2021
[1]
J. Ali, P. Lahoti, and K. P. Gummadi, “Accounting for Model Uncertainty in Algorithmic Discrimination,” in Fourth AAAI/ACM Conference on Artificial Intelligence, Ethics and Society, Virtual Conference. (Accepted/in press)
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@inproceedings{Ali_AIES2021, TITLE = {Accounting for Model Uncertainty in Algorithmic Discrimination}, AUTHOR = {Ali, Junaid and Lahoti, Preethi and Gummadi, Krishna P.}, LANGUAGE = {eng}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Fourth AAAI/ACM Conference on Artificial Intelligence, Ethics and Society}, ADDRESS = {Virtual Conference}, }
Endnote
%0 Conference Proceedings %A Ali, Junaid %A Lahoti, Preethi %A Gummadi, Krishna P. %+ Computer Graphics, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society %T Accounting for Model Uncertainty in Algorithmic Discrimination : %G eng %U http://hdl.handle.net/21.11116/0000-0008-72E3-7 %D 2021 %B Fourth AAAI/ACM Conference on Artificial Intelligence, Ethics and Society %Z date of event: 2021-05-19 - 2021-05-21 %C Virtual Conference %B Fourth AAAI/ACM Conference on Artificial Intelligence, Ethics and Society
[2]
K. Budhathoki, M. Boley, and J. Vreeken, “Rule Discovery for Exploratory Causal Reasoning,” in Proceedings of the SIAM International Conference on Data Mining (SDM 2021), Virtual Conference. (Accepted/in press)
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@inproceedings{budhathoki:21:dice, TITLE = {Rule Discovery for Exploratory Causal Reasoning}, AUTHOR = {Budhathoki, Kailash and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, PUBLISHER = {SIAM}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the SIAM International Conference on Data Mining (SDM 2021)}, ADDRESS = {Virtual Conference}, }
Endnote
%0 Conference Proceedings %A Budhathoki, Kailash %A Boley, Mario %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Rule Discovery for Exploratory Causal Reasoning : %G eng %U http://hdl.handle.net/21.11116/0000-0008-2571-F %D 2021 %B SIAM International Conference on Data Mining %Z date of event: 2021-04-29 - 2021-05-01 %C Virtual Conference %B Proceedings of the SIAM International Conference on Data Mining %I SIAM
[3]
E. Chang, X. Shen, D. Zhu, V. Demberg, and H. Su, “Neural Data-to-Text Generation with LM-based Text Augmentation,” in EACL 2021, 16th Conference of the European Chapter of the Association for Computational Linguistics, Online. (Accepted/in press)
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@inproceedings{chang2021neural, TITLE = {Neural Data-to-Text Generation with {LM}-based Text Augmentation}, AUTHOR = {Chang, Ernie and Shen, Xiaoyu and Zhu, Dawei and Demberg, Vera and Su, Hui}, LANGUAGE = {eng}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {EACL 2021, 16th Conference of the European Chapter of the Association for Computational Linguistics}, ADDRESS = {Online}, }
Endnote
%0 Conference Proceedings %A Chang, Ernie %A Shen, Xiaoyu %A Zhu, Dawei %A Demberg, Vera %A Su, Hui %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T Neural Data-to-Text Generation with LM-based Text Augmentation : %G eng %U http://hdl.handle.net/21.11116/0000-0008-149E-0 %D 2021 %B 16th Conference of the European Chapter of the Association for Computational Linguistics %Z date of event: 2021-04-19 - 2021-04-23 %C Online %B EACL 2021
[4]
L. De Stefani, E. Terolli, and E. Upfal, “Tiered Sampling: An Efficient Method for Counting Sparse Motifs in Massive Graph Streams,” ACM Transactions on Knowledge Discovery from Data, vol. 15, no. 5, 2021.
