Peste, Elena-Alexandra, et al. “CrAM: A Compression-Aware Minimizer.” 11th International Conference on Learning Representations .

Tomaszewska, Paulina, and Christoph Lampert. “On the Implementation of Baselines and Lightweight Conditional Model Extrapolation (LIMES) under Class-Prior Shift.”10.1007/978-3-031-40773-4_6__International Workshop on Reproducible Research in Pattern Recognition, vol. 14068, Springer Nature, 2023, pp. 67–73, doi:10.1007/978-3-031-40773-4_6.

Súkeník, Peter, et al. “Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model.” 37th Annual Conference on Neural Information Processing Systems.

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

Scott, Jonathan A., et al. “Cross-Client Label Propagation for Transductive Federated Learning.”10.48550/arXiv.2210.06434__ArXiv, 2210.06434, doi:10.48550/arXiv.2210.06434.

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

Iofinova, Eugenia B., et al. “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.”10.1007/978-3-031-19803-8_21__Computer Vision – ECCV 2022, vol. 13681, Springer Nature, 2022, pp. 350–65, doi:10.1007/978-3-031-19803-8_21.

Lampert, Jasmin, and Christoph Lampert. “Overcoming Rare-Language Discrimination in Multi-Lingual Sentiment Analysis.”10.1109/bigdata52589.2021.9672003__2021 IEEE International Conference on Big Data, IEEE, 2022, pp. 5185–92, doi:10.1109/bigdata52589.2021.9672003.

Tomaszewska, Paulina, and Christoph Lampert. “Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior Shift.”10.1109/icpr56361.2022.9956195__26th International Conference on Pattern Recognition, vol. 2022, Institute of Electrical and Electronics Engineers, 2022, pp. 2128–34, doi:10.1109/icpr56361.2022.9956195.

Konstantinov, Nikola H., and Christoph Lampert. “Fairness-Aware PAC Learning from Corrupted Data.” Journal of Machine Learning Research, vol. 23, ML Research Press, 2022, pp. 1–60.

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

Volhejn, Vaclav, and Christoph Lampert. “Does SGD Implicitly Optimize for Smoothness?”10.1007/978-3-030-71278-5_18__42nd German Conference on Pattern Recognition, vol. 12544, Springer, 2021, pp. 246–59, doi:10.1007/978-3-030-71278-5_18.

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

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

Lampert, Christoph. “Zero-Shot Learning.”10.1007/978-3-030-63416-2_874__Computer Vision, edited by Katsushi Ikeuchi, 2nd ed., Springer, 2021, pp. 1395–97, doi:10.1007/978-3-030-63416-2_874.

Anciukevicius, Titas, et al. “Object-Centric Image Generation with Factored Depths, Locations, and Appearances.” ArXiv, 2004.00642.

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

Royer, Amélie, and Christoph Lampert. “Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios.”10.1109/WACV45572.2020.9093288__IEEE Winter Conference on Applications of Computer Vision, 1716–1725, IEEE, 2020, doi:10.1109/WACV45572.2020.9093288.

Royer, Amélie, and Christoph Lampert. “A Flexible Selection Scheme for Minimum-Effort Transfer Learning.”10.1109/WACV45572.2020.9093635__2020 IEEE Winter Conference on Applications of Computer Vision, 2180–2189, IEEE, 2020, doi:10.1109/WACV45572.2020.9093635.

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

Konstantinov, Nikola H., et al. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” Proceedings of the 37th International Conference on Machine Learning, vol. 119, ML Research Press, 2020, pp. 5416–25.

Henderson, Paul M., et al. “Leveraging 2D Data to Learn Textured 3D Mesh Generation.”10.1109/CVPR42600.2020.00752__Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2020, pp. 7498–507, doi: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.”10.1007/s11263-019-01232-x__International Journal of Computer Vision, vol. 128, no. 4, Springer Nature, 2020, pp. 970–95, doi:10.1007/s11263-019-01232-x.

Kersting, Kristian, et al., editors. Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt. 1st ed., Springer Nature, 2019, doi:10.1007/978-3-658-26763-6.

Ashok, Pranav, et al. “Strategy Representation by Decision Trees with Linear Classifiers.”10.1007/978-3-030-30281-8_7__16th International Conference on Quantitative Evaluation of Systems, vol. 11785, Springer Nature, 2019, pp. 109–28, doi:10.1007/978-3-030-30281-8_7.

Xian, Yongqin, et al. “Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly.”10.1109/tpami.2018.2857768__IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 9, Institute of Electrical and Electronics Engineers (IEEE), 2019, pp. 2251–65, doi:10.1109/tpami.2018.2857768.

