Andrea Hornakova (PhD Student)
Personal Information
Publications
Swoboda, P., Horňáková, A., Rötzer, P., Savchynskyy, B., & Abbas, A. (2022). Structured Prediction Problem Archive. Retrieved from https://arxiv.org/abs/2202.03574
(arXiv: 2202.03574) Abstract
Structured prediction problems are one of the fundamental tools in machine<br>learning. In order to facilitate algorithm development for their numerical<br>solution, we collect in one place a large number of datasets in easy to read<br>formats for a diverse set of problem classes. We provide archival links to<br>datasets, description of the considered problems and problem formats, and a<br>short summary of problem characteristics including size, number of instances<br>etc. For reference we also give a non-exhaustive selection of algorithms<br>proposed in the literature for their solution. We hope that this central<br>repository will make benchmarking and comparison to established works easier.<br>We welcome submission of interesting new datasets and algorithms for inclusion<br>in our archive.<br>
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BibTeX
@online{Swoboda2202.03574,
TITLE = {Structured Prediction Problem Archive},
AUTHOR = {Swoboda, Paul and Hor{\v n}{\'a}kov{\'a}, Andrea and R{\"o}tzer, Paul and Savchynskyy, Bogdan and Abbas, Ahmed},
LANGUAGE = {eng},
URL = {https://arxiv.org/abs/2202.03574},
EPRINT = {2202.03574},
EPRINTTYPE = {arXiv},
YEAR = {2022},
MARGINALMARK = {$\bullet$},
ABSTRACT = {Structured prediction problems are one of the fundamental tools in machine<br>learning. In order to facilitate algorithm development for their numerical<br>solution, we collect in one place a large number of datasets in easy to read<br>formats for a diverse set of problem classes. We provide archival links to<br>datasets, description of the considered problems and problem formats, and a<br>short summary of problem characteristics including size, number of instances<br>etc. For reference we also give a non-exhaustive selection of algorithms<br>proposed in the literature for their solution. We hope that this central<br>repository will make benchmarking and comparison to established works easier.<br>We welcome submission of interesting new datasets and algorithms for inclusion<br>in our archive.<br>},
}
Endnote
%0 Report
%A Swoboda, Paul
%A Horňáková, Andrea
%A Rötzer, Paul
%A Savchynskyy, Bogdan
%A Abbas, Ahmed
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T Structured Prediction Problem Archive :
%G eng
%U http://hdl.handle.net/21.11116/0000-000C-2AAA-6
%U https://arxiv.org/abs/2202.03574
%D 2022
%X Structured prediction problems are one of the fundamental tools in machine<br>learning. In order to facilitate algorithm development for their numerical<br>solution, we collect in one place a large number of datasets in easy to read<br>formats for a diverse set of problem classes. We provide archival links to<br>datasets, description of the considered problems and problem formats, and a<br>short summary of problem characteristics including size, number of instances<br>etc. For reference we also give a non-exhaustive selection of algorithms<br>proposed in the literature for their solution. We hope that this central<br>repository will make benchmarking and comparison to established works easier.<br>We welcome submission of interesting new datasets and algorithms for inclusion<br>in our archive.<br>
%K Computer Science, Learning, cs.LG,Computer Science, Computer Vision and Pattern Recognition, cs.CV
Horňáková, A. (2022). Lifted Edges as Connectivity Priors for Multicut and Disjoint Paths. Universität des Saarlandes, Saarbrücken. Retrieved from urn:nbn:de:bsz:291--ds-369193
Export
BibTeX
@phdthesis{HornakovaPhD22,
TITLE = {Lifted Edges as Connectivity Priors for Multicut and Disjoint Paths},
AUTHOR = {Hor{\v n}{\'a}kov{\'a}, Andrea},
LANGUAGE = {eng},
URL = {urn:nbn:de:bsz:291--ds-369193},
DOI = {10.22028/D291-36919},
SCHOOL = {Universit{\"a}t des Saarlandes},
ADDRESS = {Saarbr{\"u}cken},
YEAR = {2022},
MARGINALMARK = {$\bullet$},
DATE = {2022},
}
Endnote
%0 Thesis
%A Horňáková, Andrea
%Y Swoboda, Paul
%A referee: Schiele, Bernt
%A referee: Werner, Tomáš
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
International Max Planck Research School, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
%T Lifted Edges as Connectivity Priors for Multicut and Disjoint Paths :
%G eng
%U http://hdl.handle.net/21.11116/0000-000B-2AD2-9
%U urn:nbn:de:bsz:291--ds-369193
%R 10.22028/D291-36919
%F OTHER: hdl:20.500.11880/33680
%I Universität des Saarlandes
%C Saarbrücken
%D 2022
%P X, 150 p.