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@article{DeStefani2021, TITLE = {Tiered Sampling: {A}n Efficient Method for Counting Sparse Motifs in Massive Graph Streams}, AUTHOR = {De Stefani, Lorenzo and Terolli, Erisa and Upfal, Eli}, LANGUAGE = {eng}, ISSN = {1556-4681}, DOI = {10.1145/3441299}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, JOURNAL = {ACM Transactions on Knowledge Discovery from Data}, VOLUME = {15}, NUMBER = {5}, PAGES = {1--52}, EID = {79}, }
Endnote
%0 Journal Article %A De Stefani, Lorenzo %A Terolli, Erisa %A Upfal, Eli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Tiered Sampling: An Efficient Method for Counting Sparse Motifs in Massive Graph Streams : %G eng %U http://hdl.handle.net/21.11116/0000-0008-ED51-2 %R 10.1145/3441299 %7 2021 %D 2021 %J ACM Transactions on Knowledge Discovery from Data %V 15 %N 5 %& 1 %P 1 - 52 %Z sequence number: 79 %I ACM %C New York, NY %@ false
[5]
J. Fischer, F. B. Ardakani, K. Kattler, J. Walter, and M. H. Schulz, “CpG Content-dependent Associations between Transcription Factors and Histone Modifications,” PLoS One, vol. 16, no. 4, 2021.
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@article{fischer:21:cpgtfhm, TITLE = {{CpG} content-dependent associations between transcription factors and histone modifications}, AUTHOR = {Fischer, Jonas and Ardakani, Fatemeh Behjati and Kattler, Kathrin and Walter, J{\"o}rn and Schulz, Marcel Holger}, LANGUAGE = {eng}, ISSN = {1932-6203}, DOI = {10.1371/journal.pone.0249985}, PUBLISHER = {Public Library of Science}, ADDRESS = {San Francisco, CA}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, JOURNAL = {PLoS One}, VOLUME = {16}, NUMBER = {4}, EID = {0249985}, }
Endnote
%0 Journal Article %A Fischer, Jonas %A Ardakani, Fatemeh Behjati %A Kattler, Kathrin %A Walter, Jörn %A Schulz, Marcel Holger %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T CpG Content-dependent Associations between Transcription Factors and Histone Modifications : %G eng %U http://hdl.handle.net/21.11116/0000-0008-5602-5 %R 10.1371/journal.pone.0249985 %7 2021 %D 2021 %J PLoS One %V 16 %N 4 %Z sequence number: 0249985 %I Public Library of Science %C San Francisco, CA %@ false
[6]
A. Ghazimatin, S. Pramanik, R. Saha Roy, and G. Weikum, “ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models,” 2021. [Online]. Available: https://arxiv.org/abs/2102.09388. (arXiv: 2102.09388)
Abstract
System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of generated recommendations themselves. We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors, overcoming sparseness by label propagation with item-similarity-based neighborhoods. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart. Insightful experiments with a real user study show significant improvements in movie and book recommendations over item-level feedback.
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@online{Ghazimatin_2102.09388, TITLE = {{ELIXIR}: {L}earning from User Feedback on Explanations to Improve Recommender Models}, AUTHOR = {Ghazimatin, Azin and Pramanik, Soumajit and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2102.09388}, EPRINT = {2102.09388}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of generated recommendations themselves. We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors, overcoming sparseness by label propagation with item-similarity-based neighborhoods. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart. Insightful experiments with a real user study show significant improvements in movie and book recommendations over item-level feedback.}, }
Endnote
%0 Report %A Ghazimatin, Azin %A Pramanik, Soumajit %A Saha Roy, Rishiraj %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0309-B %U https://arxiv.org/abs/2102.09388 %D 2021 %X System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of generated recommendations themselves. We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors, overcoming sparseness by label propagation with item-similarity-based neighborhoods. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart. Insightful experiments with a real user study show significant improvements in movie and book recommendations over item-level feedback. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Learning, cs.LG
[7]
A. Ghazimatin, S. Pramanik, R. Saha Roy, and G. Weikum, “ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models,” in Proceedings of The Web Conference 2021 (WWW 2021), Ljubljana, Slovenia. (Accepted/in press)
