Publikationen -

Kluger, F., and Rosenhahn, B. (2024)PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample Consensus. In AAAI.

BibTeXEndNoteBibSonomy

@inproceedings{KluRos2024a,
author = {Kluger, Florian and Rosenhahn, Bodo},
booktitle = {AAAI},
keywords = {PARSAC},
month = {02},
title = {PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample Consensus},
year = 2024
}
%0 Conference Paper
%1 KluRos2024a
%A Kluger, Florian
%A Rosenhahn, Bodo
%B AAAI
%D 2024
%T PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample Consensus
Cong, Y., Xu, M., Simon, C., Chen, S., Ren, J., Xie, Y., Perez-Rua, J.-M., Rosenhahn, B., Xiang, T., and He, S. (2024)FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing. In International Conference on Learning Representations (ICLR).

BibTeXEndNoteBibSonomy

@inproceedings{ConXu2024a,
author = {Cong, Yuren and Xu, Mengmeng and Simon, Christian and Chen, Shoufa and Ren, Jiawei and Xie, Yanping and Perez-Rua, Juan-Manuel and Rosenhahn, Bodo and Xiang, Tao and He, Sen},
booktitle = {International Conference on Learning Representations (ICLR)},
keywords = {from:tntl3s},
title = {FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing},
year = 2024
}
%0 Conference Paper
%1 ConXu2024a
%A Cong, Yuren
%A Xu, Mengmeng
%A Simon, Christian
%A Chen, Shoufa
%A Ren, Jiawei
%A Xie, Yanping
%A Perez-Rua, Juan-Manuel
%A Rosenhahn, Bodo
%A Xiang, Tao
%A He, Sen
%B International Conference on Learning Representations (ICLR)
%D 2024
%T FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing
Poker, Y., von Hardenberg, S., Hofmann, W., Tang, M., Baumann, U., Schwerk, N., Wetzke, M., Lindenthal, V., Auber, B., Schlegelberger, B., Ott, H., von Bismarck, P., Viemann, D., Dressler, F., Klemann, C., and Bergmann, A. K. (2023)Systematic genetic analysis of pediatric patients with autoinflammatory diseases, Frontiers in Genetics, Frontiers Media {SA} 14.

URLBibTeXEndNoteBibSonomy

@article{Poker_2023,
author = {Poker, Yvonne and von Hardenberg, Sandra and Hofmann, Winfried and Tang, Ming and Baumann, Ulrich and Schwerk, Nicolaus and Wetzke, Martin and Lindenthal, Viola and Auber, Bernd and Schlegelberger, Brigitte and Ott, Hagen and von Bismarck, Philipp and Viemann, Dorothee and Dressler, Frank and Klemann, Christian and Bergmann, Anke Katharina},
journal = {Frontiers in Genetics},
keywords = {l3s},
month = {01},
publisher = {Frontiers Media {SA}},
title = {Systematic genetic analysis of pediatric patients with autoinflammatory diseases},
volume = 14,
year = 2023
}
%0 Journal Article
%1 Poker_2023
%A Poker, Yvonne
%A von Hardenberg, Sandra
%A Hofmann, Winfried
%A Tang, Ming
%A Baumann, Ulrich
%A Schwerk, Nicolaus
%A Wetzke, Martin
%A Lindenthal, Viola
%A Auber, Bernd
%A Schlegelberger, Brigitte
%A Ott, Hagen
%A von Bismarck, Philipp
%A Viemann, Dorothee
%A Dressler, Frank
%A Klemann, Christian
%A Bergmann, Anke Katharina
%D 2023
%I Frontiers Media {SA}
%J Frontiers in Genetics
%R 10.3389/fgene.2023.1065907
%T Systematic genetic analysis of pediatric patients with autoinflammatory diseases
%U https://doi.org/10.3389%2Ffgene.2023.1065907
%V 14
Safikhani, P., and Broneske, D. (2023)Enhancing AutoNLP with fine-tuned BERT models: An evaluation of text representation methods for AutoPyTorch., International Conference on Machine Learning Techniques and NLP 13.

BibTeXEndNoteBibSonomy

@article{SafBro2023,
author = {Safikhani, Parisa and Broneske, David},
journal = {International Conference on Machine Learning Techniques and NLP},
keywords = {BERT},
month = {09},
number = 16,
title = {Enhancing AutoNLP with fine-tuned BERT models: An evaluation of text representation methods for AutoPyTorch.},
volume = 13,
year = 2023
}
%0 Journal Article
%1 SafBro2023
%A Safikhani, Parisa
%A Broneske, David
%D 2023
%J International Conference on Machine Learning Techniques and NLP
%N 16
%T Enhancing AutoNLP with fine-tuned BERT models: An evaluation of text representation methods for AutoPyTorch.
%V 13
%@ 978-1-923107-04-5
Roy, S., Wallat, J., Sundaram, S. S., Nejdl, W., and Ganguly, N. (2023)GENEMASK: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning. In Frontiers in Artificial Intelligence and Applications, pp. 2002–2009.

AbstractBibTeXEndNoteBibSonomy

Large-scale language models such as DNABert and LOGO aim to learn optimal gene representations and are trained on the entire Human Reference Genome. However, standard tokenization schemes involve a simple sliding window of tokens like k-mers that do not leverage any gene-based semantics and thus may lead to (trivial) masking of easily predictable sequences, and subsequently inefficient Masked Language Modeling (MLM) training. Therefore, we propose a novel masking algorithm, GENEMASK, for MLM training of gene sequences, where we randomly identify positions in a gene sequence as mask centers and locally select the span around the mask center with the highest Normalized Pointwise Mutual Information (NPMI) to mask. We observe that in the absence of human-understandable semantics in the genomics domain (in contrast, semantic units like words and phrases are inherently available in NLP), GENEMASK-based models substantially outperform the SOTA models (DNABert and LOGO) over four benchmark gene sequence classification datasets in five few-shot settings (10 to 1000-shot). More significantly, the GENEMASK-based DNABert model is trained for less than one-tenth of the number of epochs of the original SOTA model. We also observe a strong correlation between top-ranked PMI tokens and conserved DNA sequence motifs, which may indicate the incorporation of latent genomic information. The codes (including trained models) and datasets are made publicly available at unmapped: uri https://github.com/roysoumya/GeneMask.
@inproceedings{noauthororeditor,
abstract = {Large-scale language models such as DNABert and LOGO aim to learn optimal gene representations and are trained on the entire Human Reference Genome. However, standard tokenization schemes involve a simple sliding window of tokens like k-mers that do not leverage any gene-based semantics and thus may lead to (trivial) masking of easily predictable sequences, and subsequently inefficient Masked Language Modeling (MLM) training. Therefore, we propose a novel masking algorithm, GENEMASK, for MLM training of gene sequences, where we randomly identify positions in a gene sequence as mask centers and locally select the span around the mask center with the highest Normalized Pointwise Mutual Information (NPMI) to mask. We observe that in the absence of human-understandable semantics in the genomics domain (in contrast, semantic units like words and phrases are inherently available in NLP), GENEMASK-based models substantially outperform the SOTA models (DNABert and LOGO) over four benchmark gene sequence classification datasets in five few-shot settings (10 to 1000-shot). More significantly, the GENEMASK-based DNABert model is trained for less than one-tenth of the number of epochs of the original SOTA model. We also observe a strong correlation between top-ranked PMI tokens and conserved DNA sequence motifs, which may indicate the incorporation of latent genomic information. The codes (including trained models) and datasets are made publicly available at unmapped: uri https://github.com/roysoumya/GeneMask.},
author = {Roy, Soumyadeep and Wallat, Jonas and Sundaram, Sowmya S and Nejdl, Wolfgang and Ganguly, Niloy},
keywords = {l3s},
pages = {2002-2009},
series = {Frontiers in Artificial Intelligence and Applications},
title = {GENEMASK: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning},
volume = 372,
year = 2023
}
%0 Conference Paper
%1 noauthororeditor
%A Roy, Soumyadeep
%A Wallat, Jonas
%A Sundaram, Sowmya S
%A Nejdl, Wolfgang
%A Ganguly, Niloy
%B Frontiers in Artificial Intelligence and Applications
%D 2023
%P 2002-2009
%R 10.3233/FAIA230492
%T GENEMASK: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning
%V 372
%X Large-scale language models such as DNABert and LOGO aim to learn optimal gene representations and are trained on the entire Human Reference Genome. However, standard tokenization schemes involve a simple sliding window of tokens like k-mers that do not leverage any gene-based semantics and thus may lead to (trivial) masking of easily predictable sequences, and subsequently inefficient Masked Language Modeling (MLM) training. Therefore, we propose a novel masking algorithm, GENEMASK, for MLM training of gene sequences, where we randomly identify positions in a gene sequence as mask centers and locally select the span around the mask center with the highest Normalized Pointwise Mutual Information (NPMI) to mask. We observe that in the absence of human-understandable semantics in the genomics domain (in contrast, semantic units like words and phrases are inherently available in NLP), GENEMASK-based models substantially outperform the SOTA models (DNABert and LOGO) over four benchmark gene sequence classification datasets in five few-shot settings (10 to 1000-shot). More significantly, the GENEMASK-based DNABert model is trained for less than one-tenth of the number of epochs of the original SOTA model. We also observe a strong correlation between top-ranked PMI tokens and conserved DNA sequence motifs, which may indicate the incorporation of latent genomic information. The codes (including trained models) and datasets are made publicly available at unmapped: uri https://github.com/roysoumya/GeneMask.
%@ 978-1-64368-437-6
Nandy, A., Kapadnis, M. N., Goyal, P., and Ganguly, N. (2023)CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text. In The 2023 Conference on Empirical Methods in Natural Language Processing.

