109/IROS.2011.6094892__332–37. IEEE, 2011.https://doi.org/10.1109/IROS.2011.6094892 .

Blaschko, Matthew, Jacquelyn Shelton, Andreas Bartels, Christoph Lampert, and Arthur Gretton. “Semi Supervised Kernel Canonical Correlation Analysis with Application to Human FMRI.”https://doi.org/10.1016/j.patrec.2011.02.011__Pattern Recognition Letters. Elsevier, 2011.https://doi.org/10.1016/j.patrec.2011.02.011 .

Kroemer, Oliver, Christoph Lampert, and Jan Peters. “Learning Dynamic Tactile Sensing with Robust Vision Based Training.”https://doi.org/10.1109/TRO.2011.2121130__IEEE Transactions on Robotics. IEEE, 2011.https://doi.org/10.1109/TRO.2011.2121130 .

Chen, Chao, Daniel Freedman, and Christoph Lampert. Enforcing Topological Constraints in Random Field Image Segmentation. IST Austria, 2011.https://doi.org/10.15479/AT:IST-2011-0002 .

Chen, Chao, Daniel Freedman, and Christoph Lampert. “Enforcing Topological Constraints in Random Field Image Segmentation.”https://doi.org/10.1109/CVPR.2011.5995503__In CVPR: Computer Vision and Pattern Recognition, 2089–96. IEEE, 2011.https://doi.org/10.1109/CVPR.2011.5995503 .

Lampert, Christoph. “Maximum Margin Multi-Label Structured Prediction.” Neural Information Processing Systems, 2011.

Lampert, Christoph. Maximum Margin Multi Label Structured Prediction. NIPS: Neural Information Processing Systems. Neural Information Processing Systems Foundation, 2011.

Nowozin, Sebastian, and Christoph Lampert. “Structured Learning and Prediction in Computer Vision.”https://doi.org/10.1561/0600000033__Foundations and Trends in Computer Graphics and Vision. Now Publishers, 2011.https://doi.org/10.1561/0600000033 .

Quadrianto, Novi, and Christoph Lampert. “Learning Multi-View Neighborhood Preserving Projections,” 425–32. ML Research Press, 2011.

Lampert, Christoph, and Oliver Krömer. “Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning,”https://doi.org/10.1007/978-3-642-15552-9_41__6312:566–79. Springer, 2010.https://doi.org/10.1007/978-3-642-15552-9_41 .

Nowozin, Sebastian, Peter Gehler, and Christoph Lampert. “On Parameter Learning in CRF-Based Approaches to Object Class Image Segmentation,”https://doi.org/10.1007/978-3-642-15567-3_8__6316:98–111. Springer, 2010.https://doi.org/10.1007/978-3-642-15567-3_8 .