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@inproceedings{Ghazimatin_WWW21, TITLE = {{ELIXIR}: {L}earning from User Feedback on Explanations to Improve Recommender Models}, AUTHOR = {Ghazimatin, Azin and Pramanik, Soumajit and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, DOI = {10.1145/3442381.3449848}, PUBLISHER = {ACM}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of The Web Conference 2021 (WWW 2021)}, ADDRESS = {Ljubljana, Slovenia}, }
Endnote
%0 Conference Proceedings %A Ghazimatin, Azin %A Pramanik, Soumajit %A Saha Roy, Rishiraj %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0303-1 %R 10.1145/3442381.3449848 %D 2021 %B 30th The Web Conference %Z date of event: 2021-04-30 - %C Ljubljana, Slovenia %B Proceedings of The Web Conference 2021 %I ACM
[8]
A. Guimarães and G. Weikum, “X-Posts Explained: Analyzing and Predicting Controversial Contributions in Thematically Diverse Reddit Forums,” in Proceedings of the Fifteenth International Conference on Web and Social Media (ICWSM 2021), Atlanta, GA, USA. (Accepted/in press)
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@inproceedings{Guimaraes_ICWSM2021, TITLE = {X-Posts Explained: {A}nalyzing and Predicting Controversial Contributions in Thematically Diverse {R}eddit Forums}, AUTHOR = {Guimar{\~a}es, Anna and Weikum, Gerhard}, LANGUAGE = {eng}, PUBLISHER = {AAAI}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the Fifteenth International Conference on Web and Social Media (ICWSM 2021)}, ADDRESS = {Atlanta, GA, USA}, }
Endnote
%0 Conference Proceedings %A Guimarães, Anna %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T X-Posts Explained: Analyzing and Predicting Controversial Contributions in Thematically Diverse Reddit Forums : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0345-7 %D 2021 %B 15th International Conference on Web and Social Media %Z date of event: 2021-06-07 - 2021-06-10 %C Atlanta, GA, USA %B Proceedings of the Fifteenth International Conference on Web and Social Media %I AAAI
[9]
E. Heiter, J. Fischer, and J. Vreeken, “Factoring Out Prior Knowledge from Low-dimensional Embeddings,” 2021. [Online]. Available: https://arxiv.org/abs/2103.01828. (arXiv: 2103.01828)
Abstract
Low-dimensional embedding techniques such as tSNE and UMAP allow visualizing high-dimensional data and therewith facilitate the discovery of interesting structure. Although they are widely used, they visualize data as is, rather than in light of the background knowledge we have about the data. What we already know, however, strongly determines what is novel and hence interesting. In this paper we propose two methods for factoring out prior knowledge in the form of distance matrices from low-dimensional embeddings. To factor out prior knowledge from tSNE embeddings, we propose JEDI that adapts the tSNE objective in a principled way using Jensen-Shannon divergence. To factor out prior knowledge from any downstream embedding approach, we propose CONFETTI, in which we directly operate on the input distance matrices. Extensive experiments on both synthetic and real world data show that both methods work well, providing embeddings that exhibit meaningful structure that would otherwise remain hidden.
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@online{heiter:21:factoring, TITLE = {Factoring Out Prior Knowledge from Low-dimensional Embeddings}, AUTHOR = {Heiter, Edith and Fischer, Jonas and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2103.01828}, EPRINT = {2103.01828}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Low-dimensional embedding techniques such as tSNE and UMAP allow visualizing high-dimensional data and therewith facilitate the discovery of interesting structure. Although they are widely used, they visualize data as is, rather than in light of the background knowledge we have about the data. What we already know, however, strongly determines what is novel and hence interesting. In this paper we propose two methods for factoring out prior knowledge in the form of distance matrices from low-dimensional embeddings. To factor out prior knowledge from tSNE embeddings, we propose JEDI that adapts the tSNE objective in a principled way using Jensen-Shannon divergence. To factor out prior knowledge from any downstream embedding approach, we propose CONFETTI, in which we directly operate on the input distance matrices. Extensive experiments on both synthetic and real world data show that both methods work well, providing embeddings that exhibit meaningful structure that would otherwise remain hidden.}, }
Endnote