%V phd
%9 phd
%U http://dx.doi.org/10.22028/D291-36919
Horňáková, A., Kaiser, T., Swoboda, P., Rolinek, M., Rosenhahn, B., & Henschel, R. (2021). Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths. In ICCV 2021, IEEE/CVF International Conference on Computer Vision. Virtual Event: IEEE. doi:10.1109/ICCV48922.2021.00627
Export
BibTeX
@inproceedings{HornakovaICCV2021,
TITLE = {Making Higher Order {MOT} Scalable: {A}n Efficient Approximate Solver for Lifted Disjoint Paths},
AUTHOR = {Hor{\v n}{\'a}kov{\'a}, Andrea and Kaiser, Timo and Swoboda, Paul and Rolinek, Michal and Rosenhahn, Bodo and Henschel, Roberto},
LANGUAGE = {eng},
ISBN = {978-1-6654-2812-5},
DOI = {10.1109/ICCV48922.2021.00627},
PUBLISHER = {IEEE},
YEAR = {2021},
MARGINALMARK = {$\bullet$},
BOOKTITLE = {ICCV 2021, IEEE/CVF International Conference on Computer Vision},
PAGES = {6310--6320},
ADDRESS = {Virtual Event},
}
Endnote
%0 Conference Proceedings
%A Horňáková, Andrea
%A Kaiser, Timo
%A Swoboda, Paul
%A Rolinek, Michal
%A Rosenhahn, Bodo
%A Henschel, Roberto
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
External Organizations
External Organizations
%T Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths :
%G eng
%U http://hdl.handle.net/21.11116/0000-0009-B3E1-E
%R 10.1109/ICCV48922.2021.00627
%D 2021
%B IEEE/CVF International Conference on Computer Vision
%Z date of event: 2021-10-11 - 2021-10-17
%C Virtual Event
%B ICCV 2021
%P 6310 - 6320
%I IEEE
%@ 978-1-6654-2812-5
Horňáková, A., Henschel, R., Rosenhahn, B., & Swoboda, P. (2020). Lifted Disjoint Paths with Application in Multiple Object Tracking. In Proceedings of the 37th International Conference on Machine Learning (ICML 2020). Virtual Conference: MLResearchPress.