URLBibTeXEndNoteBibSonomy

@inproceedings{nandy2023textbfemphclmsm,
author = {Nandy, Abhilash and Kapadnis, Manav Nitin and Goyal, Pawan and Ganguly, Niloy},
booktitle = {The 2023 Conference on Empirical Methods in Natural Language Processing},
keywords = {pretraining},
title = {CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text},
year = 2023
}
%0 Conference Paper
%1 nandy2023textbfemphclmsm
%A Nandy, Abhilash
%A Kapadnis, Manav Nitin
%A Goyal, Pawan
%A Ganguly, Niloy
%B The 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%T CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text
%U https://openreview.net/forum?id=SP8zIwanHD
Benjamins, C., Eimer, T., Schubert, F., Mohan, A., D{ö}hler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F., and Lindauer, M. (2023)Contextualize Me - The Case for Context in Reinforcement Learning, Transactions on Machine Learning Research.

URLBibTeXEndNoteBibSonomy

@article{BenEim2023,
author = {Benjamins, Carolin and Eimer, Theresa and Schubert, Frederik and Mohan, Aditya and D{ö}hler, Sebastian and Biedenkapp, André and Rosenhahn, Bodo and Hutter, Frank and Lindauer, Marius},
journal = {Transactions on Machine Learning Research},
keywords = {reinforcement},
month = {06},
title = {Contextualize Me - The Case for Context in Reinforcement Learning},
year = 2023
}
%0 Journal Article
%1 BenEim2023
%A Benjamins, Carolin
%A Eimer, Theresa
%A Schubert, Frederik
%A Mohan, Aditya
%A D{ö}hler, Sebastian
%A Biedenkapp, André
%A Rosenhahn, Bodo
%A Hutter, Frank
%A Lindauer, Marius
%D 2023
%J Transactions on Machine Learning Research
%T Contextualize Me - The Case for Context in Reinforcement Learning
%U https://arxiv.org/abs/2202.04500
Auer, S., Barone, D. A. C., Bartz, C., Cortes, E. G., Jaradeh, M. Y., Karras, O., Koubarakis, M., Mouromtsev, D., Pliukhin, D., Radyush, D., Shilin, I., Stocker, M., and Tsalapati, E. (2023, March)SciQA benchmark: Dataset and {RDF} dump (Version 5), Zenodo.

URLBibTeXEndNoteBibSonomy

@misc{DBLP:data/10/AuerBBCJKKMPRSST23a,
author = {Auer, S{{ö}}ren and Barone, Dante A. C. and Bartz, Cassiano and Cortes, Eduardo G. and Jaradeh, Mohamad Yaser and Karras, Oliver and Koubarakis, Manolis and Mouromtsev, Dmitry and Pliukhin, Dmitrii and Radyush, Daniil and Shilin, Ivan and Stocker, Markus and Tsalapati, Eleni},
howpublished = {\url{https://doi.org/10.5281/zenodo.7727922}},
keywords = {leibnizailab},
month = {03},
note = {Accessed on YYYY-MM-DD.},
publisher = {Zenodo},
title = {SciQA benchmark: Dataset and {RDF} dump (Version 5)},
year = 2023
}
%0 Generic
%1 DBLP:data/10/AuerBBCJKKMPRSST23a
%A Auer, S{{ö}}ren
%A Barone, Dante A. C.
%A Bartz, Cassiano
%A Cortes, Eduardo G.
%A Jaradeh, Mohamad Yaser
%A Karras, Oliver
%A Koubarakis, Manolis
%A Mouromtsev, Dmitry
%A Pliukhin, Dmitrii
%A Radyush, Daniil
%A Shilin, Ivan
%A Stocker, Markus
%A Tsalapati, Eleni
%D 2023
%I Zenodo
%R 10.5281/ZENODO.7727922
%T SciQA benchmark: Dataset and {RDF} dump (Version 5)
%U https://doi.org/10.5281/zenodo.7727922
Kuhnke, F., and Ostermann, J. (2023)Domain Adaptation for Head Pose Estimation Using Relative Pose Consistency, IEEE Transactions on Biometrics, Behavior, and Identity Science.

BibTeXEndNoteBibSonomy

@article{KuhOst2023,
author = {Kuhnke, Felix and Ostermann, J{ö}rn},
journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science},
keywords = {Adaptation},
title = {Domain Adaptation for Head Pose Estimation Using Relative Pose Consistency},
year = 2023
}
%0 Journal Article
%1 KuhOst2023
%A Kuhnke, Felix
%A Ostermann, J{ö}rn
%D 2023
%J IEEE Transactions on Biometrics, Behavior, and Identity Science
%R 10.1109/TBIOM.2023.3237039
%T Domain Adaptation for Head Pose Estimation Using Relative Pose Consistency
Dockhorn, A., Kirst, M., Mostaghim, S., Wieczorek, M., and Zille, H. (2022)Evolutionary Algorithm for Parameter Optimization of Context Steering Agents, IEEE Transactions on Games 1–12.