%0 Report %A Heiter, Edith %A Fischer, Jonas %A Vreeken, Jilles %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Factoring Out Prior Knowledge from Low-dimensional Embeddings : %G eng %U http://hdl.handle.net/21.11116/0000-0008-16ED-5 %U https://arxiv.org/abs/2103.01828 %D 2021 %X Low-dimensional embedding techniques such as tSNE and UMAP allow visualizing high-dimensional data and therewith facilitate the discovery of interesting structure. Although they are widely used, they visualize data as is, rather than in light of the background knowledge we have about the data. What we already know, however, strongly determines what is novel and hence interesting. In this paper we propose two methods for factoring out prior knowledge in the form of distance matrices from low-dimensional embeddings. To factor out prior knowledge from tSNE embeddings, we propose JEDI that adapts the tSNE objective in a principled way using Jensen-Shannon divergence. To factor out prior knowledge from any downstream embedding approach, we propose CONFETTI, in which we directly operate on the input distance matrices. Extensive experiments on both synthetic and real world data show that both methods work well, providing embeddings that exhibit meaningful structure that would otherwise remain hidden. %K Computer Science, Learning, cs.LG,Statistics, Machine Learning, stat.ML
[10]
V. T. Ho, K. Pal, and G. Weikum, “QuTE: Answering Quantity Queries from Web Tables,” in SIGMOD 2021, Xi’an, Shaanxi, China. (Accepted/in press)
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@inproceedings{Thinh_SIG21, TITLE = {Qu{TE}: Answering Quantity Queries from Web Tables}, AUTHOR = {Ho, Vinh Thinh and Pal, Koninika and Weikum, Gerhard}, LANGUAGE = {eng}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {SIGMOD 2021}, ADDRESS = {Xi'an, Shaanxi, China}, }
Endnote
%0 Conference Proceedings %A Ho, Vinh Thinh %A Pal, Koninika %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T QuTE: Answering Quantity Queries from Web Tables : %G eng %U http://hdl.handle.net/21.11116/0000-0008-052E-0 %D 2021 %B SIGMOD 2021 %Z date of event: 2021-06-19 - 2021-06-25 %C Xi'an, Shaanxi, China %B SIGMOD 2021
[11]
V. T. Ho, K. Pal, S. Razniewski, K. Berberich, and G. Weikum, “Extracting Contextualized Quantity Facts from Web Tables,” in Proceedings of The Web Conference 2021 (WWW 2021), Ljubljana, Slovenia. (Accepted/in press)
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@inproceedings{Thinh_WWW21, TITLE = {Extracting Contextualized Quantity Facts from Web Tables}, AUTHOR = {Ho, Vinh Thinh and Pal, Koninika and Razniewski, Simon and Berberich, Klaus and Weikum, Gerhard}, LANGUAGE = {eng}, PUBLISHER = {ACM}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of The Web Conference 2021 (WWW 2021)}, ADDRESS = {Ljubljana, Slovenia}, }
Endnote
%0 Conference Proceedings %A Ho, Vinh Thinh %A Pal, Koninika %A Razniewski, Simon %A Berberich, Klaus %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Extracting Contextualized Quantity Facts from Web Tables : %G eng %U http://hdl.handle.net/21.11116/0000-0008-04A0-E %D 2021 %B 30th The Web Conference %Z date of event: 2021-04-30 - %C Ljubljana, Slovenia %B Proceedings of The Web Conference 2021 %I ACM
[12]
M. Kaiser, R. Saha Roy, and G. Weikum, “Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Online. (Accepted/in press)
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@inproceedings{kaiser2021reinforcement, TITLE = {Reinforcement Learning from Reformulations in~Conversational Question Answering over Knowledge Graphs}, AUTHOR = {Kaiser, Magdalena and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, PUBLISHER = {ACM}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval}, ADDRESS = {Online}, }
Endnote
%0 Conference Proceedings %A Kaiser, Magdalena %A Saha Roy, Rishiraj %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0008-513E-8 %D 2021 %B 44th International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2021-07-11 - 2021-07-15 %C Online %B Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval %I ACM
[13]
J. Kalofolias, P. Welke, and J. Vreeken, “SUSAN: The Structural Similarity Random Walk Kernel,” in Proceedings of the SIAM International Conference on Data Mining (SDM 2021), Virtual Conference. (Accepted/in press)
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@inproceedings{kalofolias:21:susan, TITLE = {{SUSAN}: The Structural Similarity Random Walk Kernel}, AUTHOR = {Kalofolias, Janis and Welke, Pascal and Vreeken, Jilles}, LANGUAGE = {eng}, PUBLISHER = {SIAM}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the SIAM International Conference on Data Mining (SDM 2021)}, ADDRESS = {Virtual Conference}, }
Endnote
%0 Conference Proceedings %A Kalofolias, Janis %A Welke, Pascal %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T SUSAN: The Structural Similarity Random Walk Kernel : %G eng %U http://hdl.handle.net/21.11116/0000-0008-26C9-B %D 2021 %B SIAM International Conference on Data Mining %Z date of event: 2021-04-29 - 2021-05-01 %C Virtual Conference %B Proceedings of the SIAM International Conference on Data Mining %I SIAM
[14]
