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.
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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.
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DOI: 10.1007/978-3-662-44415-3_16
arXiv: 1404.7796

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.
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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.
LibreCat: https://research-explorer.ista.ac.at/record/2173

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.
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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.
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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.
LibreCat: https://research-explorer.ista.ac.at/record/2189

Pentina, Anastasia, and Christoph Lampert. A PAC-Bayesian Bound for Lifelong Learning. Vol. 32, ML Research Press, 2014, pp. 991–99.
LibreCat: https://research-explorer.ista.ac.at/record/2160

Kazmar, Tomas, et al. Drosophila Embryo Stage Annotation Using Label Propagation. IEEE, 2013, doi:10.1109/ICCV.2013.139.
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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.
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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.
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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.
LibreCat: https://research-explorer.ista.ac.at/record/2520

Chen, Chao, et al. Computing the M Most Probable Modes of a Graphical Model. Vol. 31, JMLR, 2013, pp. 161–69.
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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.
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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.
LibreCat: https://research-explorer.ista.ac.at/record/3321
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.
LibreCat: https://research-explorer.ista.ac.at/record/2825

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.
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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.
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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.
LibreCat: https://research-explorer.ista.ac.at/record/3126
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.
LibreCat: https://research-explorer.ista.ac.at/record/3248
DOI: 10.1007/s11554-010-0168-3