URLBibTeXEndNoteBibSonomy

@article{DocKir2022,
author = {Dockhorn, Alexander and Kirst, Martin and Mostaghim, Sanaz and Wieczorek, Martin and Zille, Heiner},
journal = {IEEE Transactions on Games},
keywords = {Algorithm},
pages = {1-12},
title = {Evolutionary Algorithm for Parameter Optimization of Context Steering Agents},
year = 2022
}
%0 Journal Article
%1 DocKir2022
%A Dockhorn, Alexander
%A Kirst, Martin
%A Mostaghim, Sanaz
%A Wieczorek, Martin
%A Zille, Heiner
%D 2022
%J IEEE Transactions on Games
%P 1-12
%R 10.1109/TG.2022.3157247
%T Evolutionary Algorithm for Parameter Optimization of Context Steering Agents
%U https://ieeexplore.ieee.org/document/9729529
Rumberg, L., Gebauer, C., Ehlert, H., L{ü}dtke, U., and Ostermann, J. (2022)Improving Phonetic Transcriptions of Children’s Speech by Pronunciation Modelling with Constrained CTC-Decoding. In Proceedings INTERSPEECH 2022 – 23rd Annual Conference of the International Speech Communication Association.

BibTeXEndNoteBibSonomy

@inproceedings{RumGeb2022b,
author = {Rumberg, Lars and Gebauer, Christopher and Ehlert, Hanna and L{ü}dtke, Ulrike and Ostermann, J{ö}rn},
booktitle = {Proceedings INTERSPEECH 2022 – 23rd Annual Conference of the International Speech Communication Association},
keywords = {Improving},
month = {09},
title = {Improving Phonetic Transcriptions of Children’s Speech by Pronunciation Modelling with Constrained CTC-Decoding},
year = 2022
}
%0 Conference Paper
%1 RumGeb2022b
%A Rumberg, Lars
%A Gebauer, Christopher
%A Ehlert, Hanna
%A L{ü}dtke, Ulrike
%A Ostermann, J{ö}rn
%B Proceedings INTERSPEECH 2022 – 23rd Annual Conference of the International Speech Communication Association
%D 2022
%T Improving Phonetic Transcriptions of Children’s Speech by Pronunciation Modelling with Constrained CTC-Decoding
Reinders, C., Schubert, F., and Rosenhahn, B. (2022)ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing. In Arxiv Preprint.

BibTeXEndNoteBibSonomy

@inproceedings{ReiSch2022,
author = {Reinders, Christoph and Schubert, Frederik and Rosenhahn, Bodo},
booktitle = {Arxiv Preprint},
keywords = {Feature},
month = {03},
title = {ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing},
year = 2022
}
%0 Conference Paper
%1 ReiSch2022
%A Reinders, Christoph
%A Schubert, Frederik
%A Rosenhahn, Bodo
%B Arxiv Preprint
%D 2022
%T ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing
Grimm, E., Kuhnke, F., Gajdt, A., Ostermann, J., and Knoche, M. (2022)Accurate Quantification of Anthocyanin in Red Flesh Apples Using Digital Photography and Image Analysis, Horticulturae 8.

URLBibTeXEndNoteBibSonomy

@article{GriKuh2022,
author = {Grimm, Eckhard and Kuhnke, Felix and Gajdt, Anna and Ostermann, J{ö}rn and Knoche, Moritz},
journal = {Horticulturae},
keywords = {Flesh},
month = {01},
number = 2,
title = {Accurate Quantification of Anthocyanin in Red Flesh Apples Using Digital Photography and Image Analysis},
volume = 8,
year = 2022
}
%0 Journal Article
%1 GriKuh2022
%A Grimm, Eckhard
%A Kuhnke, Felix
%A Gajdt, Anna
%A Ostermann, J{ö}rn
%A Knoche, Moritz
%D 2022
%J Horticulturae
%N 2
%R https://doi.org/10.3390/horticulturae8020145
%T Accurate Quantification of Anthocyanin in Red Flesh Apples Using Digital Photography and Image Analysis
%U https://www.mdpi.com/2311-7524/8/2/145
%V 8
Schier, M., Reinders, C., and Rosenhahn, B. (2022)Constrained Mean Shift Clustering. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM).

URLBibTeXEndNoteBibSonomy

@inproceedings{SchRei2022a,
author = {Schier, Maximilian and Reinders, Christoph and Rosenhahn, Bodo},
booktitle = {Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)},
keywords = {Clustering},
month = {04},
title = {Constrained Mean Shift Clustering},
year = 2022
}
%0 Conference Paper
%1 SchRei2022a
%A Schier, Maximilian
%A Reinders, Christoph
%A Rosenhahn, Bodo
%B Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)
%D 2022
%T Constrained Mean Shift Clustering
%U https://github.com/m-schier/cms
Hinrichs, R., Liang, K., Lu, Z., and Ostermann, J. (2022)Improved Compression of Artificial Neural Networks through Curvature-Aware Training. In Proceedings of the IEEE World Congress on Computational Intelligence.

BibTeXEndNoteBibSonomy

@inproceedings{HinLia2022,
author = {Hinrichs, Reemt and Liang, Kai and Lu, Ze and Ostermann, J{ö}rn},
booktitle = {Proceedings of the IEEE World Congress on Computational Intelligence},
keywords = {Curvature-Aware},
month = {07},
title = {Improved Compression of Artificial Neural Networks through Curvature-Aware Training},
year = 2022
}
%0 Conference Paper
%1 HinLia2022
%A Hinrichs, Reemt
%A Liang, Kai
%A Lu, Ze
%A Ostermann, J{ö}rn
%B Proceedings of the IEEE World Congress on Computational Intelligence
%D 2022
%T Improved Compression of Artificial Neural Networks through Curvature-Aware Training
Stahl, M., Spliethöver, M., and Wachsmuth, H. (2022)To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation. In Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science.

BibTeXEndNoteBibSonomy

@inproceedings{Stahl_Spliethöver_Wachsmuth,
author = {Stahl, Maja and Spliethöver, Maximilian and Wachsmuth, Henning},
booktitle = {Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science},
keywords = {leibnizailab},
title = {To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation},
year = 2022
}
%0 Conference Paper
%1 Stahl_Spliethöver_Wachsmuth
%A Stahl, Maja
%A Spliethöver, Maximilian
%A Wachsmuth, Henning
%B Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science
%D 2022
%T To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation
Alshomary, M., and Stahl, M. (2022)Argument Novelty and Validity Assessment via Multitask and Transfer Learning. In Proceedings of the 9th Workshop on Argument Mining, pp. 111–114, International Conference on Computational Linguistics.

BibTeXEndNoteBibSonomy

@inproceedings{Alshomary_Stahl_2022,
author = {Alshomary, Milad and Stahl, Maja},
booktitle = {Proceedings of the 9th Workshop on Argument Mining},
keywords = {leibnizailab},
pages = {111–114},
publisher = {International Conference on Computational Linguistics},
title = {Argument Novelty and Validity Assessment via Multitask and Transfer Learning},
year = 2022
}
%0 Conference Paper
%1 Alshomary_Stahl_2022
%A Alshomary, Milad
%A Stahl, Maja
%B Proceedings of the 9th Workshop on Argument Mining
%D 2022
%I International Conference on Computational Linguistics
%P 111–114
%T Argument Novelty and Validity Assessment via Multitask and Transfer Learning
Sass, R., Bergman, E., Biedenkapp, A., Hutter, F., and Lindauer, M. (2022)DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning. In ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML), arXiv.