P. Mandros, “Discovering robust dependencies from data,” Universität des Saarlandes, Saarbrücken, 2021.
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@phdthesis{Panphd2020, TITLE = {Discovering robust dependencies from data}, AUTHOR = {Mandros, Panagiotis}, LANGUAGE = {eng}, DOI = {10.22028/D291-34291}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, }
Endnote
%0 Thesis %A Mandros, Panagiotis %Y Vreeken, Jilles %A referee: Weikum, Gerhard %A referee: Webb, Geoffrey %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Discovering robust dependencies from data : %G eng %U http://hdl.handle.net/21.11116/0000-0008-E4CF-E %R 10.22028/D291-34291 %I Universität des Saarlandes %C Saarbrücken %D 2021 %P 194 p. %V phd %9 phd %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/31535
[15]
A. Marx, L. Yang, and M. van Leeuwen, “Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multidimensional Adaptive Histograms,” in Proceedings of the SIAM International Conference on Data Mining (SDM 2021), Virtual Conference. (Accepted/in press)
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@inproceedings{marx:20:myl, TITLE = {Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multidimensional Adaptive Histograms}, AUTHOR = {Marx, Alexander and Yang, Lincen and van Leeuwen, Matthijs}, LANGUAGE = {eng}, PUBLISHER = {SIAM}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the SIAM International Conference on Data Mining (SDM 2021)}, ADDRESS = {Virtual Conference}, }
Endnote
%0 Conference Proceedings %A Marx, Alexander %A Yang, Lincen %A van Leeuwen, Matthijs %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multidimensional Adaptive Histograms : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0BC7-C %D 2021 %B SIAM International Conference on Data Mining %Z date of event: 2021-04-29 - 2021-05-01 %C Virtual Conference %B Proceedings of the SIAM International Conference on Data Mining %I SIAM
[16]
A. Marx, A. Gretton, and J. M. Mooij, “A Weaker Faithfulness Assumption based on Triple Interactions,” 2021. [Online]. Available: https://arxiv.org/abs/2010.14265. (arXiv: 2010.14265)
Abstract
One of the core assumptions in causal discovery is the faithfulness assumption---i.e. assuming that independencies found in the data are due to separations in the true causal graph. This assumption can, however, be violated in many ways, including xor connections, deterministic functions or cancelling paths. In this work, we propose a weaker assumption that we call 2-adjacency faithfulness. In contrast to adjacency faithfulness, which assumes that there is no conditional independence between each pair of variables that are connected in the causal graph, we only require no conditional independence between a node and a subset of its Markov blanket that can contain up to two nodes. Equivalently, we adapt orientation faithfulness to this setting. We further propose a sound orientation rule for causal discovery that applies under weaker assumptions. As a proof of concept, we derive a modified Grow and Shrink algorithm that recovers the Markov blanket of a target node and prove its correctness under strictly weaker assumptions than the standard faithfulness assumption.