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

Kolesnikov, Alexander, et al. “Detecting Visual Relationships Using Box Attention.”10.1109/ICCVW.2019.00217__Proceedings of the 2019 International Conference on Computer Vision Workshop, 1749–1753, IEEE, 2019, doi:10.1109/ICCVW.2019.00217.

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

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

Sun, Rémy, and Christoph Lampert. KS(Conf): A Light-Weight Test If a ConvNet Operates Outside of Its Specifications. Vol. 11269, Springer Nature, 2019, pp. 244–59, doi:10.1007/978-3-030-12939-2_18.

Darrell, Trevor, et al. “Guest Editors’ Introduction to the Special Section on Learning with Shared Information for Computer Vision and Multimedia Analysis.”10.1109/TPAMI.2018.2804998__IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 5, IEEE, 2018, pp. 1029–31, doi:10.1109/TPAMI.2018.2804998.

Uijlings, Jasper, et al. “Learning Intelligent Dialogs for Bounding Box Annotation.”10.1109/cvpr.2018.00956__2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018, pp. 9175–84, doi:10.1109/cvpr.2018.00956.

Sahoo, Subham, et al. “Learning Equations for Extrapolation and Control.” Proceedings of the 35th International Conference on Machine Learning, vol. 80, ML Research Press, 2018, pp. 4442–50.

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

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

Pielorz, Jasmin, et al. “Optimal Geospatial Volunteer Allocation Needs Realistic Distances.”10.1109/BigData.2017.8258375__2017 IEEE International Conference on Big Data, IEEE, 2017, pp. 3760–63, doi:10.1109/BigData.2017.8258375.

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

Rebuffi, Sylvestre Alvise, et al. ICaRL: Incremental Classifier and Representation Learning. Vol. 2017, IEEE, 2017, pp. 5533–42, doi:10.1109/CVPR.2017.587.

Royer, Amélie, et al. Probabilistic Image Colorization. BMVA Press, 2017, p. 85.1-85.12, doi:10.5244/c.31.85.

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

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

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

Kolesnikov, Alexander, and Christoph Lampert. Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation. Vol. 9908, Springer, 2016, pp. 695–711, doi:10.1007/978-3-319-46493-0_42.

Pielorz, Jasmin, and Christoph Lampert. Optimal Geospatial Allocation of Volunteers for Crisis Management. 7402041, IEEE, 2016, doi:10.1109/ICT-DM.2015.7402041.

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

Shah, Neel, et al. A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly Max-Oracle. IEEE, 2015, pp. 2737–45, doi:10.1109/CVPR.2015.7298890.

Royer, Amélie, and Christoph Lampert. Classifier Adaptation at Prediction Time. IEEE, 2015, pp. 1401–09, doi:10.1109/CVPR.2015.7298746.

Lampert, Christoph. Predicting the Future Behavior of a Time-Varying Probability Distribution. IEEE, 2015, pp. 942–50, doi:10.1109/CVPR.2015.7298696.

Pentina, Anastasia, et al. Curriculum Learning of Multiple Tasks. IEEE, 2015, pp. 5492–500, doi:10.1109/CVPR.2015.7299188.

Muelling, Katharina, et al. “Movement Templates for Learning of Hitting and Batting.”10.1007/978-3-319-03194-1_3__Learning Motor Skills, edited by Jens Kober and Jan Peters, vol. 97, Springer, 2014, pp. 69–82, doi:10.1007/978-3-319-03194-1_3.

Hernandez Lobato, Daniel, et al. “Mind the Nuisance: Gaussian Process Classification Using Privileged Noise.” Advances in Neural Information Processing Systems, vol. 1, no. January, Neural Information Processing Systems, 2014, pp. 837–45.

Kolesnikov, Alexander, et al. “Closed-Form Approximate CRF Training for Scalable Image Segmentation.”10.1007/978-3-319-10578-9_36__Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), edited by David Fleet et al., vol. 8691, no. PART 3, Springer, 2014, pp. 550–65, doi:10.1007/978-3-319-10578-9_36.

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

Sydorov, Vladyslav, et al. “Deep Fisher Kernels – End to End Learning of the Fisher Kernel GMM Parameters.”10.1109/CVPR.2014.182__Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2014, pp. 1402–09, doi:10.1109/CVPR.2014.182.

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

Kazmar, Tomas, et al. Drosophila Embryo Stage Annotation Using Label Propagation. IEEE, 2013, doi:10.1109/ICCV.2013.139.