URLBibTeXEndNoteBibSonomy

@inproceedings{https://doi.org/10.48550/arxiv.2206.03493,
author = {Sass, René and Bergman, Eddie and Biedenkapp, André and Hutter, Frank and Lindauer, Marius},
booktitle = {ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML)},
keywords = {leibnizailab},
publisher = {arXiv},
title = {DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning},
year = 2022
}
%0 Conference Paper
%1 https://doi.org/10.48550/arxiv.2206.03493
%A Sass, René
%A Bergman, Eddie
%A Biedenkapp, André
%A Hutter, Frank
%A Lindauer, Marius
%B ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML)
%D 2022
%I arXiv
%R 10.48550/ARXIV.2206.03493
%T DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning
%U https://arxiv.org/abs/2206.03493
Benjamins, C., Jankovic, A., Raponi, E., van der Blom, K., Lindauer, M., and Doerr, C. (2022)Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis. In Workshop on Meta-Learning (MetaLearn 2022).

URLBibTeXEndNoteBibSonomy

@inproceedings{benjamins2022towards,
author = {Benjamins, Carolin and Jankovic, Anja and Raponi, Elena and van der Blom, Koen and Lindauer, Marius and Doerr, Carola},
booktitle = {Workshop on Meta-Learning (MetaLearn 2022)},
keywords = {leibnizailab},
title = {Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis},
year = 2022
}
%0 Conference Paper
%1 benjamins2022towards
%A Benjamins, Carolin
%A Jankovic, Anja
%A Raponi, Elena
%A van der Blom, Koen
%A Lindauer, Marius
%A Doerr, Carola
%B Workshop on Meta-Learning (MetaLearn 2022)
%D 2022
%T Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis
%U https://www.ai.uni-hannover.de/de/forschung/publikationen/publikationen-detailansicht?tx_t3luhpublications_publications%5Baction%5D=show&tx_t3luhpublications_publications%5Bcontroller%5D=Publication&tx_t3luhpublications_publications%5Bpublication%5D=7783&cHash=04b8ffd50e56727ae4181c8e2a2261f1
Chen, W.-F., Chen, M.-H., Mudgal, G., and Wachsmuth, H. (2022)Analyzing Culture-Specific Argument Structures in Learner Essays. In Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022), pp. 51–61.

BibTeXEndNoteBibSonomy

@inproceedings{Chen_Chen_Mudgal_Wachsmuth_2022,
author = {Chen, Wei-Fan and Chen, Mei-Hua and Mudgal, Garima and Wachsmuth, Henning},
booktitle = {Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022)},
keywords = {leibnizailab},
pages = {51–61},
title = {Analyzing Culture-Specific Argument Structures in Learner Essays},
year = 2022
}
%0 Conference Paper
%1 Chen_Chen_Mudgal_Wachsmuth_2022
%A Chen, Wei-Fan
%A Chen, Mei-Hua
%A Mudgal, Garima
%A Wachsmuth, Henning
%B Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022)
%D 2022
%P 51–61
%T Analyzing Culture-Specific Argument Structures in Learner Essays
Biedenkapp, A., Speck, D., Sievers, S., Hutter, F., Lindauer, M., and Seipp, J. (2022)Learning Domain-Independent Policies for Open List Selection. In Proceedings of the 3rd ICAPS workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL), pp. 1–9.

BibTeXEndNoteBibSonomy

@inproceedings{BieSpe2022,
author = {Biedenkapp, André and Speck, David and Sievers, Silvan and Hutter, Frank and Lindauer, Marius and Seipp, Jendrik},
booktitle = {Proceedings of the 3rd ICAPS workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL)},
keywords = {leibnizailab},
pages = {1-9},
title = {Learning Domain-Independent Policies for Open List Selection},
year = 2022
}
%0 Conference Paper
%1 BieSpe2022
%A Biedenkapp, André
%A Speck, David
%A Sievers, Silvan
%A Hutter, Frank
%A Lindauer, Marius
%A Seipp, Jendrik
%B Proceedings of the 3rd ICAPS workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL)
%D 2022
%P 1-9
%T Learning Domain-Independent Policies for Open List Selection
Parker-Holder, J., Rajan, R., Song, X., Biedenkapp, A., Miao, Y., Eimer, T., Zhang, B., Nguyen, V., Calandra, R., Faust, A., Hutter, F., and Lindauer, M. (2022)Automated Reinforcement Learning (AutoRL): A Survey and Open Problems, Journal of Artificial Intelligence Research 74 (2022) 517–568.

BibTeXEndNoteBibSonomy

@article{2201.03916,
author = {Parker-Holder, Jack and Rajan, Raghu and Song, Xingyou and Biedenkapp, André and Miao, Yingjie and Eimer, Theresa and Zhang, Baohe and Nguyen, Vu and Calandra, Roberto and Faust, Aleksandra and Hutter, Frank and Lindauer, Marius},
journal = {Journal of Artificial Intelligence Research 74 (2022)},
keywords = {AutoML},
pages = {517-568},
title = {Automated Reinforcement Learning (AutoRL): A Survey and Open Problems},
year = 2022
}
%0 Journal Article
%1 2201.03916
%A Parker-Holder, Jack
%A Rajan, Raghu
%A Song, Xingyou
%A Biedenkapp, André
%A Miao, Yingjie
%A Eimer, Theresa
%A Zhang, Baohe
%A Nguyen, Vu
%A Calandra, Roberto
%A Faust, Aleksandra
%A Hutter, Frank
%A Lindauer, Marius
%D 2022
%J Journal of Artificial Intelligence Research 74 (2022)
%P 517-568
%R 10.1613/jair.1.13596
%T Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
Pestel-Schiller, U., and Ostermann, J. (2022)Impact of Spatial Resolution and Zoom on Interpreter-Based Evaluation of Compressed SAR Images. In 14th European Conference on Synthetic Aperture Radar.

BibTeXEndNoteBibSonomy

@inproceedings{PesOst2022,
author = {Pestel-Schiller, Ulrike and Ostermann, J{ö}rn},
booktitle = {14th European Conference on Synthetic Aperture Radar},
keywords = {of},
month = {07},
title = {Impact of Spatial Resolution and Zoom on Interpreter-Based Evaluation of Compressed SAR Images},
year = 2022
}
%0 Conference Paper
%1 PesOst2022
%A Pestel-Schiller, Ulrike
%A Ostermann, J{ö}rn
%B 14th European Conference on Synthetic Aperture Radar
%D 2022
%T Impact of Spatial Resolution and Zoom on Interpreter-Based Evaluation of Compressed SAR Images
Alshomary, M., El Baff, R., Gurcke, T., and Wachsmuth, H. (2022)The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, pp. 8782–8797.

BibTeXEndNoteBibSonomy

@inproceedings{alshomary2022moral,
author = {Alshomary, Milad and El Baff, Roxanne and Gurcke, Timon and Wachsmuth, Henning},
booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
keywords = {leibnizailab},
pages = {8782–8797},
title = {The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments},
year = 2022
}
%0 Conference Paper
%1 alshomary2022moral
%A Alshomary, Milad
%A El Baff, Roxanne
%A Gurcke, Timon
%A Wachsmuth, Henning
%B Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics
%D 2022
%P 8782–8797
%T The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments
Mast, M., Marschollek, M., Jack, T., Wulff, A., and Elise Study, G. (2022)Developing a Data Driven Approach for Early Detection of SIRS in Pediatric Intensive Care Using Automatically Labeled Training Data, Stud Health Technol Inform 289, 228–231.

