Zakerinia, Hossein, et al. “Communication-Efficient Federated Learning with Data and Client Heterogeneity.” Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, vol. 238, ML Research Press, 2024, pp. 3448–56.

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

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

Henderson, Paul M., et al. “Deep Learning Extraction of Band Structure Parameters from Density of States: A Case Study on Trilayer Graphene.”10.1103/physrevb.108.125411__Physical Review B, vol. 108, no. 12, 125411, American Physical Society, 2023, doi: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.”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.

Jakubík, Jozef, et al. “Against the Flow of Time with Multi-Output Models.”10.2478/msr-2023-0023__Measurement Science Review, vol. 23, no. 4, Sciendo, 2023, pp. 175–83, doi:10.2478/msr-2023-0023.

Iofinova, Eugenia B., et al. “Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures.”10.1109/cvpr52729.2023.02334__2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2023, pp. 24364–73, doi:10.1109/cvpr52729.2023.02334.

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.

Iofinova, Eugenia B., et al. “How Well Do Sparse ImageNet Models Transfer?”10.1109/cvpr52688.2022.01195__2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 12256–66, doi:10.1109/cvpr52688.2022.01195.

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.

Konstantinov, Nikola H. Robustness and Fairness in Machine Learning. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:10799.

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.

Phuong, Mary. Underspecification in Deep Learning. Institute of Science and Technology Austria, 2021, doi:10.15479/AT:ISTA:9418.

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.

Henderson, Paul M., and Vittorio Ferrari. “Learning Single-Image 3D Reconstruction by Generative Modelling of Shape, Pose and Shading.”10.1007/s11263-019-01219-8__International Journal of Computer Vision, vol. 128, Springer Nature, 2020, pp. 835–54, doi:10.1007/s11263-019-01219-8.

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.

Royer, Amélie, et al. “XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings.”10.1007/978-3-030-30671-7_3__Domain Adaptation for Visual Understanding, edited by Richa Singh et al., Springer Nature, 2020, pp. 33–49, doi:10.1007/978-3-030-30671-7_3.

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.

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

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.

Zimin, Alexander. Learning from Dependent Data. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:TH1048.

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

Ringbauer, Harald, et al. “Estimating Barriers to Gene Flow from Distorted Isolation-by-Distance Patterns.”10.1534/genetics.117.300638__Genetics, vol. 208, no. 3, Genetics Society of America, 2018, pp. 1231–45, doi:10.1534/genetics.117.300638.

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.

Alistarh, Dan-Adrian, et al. “The Convergence of Sparsified Gradient Methods.” Advances in Neural Information Processing Systems 31, vol. Volume 2018, Neural Information Processing Systems Foundation, 2018, pp. 5973–83.

Deny, Stephane, et al. Nonlinear Decoding of a Complex Movie from the Mammalian Retina. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:98.

Der, Ralf, and Georg S. Martius. Dynamical Self Consistency Leads to Behavioral Development and Emergent Social Interactions in Robots. 7846789, IEEE, 2017, doi:10.1109/DEVLRN.2016.7846789.

Der, Ralf, and Georg S. Martius. “Self Organized Behavior Generation for Musculoskeletal Robots.”10.3389/fnbot.2017.00008__Frontiers in Neurorobotics, vol. 11, no. MAR, 00008, Frontiers Research Foundation, 2017, doi:10.3389/fnbot.2017.00008.

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.

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

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.

Martius, Georg S., et al. Compliant Control for Soft Robots: Emergent Behavior of a Tendon Driven Anthropomorphic Arm. Vol. 2016–November, 7759138, IEEE, 2016, doi:10.1109/IROS.2016.7759138.

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.

Martius, Georg S., et al. “Self-Organized Control of an Tendon Driven Arm by Differential Extrinsic Plasticity.”10.7551/978-0-262-33936-0-ch029__Proceedings of the Artificial Life Conference 2016, vol. 28, MIT Press, 2016, pp. 142–43, doi:10.7551/978-0-262-33936-0-ch029.

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

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

Xia, Wei, et al. “Segmentation over Detection via Optimal Sparse Reconstructions.”10.1109/TCSVT.2014.2379972__IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 8, IEEE, 2015, pp. 1295–308, doi:10.1109/TCSVT.2014.2379972.

Der, Ralf, and Georg S. Martius. “Novel Plasticity Rule Can Explain the Development of Sensorimotor Intelligence.”10.1073/pnas.1508400112__PNAS, vol. 112, no. 45, National Academy of Sciences, 2015, pp. 24–32, doi:10.1073/pnas.1508400112.

Pentina, Anastasia, and Shai Ben David. Multi-Task and Lifelong Learning of Kernels. Vol. 9355, Springer, 2015, pp. 194–208, doi:10.1007/978-3-319-24486-0_13.

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.

Martius, Georg S., and Eckehard Olbrich. “Quantifying Self-Organizing Behavior of Autonomous Robots.”10.7551/978-0-262-33027-5-ch018__Proceedings of the 13th European Conference on Artificial Life, MIT Press, 2015, p. 78, doi:10.7551/978-0-262-33027-5-ch018.

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

Martius, Georg S., and Eckehard Olbrich. “Quantifying Emergent Behavior of Autonomous Robots.”10.3390/e17107266__Entropy, vol. 17, no. 10, MDPI, 2015, pp. 7266–97, doi:10.3390/e17107266.

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.

Morvant, Emilie, et al. “Majority Vote of Diverse Classifiers for Late Fusion.”10.1007/978-3-662-44415-3_16__Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8621, Springer, 2014, pp. 153–62, doi:10.1007/978-3-662-44415-3_16.

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.

Bellet, Aurélien, et al. “Learning a Priori Constrained Weighted Majority Votes.”10.1007/s10994-014-5462-z__Machine Learning, vol. 97, no. 1–2, Springer, 2014, pp. 129–54, doi:10.1007/s10994-014-5462-z.

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

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.

Quadrianto, Novi, et al. “The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models.” Proceedings of the 29th Conference Uncertainty in Artificial Intelligence, AUAI Press, 2013, pp. 527–36.

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.

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.

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.