Sharmanska, Viktoriia, et al. Learning to Rank Using Privileged Information. IEEE, 2013, pp. 825–32, doi:10.1109/ICCV.2013.107.

Lampert, Christoph, et al. “Attribute-Based Classification for Zero-Shot Learning of Object Categories.”10.1109/TPAMI.2013.140__IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 3, IEEE, 2013, pp. 453–65, doi:10.1109/TPAMI.2013.140.

Chen, Chao, et al. Computing the M Most Probable Modes of a Graphical Model. Vol. 31, JMLR, 2013, pp. 161–69.

Tommasi, Tatiana, et al. Beyond Dataset Bias: Multi-Task Unaligned Shared Knowledge Transfer. Vol. 7724, Springer, 2013, pp. 1–15, doi:10.1007/978-3-642-37331-2_1.

Quadrianto, Novi, and Christoph Lampert. “Kernel Based Learning.”10.1007/978-1-4419-9863-7_604__Encyclopedia of Systems Biology, edited by Werner Dubitzky et al., vol. 3, Springer, 2013, pp. 1069–1069, doi:10.1007/978-1-4419-9863-7_604.

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

Blaschko, Matthew, and Christoph Lampert. “Guest Editorial: Special Issue on Structured Prediction and Inference.”10.1007/s11263-012-0530-y__International Journal of Computer Vision, vol. 99, no. 3, Springer, 2012, pp. 257–58, doi:10.1007/s11263-012-0530-y.

Sharmanska, Viktoriia, et al. Augmented Attribute Representations. Vol. 7576, no. PART 5, Springer, 2012, pp. 242–55, doi:10.1007/978-3-642-33715-4_18.

Müller, Andreas, et al. Information Theoretic Clustering Using Minimal Spanning Trees. Vol. 7476, Springer, 2012, pp. 205–15, doi: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.”10.1007/s11554-010-0168-3__Journal of Real-Time Image Processing, vol. 7, no. 1, Springer, 2012, pp. 31–41, doi:10.1007/s11554-010-0168-3.

Korc, Filip, et al. Approximating Marginals Using Discrete Energy Minimization. ICML, 2012.

Korc, Filip, et al. Approximating Marginals Using Discrete Energy Minimization. IST Austria, 2012, doi:10.15479/AT:IST-2012-0003.

Kroemer, Oliver, et al. Multi-Modal Learning for Dynamic Tactile Sensing. Deutsches Zentrum für Luft und Raumfahrt, 2012.

Quadrianto, Novi, et al. “The Most Persistent Soft-Clique in a Set of Sampled Graphs.” Proceedings of the 29th International Conference on Machine Learning, ML Research Press, 2012, pp. 211–18.

Wang, Zhikun, et al. Learning Anticipation Policies for Robot Table Tennis. IEEE, 2011, pp. 332–37, doi:10.1109/IROS.2011.6094892.

Blaschko, Matthew, et al. “Semi Supervised Kernel Canonical Correlation Analysis with Application to Human FMRI.”10.1016/j.patrec.2011.02.011__Pattern Recognition Letters, vol. 32, no. 11, Elsevier, 2011, pp. 1572–83, doi:10.1016/j.patrec.2011.02.011.

Kroemer, Oliver, et al. “Learning Dynamic Tactile Sensing with Robust Vision Based Training.”10.1109/TRO.2011.2121130__IEEE Transactions on Robotics, vol. 27, no. 3, IEEE, 2011, pp. 545–57, doi:10.1109/TRO.2011.2121130.

Chen, Chao, et al. Enforcing Topological Constraints in Random Field Image Segmentation. IST Austria, 2011, doi:10.15479/AT:IST-2011-0002.

Chen, Chao, et al. “Enforcing Topological Constraints in Random Field Image Segmentation.”10.1109/CVPR.2011.5995503__CVPR: Computer Vision and Pattern Recognition, IEEE, 2011, pp. 2089–96, doi: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.”10.1561/0600000033__Foundations and Trends in Computer Graphics and Vision, vol. 6, no. 3–4, Now Publishers, 2011, pp. 185–365, doi:10.1561/0600000033.

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

Nowozin, Sebastian, and Christoph Lampert. “Global Interactions in Random Field Models: A Potential Function Ensuring Connectedness.”10.1137/090752614__SIAM Journal on Imaging Sciences, vol. 3, no. 4 (Special Section on Optimization in Imaging Sciences), Society for Industrial and Applied Mathematics , 2010, pp. 1048–74, doi:10.1137/090752614.