URLBibTeXEndNoteBibSonomy

@article{RN11,
author = {Mast, M. and Marschollek, M. and Jack, T. and Wulff, A. and Elise Study, Group},
journal = {Stud Health Technol Inform},
keywords = {l3s},
pages = {228-231},
title = {Developing a Data Driven Approach for Early Detection of SIRS in Pediatric Intensive Care Using Automatically Labeled Training Data},
type = {Journal Article},
volume = 289,
year = 2022
}
%0 Journal Article
%1 RN11
%A Mast, M.
%A Marschollek, M.
%A Jack, T.
%A Wulff, A.
%A Elise Study, Group
%D 2022
%J Stud Health Technol Inform
%P 228-231
%R 10.3233/SHTI210901
%T Developing a Data Driven Approach for Early Detection of SIRS in Pediatric Intensive Care Using Automatically Labeled Training Data
%U https://www.ncbi.nlm.nih.gov/pubmed/35062134
%V 289
Benjamins, C., Eimer, T., Schubert, F., Mohan, A., Biedenkapp, A., Rosenhahn, B., Hutter, F., and Lindauer, M. (2022)Contextualize Me -- The Case for Context in Reinforcement Learning, Arxiv Preprint, arXiv.

URLBibTeXEndNoteBibSonomy

@article{https://doi.org/10.48550/arxiv.2202.04500,
author = {Benjamins, Carolin and Eimer, Theresa and Schubert, Frederik and Mohan, Aditya and Biedenkapp, André and Rosenhahn, Bodo and Hutter, Frank and Lindauer, Marius},
journal = {Arxiv Preprint},
keywords = {leibnizailab},
publisher = {arXiv},
title = {Contextualize Me -- The Case for Context in Reinforcement Learning},
year = 2022
}
%0 Journal Article
%1 https://doi.org/10.48550/arxiv.2202.04500
%A Benjamins, Carolin
%A Eimer, Theresa
%A Schubert, Frederik
%A Mohan, Aditya
%A Biedenkapp, André
%A Rosenhahn, Bodo
%A Hutter, Frank
%A Lindauer, Marius
%D 2022
%I arXiv
%J Arxiv Preprint
%R 10.48550/ARXIV.2202.04500
%T Contextualize Me -- The Case for Context in Reinforcement Learning
%U https://arxiv.org/abs/2202.04500
Xu, L., Hurtado-Grueso, J., Jeurissen, D., Liebana, D. P., and Dockhorn, A. (2022)Elastic Monte Carlo Tree Search State Abstraction for Strategy Game Playing. In 2022 IEEE Conference on Games (CoG).

URLBibTeXEndNoteBibSonomy

@inproceedings{XuHur2022,
author = {Xu, Linjie and Hurtado-Grueso, Jorge and Jeurissen, Dominic and Liebana, Diego Perez and Dockhorn, Alexander},
booktitle = {2022 IEEE Conference on Games (CoG)},
keywords = {Monte},
title = {Elastic Monte Carlo Tree Search State Abstraction for Strategy Game Playing},
year = 2022
}
%0 Conference Paper
%1 XuHur2022
%A Xu, Linjie
%A Hurtado-Grueso, Jorge
%A Jeurissen, Dominic
%A Liebana, Diego Perez
%A Dockhorn, Alexander
%B 2022 IEEE Conference on Games (CoG)
%D 2022
%T Elastic Monte Carlo Tree Search State Abstraction for Strategy Game Playing
%U https://arxiv.org/abs/2205.15126
Dockhorn, A., and Kruse, R. (2022)Balancing Exploration and Exploitation in Forward Model Learning, Advances in Intelligent Systems Research and Innovation 1–19.

URLBibTeXEndNoteBibSonomy

@article{DocKru2022,
author = {Dockhorn, Alexander and Kruse, Rudolf},
journal = {Advances in Intelligent Systems Research and Innovation},
keywords = {Exploration},
pages = {1--19},
title = {Balancing Exploration and Exploitation in Forward Model Learning},
year = 2022
}
%0 Journal Article
%1 DocKru2022
%A Dockhorn, Alexander
%A Kruse, Rudolf
%D 2022
%J Advances in Intelligent Systems Research and Innovation
%P 1--19
%R 10.1007/978-3-030-78124-8_1
%T Balancing Exploration and Exploitation in Forward Model Learning
%U https://doi.org/10.1007/978-3-030-78124-8_1
%@ 978-3-030-78124-8
Nayak, T., Sharma, S., Butala, Y., Dasgupta, K., Goyal, P., and Ganguly, N. (2022)A Generative Approach for Financial Causality Extraction. In Companion Proceedings of the Web Conference 2022, {ACM}.

URLBibTeXEndNoteBibSonomy

@inproceedings{Nayak_2022,
author = {Nayak, Tapas and Sharma, Soumya and Butala, Yash and Dasgupta, Koustuv and Goyal, Pawan and Ganguly, Niloy},
booktitle = {Companion Proceedings of the Web Conference 2022},
keywords = {"sys:relevantfor:l3s"},
month = {04},
publisher = {{ACM}},
title = {A Generative Approach for Financial Causality Extraction},
year = 2022
}
%0 Conference Paper
%1 Nayak_2022
%A Nayak, Tapas
%A Sharma, Soumya
%A Butala, Yash
%A Dasgupta, Koustuv
%A Goyal, Pawan
%A Ganguly, Niloy
%B Companion Proceedings of the Web Conference 2022
%D 2022
%I {ACM}
%R 10.1145/3487553.3524633
%T A Generative Approach for Financial Causality Extraction
%U https://doi.org/10.1145%2F3487553.3524633
Knura, M., Kluger, F., Zahtila, M., Schiewe, J., Rosenhahn, B., and Burghardt, D. (2021)Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden, ISPRS International Journal of Geo-Information.

URLBibTeXEndNoteBibSonomy

@article{KnuKlu2021,
author = {Knura, Martin and Kluger, Florian and Zahtila, Moris and Schiewe, Jochen and Rosenhahn, Bodo and Burghardt, Dirk},
journal = {ISPRS International Journal of Geo-Information},
keywords = {Bicycle},
month = 10,
title = {Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden},
year = 2021
}
%0 Journal Article
%1 KnuKlu2021
%A Knura, Martin
%A Kluger, Florian
%A Zahtila, Moris
%A Schiewe, Jochen
%A Rosenhahn, Bodo
%A Burghardt, Dirk
%D 2021
%J ISPRS International Journal of Geo-Information
%R 10.3390/ijgi10110733
%T Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden
%U https://doi.org/10.3390%2Fijgi10110733
Eggensperger, K., M{ü}ller, P., Mallik, N., Feurer, M., Sass, R., Klein, A., Awad, N., Lindauer, M., and Hutter, F. (2021)HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. In Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track).

URLBibTeXEndNoteBibSonomy

@inproceedings{EggMue2021,
author = {Eggensperger, Katharina and M{ü}ller, Philipp and Mallik, Neeratyoy and Feurer, Matthias and Sass, René and Klein, Aaron and Awad, Noor and Lindauer, Marius and Hutter, Frank},
booktitle = {Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track)},
keywords = {HPOBench},
month = 12,
title = {HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO},
year = 2021
}
%0 Conference Paper
%1 EggMue2021
%A Eggensperger, Katharina
%A M{ü}ller, Philipp
%A Mallik, Neeratyoy
%A Feurer, Matthias
%A Sass, René
%A Klein, Aaron
%A Awad, Noor
%A Lindauer, Marius
%A Hutter, Frank
%B Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track)
%D 2021
%T HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
%U https://arxiv.org/abs/2109.06716
Benjamins, C., Eimer, T., Schubert, F., Biedenkapp, A., Rosenhahn, B., Hutter, F., and Lindauer, M. (2021)CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. In NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning.