Export
BibTeX
@inproceedings{Hornakova_ICML2020,
TITLE = {Lifted Disjoint Paths with Application in Multiple Object Tracking},
AUTHOR = {Hor{\v n}{\'a}kov{\'a}, Andrea and Henschel, Roberto and Rosenhahn, Bodo and Swoboda, Paul},
LANGUAGE = {eng},
ISSN = {2640-3498},
PUBLISHER = {MLResearchPress},
YEAR = {2020},
BOOKTITLE = {Proceedings of the 37th International Conference on Machine Learning (ICML 2020)},
EDITOR = {Daum{\'e}, Hal and Singh, Aarti},
PAGES = {1539--1548},
SERIES = {Proceedings of Machine Learning Research},
VOLUME = {119},
ADDRESS = {Virtual Conference},
}
Endnote
%0 Conference Proceedings
%A Horňáková, Andrea
%A Henschel, Roberto
%A Rosenhahn, Bodo
%A Swoboda, Paul
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T Lifted Disjoint Paths with Application in Multiple Object Tracking :
%G eng
%U http://hdl.handle.net/21.11116/0000-0007-7262-A
%D 2020
%B 37th International Conference on Machine Learning
%Z date of event: 2020-07-13 - 2020-07-18
%C Virtual Conference
%B Proceedings of the 37th International Conference on Machine Learning
%E Daumé, Hal; Singh, Aarti
%P 1539 - 1548
%I MLResearchPress
%B Proceedings of Machine Learning Research
%N 119
%@ false
%U http://proceedings.mlr.press/v119/hornakova20a/hornakova20a.pdf
Horňáková, A., List, M., Vreeken, J., & Schulz, M. H. (2018). JAMI: Fast Computation of Conditional Mutual Information for ceRNA Network Analysis. Bioinformatics, 34(17). doi:10.1093/bioinformatics/bty221
Export
BibTeX
@article{Hornakova_Bioinformatics2018,
TITLE = {{JAMI}: {F}ast Computation of Conditional Mutual Information for {ceRNA} Network Analysis},
AUTHOR = {Hor{\v n}{\'a}kov{\'a}, Andrea and List, Markus and Vreeken, Jilles and Schulz, Marcel H.},
LANGUAGE = {eng},
ISSN = {1367-4803},
DOI = {10.1093/bioinformatics/bty221},
PUBLISHER = {Oxford University Press},
ADDRESS = {Oxford},
YEAR = {2018},
DATE = {2018},
JOURNAL = {Bioinformatics},
VOLUME = {34},
NUMBER = {17},
PAGES = {3050--3051},
}
Endnote
%0 Journal Article
%A Horňáková, Andrea
%A List, Markus
%A Vreeken, Jilles
%A Schulz, Marcel H.
%+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society
Databases and Information Systems, MPI for Informatics, Max Planck Society
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society
%T JAMI: Fast Computation of Conditional Mutual Information for ceRNA Network Analysis :
%G eng
%U http://hdl.handle.net/21.11116/0000-0002-573A-C
%R 10.1093/bioinformatics/bty221
%7 2018
%D 2018
%J Bioinformatics
%V 34
%N 17
%& 3050
%P 3050 - 3051
%I Oxford University Press
%C Oxford
%@ false
Horňáková, A., Lange, J.-H., & Andres, B. (2017). Analysis and Optimization of Graph Decompositions by Lifted Multicuts. In Proceedings of the 34th International Conference on Machine Learning (ICML 2017). Sydney, Australia. Retrieved from http://proceedings.mlr.press/v70/hornakova17a.html
Export
BibTeX
@inproceedings{pmlr-v70-hornakova17a,
TITLE = {Analysis and Optimization of Graph Decompositions by Lifted Multicuts},
AUTHOR = {Hor{\v n}{\'a}kov{\'a}, Andrea and Lange, Jan-Hendrik and Andres, Bjoern},
LANGUAGE = {eng},
ISSN = {1938-7228},
URL = {http://proceedings.mlr.press/v70/hornakova17a.html},
YEAR = {2017},
BOOKTITLE = {Proceedings of the 34th International Conference on Machine Learning (ICML 2017)},
EDITOR = {Precup, Doina and Teh, Yee Whye},
PAGES = {1539--1548},
SERIES = {Proceedings of Machine Learning Research},
VOLUME = {70},
ADDRESS = {Sydney, Australia},
}
Endnote
%0 Conference Proceedings
%A Horňáková, Andrea
%A Lange, Jan-Hendrik
%A Andres, Bjoern
%+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society
%T Analysis and Optimization of Graph Decompositions by Lifted Multicuts :
%G eng
%U http://hdl.handle.net/11858/00-001M-0000-002D-D59B-C
%U http://proceedings.mlr.press/v70/hornakova17a.html
%D 2017
%B 34th International Conference on Machine Learning
%Z date of event: 2017-08-06 - 2017-08-11
%C Sydney, Australia
%B Proceedings of the 34th International Conference on Machine Learning
%E Precup, Doina; Teh, Yee Whye
%P 1539 - 1548
%B Proceedings of Machine Learning Research
%N 70
%@ false
%U http://proceedings.mlr.press/v70/hornakova17a/hornakova17a.pdf