Tang, Kevin, et al. Optimizing One-Shot Recognition with Micro-Set Learning. IEEE, 2010, pp. 3027–34, doi:10.1109/CVPR.2010.5540053.

Kober, Jens, et al. Movement Templates for Learning of Hitting and Batting. IEEE, 2010, pp. 853–58, doi:10.1109/ROBOT.2010.5509672.

Lampert, Christoph, and Oliver Krömer. Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning. Vol. 6312, Springer, 2010, pp. 566–79, doi:10.1007/978-3-642-15552-9_41.

Wanke, Jörn, et al. Topic Models for Semantic Video Compression. ACM, 2010, pp. 275–84, doi:10.1145/1743384.1743433.

Tuytelaars, Tinne, et al. “Unsupervised Object Discovery: A Comparison.”10.1007/s11263-009-0271-8__International Journal of Computer Vision, vol. 88, no. 2, Springer, 2010, pp. 284–302, doi:10.1007/s11263-009-0271-8.

Lampert, Christoph. An Efficient Divide-and-Conquer Cascade for Nonlinear Object Detection. IEEE, 2010, pp. 1022–29, doi:10.1109/CVPR.2010.5540107.

Nowozin, Sebastian, et al. On Parameter Learning in CRF-Based Approaches to Object Class Image Segmentation. Vol. 6316, Springer, 2010, pp. 98–111, doi:10.1007/978-3-642-15567-3_8.

Blaschko, Matthew, et al. “Semi-Supervised Analysis of Human FMRI Data.” BBCI: Berlin Brain-Computer Interface Workshop - Advances in Neurotechnology, Berlin Institute of Technology, 2009.

Blaschko, Matthew, and Christoph Lampert. Object Localization with Global and Local Context Kernels. BMVA Press, 2009, pp. 1–11, doi:10.5244/C.23.63.

Lampert, Christoph, et al. Learning to Detect Unseen Object Classes by Between-Class Attribute Transfer. IEEE, 2009, pp. 951–58, doi:10.1109/CVPR.2009.5206594.

Lampert, Christoph, and Jan Peters. Active Structured Learning for High-Speed Object Detection. Vol. 5748, Springer, 2009, pp. 221–31, doi:10.1007/978-3-642-03798-6_23.

Lampert, Christoph, and Jan Peters. “A High-Speed Object Tracker from off-the-Shelf Components.” ICCV: International Conference on Computer Vision, IEEE, 2009.

Lampert, Christoph, and Matthew Blaschko. “Structured Prediction by Joint Kernel Support Estimation.”10.1007/s10994-009-5111-0__Machine Learning, vol. 77, no. 2–3, Springer, 2009, pp. 249–69, doi:10.1007/s10994-009-5111-0.

Dhillon, Paramveer, et al. Combining Appearance and Motion for Human Action Classification in Videos. no. 174, IEEE, 2009, pp. 22–29, doi:10.1109/CVPRW.2009.5204237.

Lampert, Christoph, et al. “Efficient Subwindow Search: A Branch and Bound Framework for Object Localization.”10.1109/TPAMI.2009.144__IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 12, IEEE, 2009, pp. 2129–42, doi:10.1109/TPAMI.2009.144.

Dhillon, Paramveer, et al. Combining Appearance and Motion for Human Action Classification in Videos. IEEE, 2009, pp. 22–29, doi:10.1109/CVPRW.2009.5204237.

Lampert, Christoph. Kernel Methods in Computer Vision. Vol. 4, now publishers, 2009, doi:10.1561/0600000027.

Nowozin, Sebastian, and Christoph Lampert. Global Connectivity Potentials for Random Field Models. IEEE, 2009, pp. 818–25, doi:10.1109/CVPR.2009.5206567.

Lampert, Christoph. Detecting Objects in Large Image Collections and Videos by Efficient Subimage Retrieval. IEEE, 2009, pp. 987–94, doi:10.1109/ICCV.2009.5459359.

Blaschko, Matthew, and Christoph Lampert. Learning to Localize Objects with Structured Output Regression. Vol. 5302, Springer, 2008, pp. 2–15, doi:10.1007/978-3-540-88682-2_2.

Lampert, Christoph. Partitioning of Image Datasets Using Discriminative Context Information. IEEE, 2008, pp. 1–8, doi:10.1109/CVPR.2008.4587448.

Lampert, Christoph, and Matthew Blaschko. A Multiple Kernel Learning Approach to Joint Multi-Class Object Detection. Vol. 5096, Springer, 2008, pp. 31–40, doi:10.1007/978-3-540-69321-5_4.