BibTeXEndNoteBibSonomy

@inproceedings{BenEim2021a,
author = {Benjamins, Carolin and Eimer, Theresa and Schubert, Frederik and Biedenkapp, André and Rosenhahn, Bodo and Hutter, Frank and Lindauer, Marius},
booktitle = {NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning},
keywords = {Reinforcement},
month = 12,
title = {CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning},
year = 2021
}
%0 Conference Paper
%1 BenEim2021a
%A Benjamins, Carolin
%A Eimer, Theresa
%A Schubert, Frederik
%A Biedenkapp, André
%A Rosenhahn, Bodo
%A Hutter, Frank
%A Lindauer, Marius
%B NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning
%D 2021
%T CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning
Rumberg, L., Ehlert, H., L{ü}dtke, U., and Ostermann, J. (2021)Age-Invariant Training for End-to-End Child Speech Recognition using Adversarial Multi-Task Learning. In Proceedings INTERSPEECH 2021 -- 22th Annual Conference of the International Speech Communication Association.

BibTeXEndNoteBibSonomy

@inproceedings{RumEhl2021,
author = {Rumberg, Lars and Ehlert, Hanna and L{ü}dtke, Ulrike and Ostermann, J{ö}rn},
booktitle = {Proceedings INTERSPEECH 2021 -- 22th Annual Conference of the International Speech Communication Association},
keywords = {Recognition},
month = {08},
title = {Age-Invariant Training for End-to-End Child Speech Recognition using Adversarial Multi-Task Learning},
year = 2021
}
%0 Conference Paper
%1 RumEhl2021
%A Rumberg, Lars
%A Ehlert, Hanna
%A L{ü}dtke, Ulrike
%A Ostermann, J{ö}rn
%B Proceedings INTERSPEECH 2021 -- 22th Annual Conference of the International Speech Communication Association
%D 2021
%T Age-Invariant Training for End-to-End Child Speech Recognition using Adversarial Multi-Task Learning
Dockhorn, A., and Kruse, R. (2021)Modelheuristics for efficient forward model learning, At-Automatisierungstechnik.

URLBibTeXEndNoteBibSonomy

@article{DocKru2021a,
author = {Dockhorn, Alexander and Kruse, Rudolf},
journal = {At-Automatisierungstechnik},
keywords = {for},
month = 10,
title = {Modelheuristics for efficient forward model learning},
year = 2021
}
%0 Journal Article
%1 DocKru2021a
%A Dockhorn, Alexander
%A Kruse, Rudolf
%D 2021
%J At-Automatisierungstechnik
%R 10.1515/auto-2021-0037
%T Modelheuristics for efficient forward model learning
%U https://www.degruyter.com/document/doi/10.1515/auto-2021-0037/html
Dockhorn, A., Hurtado-Grueso, J., Jeurissen, D., Xu, L., and Perez-Liebana, D. (2021)Portfolio Search and Optimization for General Strategy Game-Playing. In 2021 IEEE Congress on Evolutionary Computation (CEC), pp. 2085–2092.

URLBibTeXEndNoteBibSonomy

@inproceedings{DocHur2021,
author = {Dockhorn, Alexander and Hurtado-Grueso, Jorge and Jeurissen, Dominik and Xu, Linjie and Perez-Liebana, Diego},
booktitle = {2021 IEEE Congress on Evolutionary Computation (CEC)},
keywords = {Search},
pages = {2085-2092},
title = {Portfolio Search and Optimization for General Strategy Game-Playing},
year = 2021
}
%0 Conference Paper
%1 DocHur2021
%A Dockhorn, Alexander
%A Hurtado-Grueso, Jorge
%A Jeurissen, Dominik
%A Xu, Linjie
%A Perez-Liebana, Diego
%B 2021 IEEE Congress on Evolutionary Computation (CEC)
%D 2021
%P 2085-2092
%R 10.1109/CEC45853.2021.9504824
%T Portfolio Search and Optimization for General Strategy Game-Playing
%U https://ieeexplore.ieee.org/document/9504824
Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Sass, R., and Hutter, F. (2021)SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. In ArXiv: 2109.09831.

URLBibTeXEndNoteBibSonomy

@inproceedings{LinEgg2021,
author = {Lindauer, Marius and Eggensperger, Katharina and Feurer, Matthias and Biedenkapp, André and Deng, Difan and Benjamins, Carolin and Sass, René and Hutter, Frank},
booktitle = {ArXiv: 2109.09831},
keywords = {SMAC3},
title = {SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization},
year = 2021
}
%0 Conference Paper
%1 LinEgg2021
%A Lindauer, Marius
%A Eggensperger, Katharina
%A Feurer, Matthias
%A Biedenkapp, André
%A Deng, Difan
%A Benjamins, Carolin
%A Sass, René
%A Hutter, Frank
%B ArXiv: 2109.09831
%D 2021
%T SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
%U https://arxiv.org/abs/2109.09831
Holzapfel, C., Sag, S., Graf-Schindler, J., Fischer, M., Drabsch, T., Illig, T., Grallert, H., Stecher, L., Strack, C., Caterson, I., Jebb, S., Hauner, H., and Baessler, A. (2021)Association between single nucleotide polymorphisms and weight reduction in behavioural interventions—a pooled analysis, Nutrients, MDPI 13.

AbstractURLBibTeXEndNoteBibSonomy

Knowledge of the association between single nucleotide polymorphisms (SNPs) and weight loss is limited. The aim was to analyse whether selected obesity-associated SNPs within the fat mass and obesity-associated (FTO), transmembrane protein 18 (TMEM18), melanocortin-4 receptor (MC4R), SEC16 homolog B (SEC16B), and brain-derived neurotrophic factor (BDNF) gene are associated with anthropometric changes during behavioural intervention for weight loss. genetic and anthropometric data from 576 individuals with overweight and obesity from four lifestyle interventions were obtained. A genetic predisposition score (GPS) was calculated. Our results show that study participants had a mean age of 48.2 ± 12.6 years and a mean baseline body mass index of 33.9 ± 6.4 kg/m2. Mean weight reduction after 12 months was −7.7 ± 10.9 kg. After 12 months of intervention, the MC4R SNPs rs571312 and rs17782313 were significantly associated with a greater decrease in body weight and BMI (p = 0.012, p = 0.011, respectively). The investigated SNPs within the other four genetic loci showed no statistically significant association with changes in anthropometric parameters. The GPS showed no statistically significant association with weight reduction. In conclusion there was no consistent evidence for statistically significant associations of SNPs with anthropometric changes during a behavioural intervention. It seems that other factors play a more significant in weight management than the investigated SNPs.
@article{holzapfel2021association,
abstract = {Knowledge of the association between single nucleotide polymorphisms (SNPs) and weight loss is limited. The aim was to analyse whether selected obesity-associated SNPs within the fat mass and obesity-associated (FTO), transmembrane protein 18 (TMEM18), melanocortin-4 receptor (MC4R), SEC16 homolog B (SEC16B), and brain-derived neurotrophic factor (BDNF) gene are associated with anthropometric changes during behavioural intervention for weight loss. genetic and anthropometric data from 576 individuals with overweight and obesity from four lifestyle interventions were obtained. A genetic predisposition score (GPS) was calculated. Our results show that study participants had a mean age of 48.2 ± 12.6 years and a mean baseline body mass index of 33.9 ± 6.4 kg/m2. Mean weight reduction after 12 months was −7.7 ± 10.9 kg. After 12 months of intervention, the MC4R SNPs rs571312 and rs17782313 were significantly associated with a greater decrease in body weight and BMI (p = 0.012, p = 0.011, respectively). The investigated SNPs within the other four genetic loci showed no statistically significant association with changes in anthropometric parameters. The GPS showed no statistically significant association with weight reduction. In conclusion there was no consistent evidence for statistically significant associations of SNPs with anthropometric changes during a behavioural intervention. It seems that other factors play a more significant in weight management than the investigated SNPs.},
author = {Holzapfel, C and Sag, S and Graf-Schindler, J and Fischer, M and Drabsch, T and Illig, T and Grallert, H and Stecher, L and Strack, C and Caterson, ID and Jebb, SA and Hauner, H and Baessler, A},
journal = {Nutrients},
keywords = {l3s},
number = 3,
publisher = {MDPI},
title = {Association between single nucleotide polymorphisms and weight reduction in behavioural interventions—a pooled analysis},
type = {Publication},
volume = 13,
year = 2021
}
%0 Journal Article
%1 holzapfel2021association
%A Holzapfel, C
%A Sag, S
%A Graf-Schindler, J
%A Fischer, M
%A Drabsch, T
%A Illig, T
%A Grallert, H
%A Stecher, L
%A Strack, C
%A Caterson, ID
%A Jebb, SA
%A Hauner, H
%A Baessler, A
%D 2021
%I MDPI
%J Nutrients
%N 3
%R 10.3390/nu13030819
%T Association between single nucleotide polymorphisms and weight reduction in behavioural interventions—a pooled analysis
%U https://ora.ox.ac.uk/objects/uuid:98987391-ba79-4686-a819-5fbe2a79ff45
%V 13
%X Knowledge of the association between single nucleotide polymorphisms (SNPs) and weight loss is limited. The aim was to analyse whether selected obesity-associated SNPs within the fat mass and obesity-associated (FTO), transmembrane protein 18 (TMEM18), melanocortin-4 receptor (MC4R), SEC16 homolog B (SEC16B), and brain-derived neurotrophic factor (BDNF) gene are associated with anthropometric changes during behavioural intervention for weight loss. genetic and anthropometric data from 576 individuals with overweight and obesity from four lifestyle interventions were obtained. A genetic predisposition score (GPS) was calculated. Our results show that study participants had a mean age of 48.2 ± 12.6 years and a mean baseline body mass index of 33.9 ± 6.4 kg/m2. Mean weight reduction after 12 months was −7.7 ± 10.9 kg. After 12 months of intervention, the MC4R SNPs rs571312 and rs17782313 were significantly associated with a greater decrease in body weight and BMI (p = 0.012, p = 0.011, respectively). The investigated SNPs within the other four genetic loci showed no statistically significant association with changes in anthropometric parameters. The GPS showed no statistically significant association with weight reduction. In conclusion there was no consistent evidence for statistically significant associations of SNPs with anthropometric changes during a behavioural intervention. It seems that other factors play a more significant in weight management than the investigated SNPs.
Wu, W., Li, B., Luo, C., and Nejdl, W. (2021)Hashing-Accelerated Graph Neural Networks for Link Prediction. In .

AbstractURLBibTeXEndNoteBibSonomy

Networks are ubiquitous in the real world. Link prediction, as one of the key problems for network-structured data, aims to predict whether there exists a link between two nodes. The traditional approaches are based on the explicit similarity computation between the compact node representation by embedding each node into a low-dimensional space. In order to efficiently handle the intensive similarity computation in link prediction, the hashing technique has been successfully used to produce the node representation in the Hamming space. However, the hashing-based link prediction algorithms face accuracy loss from the randomized hashing techniques or inefficiency from the learning to hash techniques in the embedding process. Currently, the Graph Neural Network (GNN) framework has been widely applied to the graph-related tasks in an end-to-end manner, but it commonly requires substantial computational resources and memory costs due to massive parameter learning, which makes the GNN-based algorithms impractical without the help of a powerful workhorse. In this paper, we propose a simple and effective model called #GNN, which balances the trade-off between accuracy and efficiency. #GNN is able to efficiently acquire node representation in the Hamming space for link prediction by exploiting the randomized hashing technique to implement message passing and capture high-order proximity in the GNN framework. Furthermore, we characterize the discriminative power of #GNN in probability. The extensive experimental results demonstrate that the proposed #GNN algorithm achieves accuracy comparable to the learning-based algorithms and outperforms the randomized algorithm, while running significantly faster than the learning-based algorithms. Also, the proposed algorithm shows excellent scalability on a large-scale network with the limited resources.
@conference{wu2021hashingaccelerated,
abstract = {Networks are ubiquitous in the real world. Link prediction, as one of the key problems for network-structured data, aims to predict whether there exists a link between two nodes. The traditional approaches are based on the explicit similarity computation between the compact node representation by embedding each node into a low-dimensional space. In order to efficiently handle the intensive similarity computation in link prediction, the hashing technique has been successfully used to produce the node representation in the Hamming space. However, the hashing-based link prediction algorithms face accuracy loss from the randomized hashing techniques or inefficiency from the learning to hash techniques in the embedding process. Currently, the Graph Neural Network (GNN) framework has been widely applied to the graph-related tasks in an end-to-end manner, but it commonly requires substantial computational resources and memory costs due to massive parameter learning, which makes the GNN-based algorithms impractical without the help of a powerful workhorse. In this paper, we propose a simple and effective model called #GNN, which balances the trade-off between accuracy and efficiency. #GNN is able to efficiently acquire node representation in the Hamming space for link prediction by exploiting the randomized hashing technique to implement message passing and capture high-order proximity in the GNN framework. Furthermore, we characterize the discriminative power of #GNN in probability. The extensive experimental results demonstrate that the proposed #GNN algorithm achieves accuracy comparable to the learning-based algorithms and outperforms the randomized algorithm, while running significantly faster than the learning-based algorithms. Also, the proposed algorithm shows excellent scalability on a large-scale network with the limited resources.},
author = {Wu, Wei and Li, Bin and Luo, Chuan and Nejdl, Wolfgang},
keywords = {l3s},
note = {cite arxiv:2105.14280},
title = {Hashing-Accelerated Graph Neural Networks for Link Prediction},
year = 2021
}
%0 Generic
%1 wu2021hashingaccelerated
%A Wu, Wei
%A Li, Bin
%A Luo, Chuan
%A Nejdl, Wolfgang
%D 2021
%R 10.1145/3442381.3449884
%T Hashing-Accelerated Graph Neural Networks for Link Prediction
%U http://arxiv.org/abs/2105.14280
%X Networks are ubiquitous in the real world. Link prediction, as one of the key problems for network-structured data, aims to predict whether there exists a link between two nodes. The traditional approaches are based on the explicit similarity computation between the compact node representation by embedding each node into a low-dimensional space. In order to efficiently handle the intensive similarity computation in link prediction, the hashing technique has been successfully used to produce the node representation in the Hamming space. However, the hashing-based link prediction algorithms face accuracy loss from the randomized hashing techniques or inefficiency from the learning to hash techniques in the embedding process. Currently, the Graph Neural Network (GNN) framework has been widely applied to the graph-related tasks in an end-to-end manner, but it commonly requires substantial computational resources and memory costs due to massive parameter learning, which makes the GNN-based algorithms impractical without the help of a powerful workhorse. In this paper, we propose a simple and effective model called #GNN, which balances the trade-off between accuracy and efficiency. #GNN is able to efficiently acquire node representation in the Hamming space for link prediction by exploiting the randomized hashing technique to implement message passing and capture high-order proximity in the GNN framework. Furthermore, we characterize the discriminative power of #GNN in probability. The extensive experimental results demonstrate that the proposed #GNN algorithm achieves accuracy comparable to the learning-based algorithms and outperforms the randomized algorithm, while running significantly faster than the learning-based algorithms. Also, the proposed algorithm shows excellent scalability on a large-scale network with the limited resources.
Zhao, B., van der Aa, H., Nguyen, T. T., Nguyen, Q. V. H., and Weidlich, M. (2021){EIRES}: Efficient Integration of Remote Data in Event Stream Processing. In Proceedings of the 2021 International Conference on Management of Data, {ACM}.