Lampert, Christoph, et al. Beyond Sliding Windows: Object Localization by Efficient Subwindow Search. IEEE, 2008, pp. 1–8, doi:10.1109/CVPR.2008.4587586.

Blaschko, Matthew, et al. Semi-Supervised Laplacian Regularization of Kernel Canonical Correlation Analysis. Vol. 5211, no. Part 1, Springer, 2008, pp. 133–45, doi:10.1007/978-3-540-87479-9_27.

Goldstein, Markus, et al. Bayes Optimal DDoS Mitigation by Adaptive History-Based IP Filtering. IEEE, 2008, pp. 174–79, doi:10.1109/ICN.2008.64.

Lampert, Christoph, and Matthew Blaschko. Joint Kernel Support Estimation for Structured Prediction. Curran Associates, Inc., 2008, pp. 1–4.

Blaschko, Matthew, and Christoph Lampert. Correlational Spectral Clustering. IEEE, 2008, pp. 1–8, doi:10.1109/CVPR.2008.4587353.

Blaschko, Matthew, et al. “Efficient Subwindow Search for Object Localization.” Unknown, no. 164, Max-Planck-Institute for Biological Cybernetics, 2007.

Ulges, Adrian, et al. Optimal Dominant Motion Estimation Using Adaptive Search of Transformation Space. Vol. 4713, Springer, 2007, pp. 204–13, doi:10.1007/978-3-540-74936-3_21.

Ulges, Adrian, et al. Optimal Dominant Motion Estimation Using Adaptive Search of Transformation Space. Vol. 4713, Springer, 2007, pp. 204–13, doi:10.1007/978-3-540-74936-3_21.

Lampert, Christoph, and Thomas Breuel. Objective Quality Measurement for Geometric Document Image Restoration. Springer, 2006.

Lampert, Christoph. Machine Learning for Video Compression: Macroblock Mode Decision. IEEE, 2006, pp. 936–40, doi:10.1109/ICPR.2006.778.

Ali, Haider, et al. Satellite Tracks Removal in Astronomical Images. Vol. 4225, Springer, 2006, pp. 892–901, doi:10.1007/11892755_92.

Ulges, Adrian, et al. Spatiogram-Based Shot Distances for Video Retrieval. NIST (National Institute of Standards and Technology, US Department of Commerce), 2006, pp. 1–10.

Lampert, Christoph, et al. Printing Technique Classification for Document Counterfeit Detection. Vol. 1, IEEE, 2006, pp. 639–634, doi:10.1109/ICCIAS.2006.294214.

Lampert, Christoph, and Oliver Wirjadi. “An Optimal Non-Orthogonal Separation of the Anisotropic Gaussian Convolution Filter.” 10.1109/TIP.2006.877501 __IEEE Transactions on Image Processing (TIP), vol. 15, no. 11, IEEE, 2006, pp. 3501–13, doi: 10.1109/TIP.2006.877501 .

Lampert, Christoph, and Oliver Wirjadi. Anisotropic Gaussian Filtering Using Fixed Point Arithmetic. IEEE, 2006, pp. 1565–68, doi:10.1109/ICIP.2006.312606.

Keysers, Daniel, et al. Color Image Dequantization by Constrained Diffusion. Vol. 6058, SPIE, 2006, doi:10.1117/12.648713.

Ulges, Adrian, et al. Document Image Dewarping Using Robust Estimation of Curled Text Lines. Vol. 2, IEEE, 2005, pp. 1001–05, doi: 10.1109/ICDAR.2005.90.

Lampert, Christoph, et al. Oblivious Document Capture and Real-Time Retrieval. CBDAR, 2005, pp. 79–86.

Lampert, Christoph. “Boundary Regularity of Admissible Operators.”10.5565/PUBLMAT_49105_08__Publicacions Matemàtiques, vol. 49, no. 1, Universitat Autònoma de Barcelona, Departament de Matemàtique, 2005, pp. 179–95, doi:10.5565/PUBLMAT_49105_08.

Ulges, Adrian, et al. Document Capture Using Stereo Vision. ACM, 2004, pp. 198–200, doi:10.1145/1030397.1030434.

Lampert, Christoph. “The Neumann Operator in Strictly Pseudoconvex Domains with Weighted Bergman Metric .” Bonner Mathematische Schriften, vol. 356, Universität Bonn, Fachbibliothek Mathematik, 2003, pp. 1–165.