AbstractURLBibTeXEndNoteBibSonomy

To support reactive and predictive applications, complex event processing (CEP) systems detect patterns in event streams based on predefined queries. To determine the events that constitute a query match, their payload data may need to be assessed together with data from remote sources. Such dependencies are problematic, since waiting for remote data to be fetched interrupts the processing of the stream. Yet, without event selection based on remote data, the query state to maintain may grow exponentially. In either case, the performance of the CEP system degrades drastically. To tackle these issues, we present EIRES, a framework for efficient integration of static data from remote sources in CEP. It employs a cost-model to determine when to fetch certain remote data elements and how long to keep them in a cache for future use. EIRES combines strategies for (i) prefetching that queries remote data based on anticipated use and (ii) lazy evaluation that postpones the event selection based on remote data without interrupting the stream processing. Our experiments indicate that the combination of these strategies improves the latency of query evaluation by up to 3,725x for synthetic data and 47x for real-world data.
@inproceedings{Zhao_2021,
abstract = {To support reactive and predictive applications, complex event processing (CEP) systems detect patterns in event streams based on predefined queries. To determine the events that constitute a query match, their payload data may need to be assessed together with data from remote sources. Such dependencies are problematic, since waiting for remote data to be fetched interrupts the processing of the stream. Yet, without event selection based on remote data, the query state to maintain may grow exponentially. In either case, the performance of the CEP system degrades drastically. To tackle these issues, we present EIRES, a framework for efficient integration of static data from remote sources in CEP. It employs a cost-model to determine when to fetch certain remote data elements and how long to keep them in a cache for future use. EIRES combines strategies for (i) prefetching that queries remote data based on anticipated use and (ii) lazy evaluation that postpones the event selection based on remote data without interrupting the stream processing. Our experiments indicate that the combination of these strategies improves the latency of query evaluation by up to 3,725x for synthetic data and 47x for real-world data.},
author = {Zhao, Bo and van der Aa, Han and Nguyen, Thanh Tam and Nguyen, Quoc Viet Hung and Weidlich, Matthias},
booktitle = {Proceedings of the 2021 International Conference on Management of Data},
keywords = {l3s},
month = {06},
publisher = {{ACM}},
title = {{EIRES}: Efficient Integration of Remote Data in Event Stream Processing},
year = 2021
}
%0 Conference Paper
%1 Zhao_2021
%A Zhao, Bo
%A van der Aa, Han
%A Nguyen, Thanh Tam
%A Nguyen, Quoc Viet Hung
%A Weidlich, Matthias
%B Proceedings of the 2021 International Conference on Management of Data
%D 2021
%I {ACM}
%R 10.1145/3448016.3457304
%T {EIRES}: Efficient Integration of Remote Data in Event Stream Processing
%U https://doi.org/10.1145%2F3448016.3457304
%X To support reactive and predictive applications, complex event processing (CEP) systems detect patterns in event streams based on predefined queries. To determine the events that constitute a query match, their payload data may need to be assessed together with data from remote sources. Such dependencies are problematic, since waiting for remote data to be fetched interrupts the processing of the stream. Yet, without event selection based on remote data, the query state to maintain may grow exponentially. In either case, the performance of the CEP system degrades drastically. To tackle these issues, we present EIRES, a framework for efficient integration of static data from remote sources in CEP. It employs a cost-model to determine when to fetch certain remote data elements and how long to keep them in a cache for future use. EIRES combines strategies for (i) prefetching that queries remote data based on anticipated use and (ii) lazy evaluation that postpones the event selection based on remote data without interrupting the stream processing. Our experiments indicate that the combination of these strategies improves the latency of query evaluation by up to 3,725x for synthetic data and 47x for real-world data.
Hu, T., Iosifidis, V., Liao, W., Zhang, H., Yang, M. Y., Ntoutsi, E., and Rosenhahn, B. (2020)FairNN - Conjoint Learning of Fair Representations for Fair Decisions.. In Discovery Science, pp. 581–595, Springer International Publishing.

URLBibTeXEndNoteBibSonomy

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booktitle = {Discovery Science},
keywords = {l3s},
pages = {581-595},
publisher = {Springer International Publishing},
series = {Lecture Notes in Computer Science},
title = {FairNN - Conjoint Learning of Fair Representations for Fair Decisions.},
type = {Publication},
volume = 12323,
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Gra{{\"s}}hof, S., Ackermann, H., Brandt, S., and Ostermann, J. (2020)Multilinear Modelling of Faces and Expressions, Transactions on Pattern Analysis and Machine Intelligence (TPAMI).

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J{ü}rgens, H., Hinrichs, R., and Ostermann, J. (2020)Recognizing Guitar Effects and Their Parameter Settings. In Proceedings of the DAFx2020 (Vol I).

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Awiszus, M., Schubert, F., and Rosenhahn, B. (2020)TOAD-GAN: Coherent Style Level Generation from a Single Example. In AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Best Student Paper Award.

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Ostermann, J., and Hinrichs, R. (2020)Links und rechts verbinden, Unimagazin.

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Hu, T., Iosifidis, V., Wentong, L., Hang, Z., Yang, M. Y., Ntoutsi, E., and Rosenhahn, B. (2020)FairNN - Conjoint Learning of Fair Representations for Fair Decisions. In 23rd International Conference on Discovery Science.

BibTeXEndNoteBibSonomy

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title = {FairNN - Conjoint Learning of Fair Representations for Fair Decisions},
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Dockhorn, A., and Kruse, R. (2020)Forward Model Learning for Motion Control Tasks. In 2020 IEEE 10th International Conference on Intelligent Systems (IS), pp. 1–5.

URLBibTeXEndNoteBibSonomy

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Dockhorn, A. (2020)Vorhersagebasierte Suche f{ü}r autonomes Spielen, pp. 69–78, GI.

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Dockhorn, A., Grueso, J. H., Jeurissen, D., and Perez-Liebana, D. (2020)“Stratega”: A General Strategy Games Framework. In Joint Proceedings of the AIIDE 2020 Workshops co-located with 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020); Artificial Intelligence for Strategy Games, pp. 1–7.

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Dockhorn, A., Schwensfeier, T., and Kruse, R. (2019)Fuzzy Multiset Clustering for Metagame Analysis. In Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019), pp. 536–543.

URLBibTeXEndNoteBibSonomy

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Held, P., Dockhorn, A., and Kruse, R. (2014)On Merging and Dividing of Barabasi-Albert-graphs. In 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS).

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