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@online{Marxarxiv21, TITLE = {A Weaker Faithfulness Assumption based on Triple Interactions}, AUTHOR = {Marx, Alexander and Gretton, Arthur and Mooij, Joris M.}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2010.14265}, EPRINT = {2010.14265}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {One of the core assumptions in causal discovery is the faithfulness assumption---i.e. assuming that independencies found in the data are due to separations in the true causal graph. This assumption can, however, be violated in many ways, including xor connections, deterministic functions or cancelling paths. In this work, we propose a weaker assumption that we call 2-adjacency faithfulness. In contrast to adjacency faithfulness, which assumes that there is no conditional independence between each pair of variables that are connected in the causal graph, we only require no conditional independence between a node and a subset of its Markov blanket that can contain up to two nodes. Equivalently, we adapt orientation faithfulness to this setting. We further propose a sound orientation rule for causal discovery that applies under weaker assumptions. As a proof of concept, we derive a modified Grow and Shrink algorithm that recovers the Markov blanket of a target node and prove its correctness under strictly weaker assumptions than the standard faithfulness assumption.}, }
Endnote
%0 Report %A Marx, Alexander %A Gretton, Arthur %A Mooij, Joris M. %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T A Weaker Faithfulness Assumption based on Triple Interactions : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0BCE-5 %U https://arxiv.org/abs/2010.14265 %D 2021 %X One of the core assumptions in causal discovery is the faithfulness assumption---i.e. assuming that independencies found in the data are due to separations in the true causal graph. This assumption can, however, be violated in many ways, including xor connections, deterministic functions or cancelling paths. In this work, we propose a weaker assumption that we call 2-adjacency faithfulness. In contrast to adjacency faithfulness, which assumes that there is no conditional independence between each pair of variables that are connected in the causal graph, we only require no conditional independence between a node and a subset of its Markov blanket that can contain up to two nodes. Equivalently, we adapt orientation faithfulness to this setting. We further propose a sound orientation rule for causal discovery that applies under weaker assumptions. As a proof of concept, we derive a modified Grow and Shrink algorithm that recovers the Markov blanket of a target node and prove its correctness under strictly weaker assumptions than the standard faithfulness assumption. %K Statistics, Machine Learning, stat.ML,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Learning, cs.LG
[17]
A. Marx, “Information-Theoretic Causal Discovery,” Universität des Saarlandes, Saarbrücken, 2021.
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@phdthesis{Marxphd2020, TITLE = {Information-Theoretic Causal Discovery}, AUTHOR = {Marx, Alexander}, LANGUAGE = {eng}, DOI = {10.22028/D291-34290}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, }
Endnote
%0 Thesis %A Marx, Alexander %Y Vreeken, Jilles %A referee: Weikum, Gerhard %A referee: Ommen, Thijs van %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Information-Theoretic Causal Discovery : %G eng %U http://hdl.handle.net/21.11116/0000-0008-EECA-9 %R 10.22028/D291-34290 %I Universität des Saarlandes %C Saarbrücken %D 2021 %P 195 p. %V phd %9 phd %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/31480
[18]
O. Mian, A. Marx, and J. Vreeken, “Discovering Fully Oriented Causal Networks,” in Thirty-Fifth AAAI Conference on Artificial Intelligence, Vancouver, Canada. (Accepted/in press)
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@inproceedings{mian:20:globe, TITLE = {Discovering Fully Oriented Causal Networks}, AUTHOR = {Mian, Osman and Marx, Alexander and Vreeken, Jilles}, LANGUAGE = {eng}, PUBLISHER = {AAAI}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Thirty-Fifth AAAI Conference on Artificial Intelligence}, ADDRESS = {Vancouver, Canada}, }
Endnote
%0 Conference Proceedings %A Mian, Osman %A Marx, Alexander %A Vreeken, Jilles %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Discovering Fully Oriented Causal Networks : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0BCB-8 %D 2021 %B The Thirty-Fifth Conference on Artificial Intelligence %Z date of event: 2021-02-02 - 2021-02-09 %C Vancouver, Canada %B Thirty-Fifth AAAI Conference on Artificial Intelligence %I AAAI
[19]
S. Nag Chowdhury, S. Razniewski, and G. Weikum, “SANDI: Story-and-Images Alignment,” in EACL 2021, 16th Conference of the European Chapter of the Association for Computational Linguistics, Online. (Accepted/in press)
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@inproceedings{Thinh_EACL21, TITLE = {{SANDI}: Story-and-Images Alignment}, AUTHOR = {Nag Chowdhury, Sreyasi and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {EACL 2021, 16th Conference of the European Chapter of the Association for Computational Linguistics}, ADDRESS = {Online}, }
Endnote
%0 Conference Proceedings %A Nag Chowdhury, Sreyasi %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T SANDI: Story-and-Images Alignment : %G eng %U http://hdl.handle.net/21.11116/0000-0008-04A2-C %D 2021 %B 16th Conference of the European Chapter of the Association for Computational Linguistics %Z date of event: 2021-04-19 - 2021-04-23 %C Online %B EACL 2021
[20]
S. Nag Chowdhury, “Exploiting Image-Text Synergy for Contextual Image Captioning,” in LANTERN 2021, The First Workshop Beyond Vision and LANguage: inTEgrating Real-world kNowledge, Virtual. (Accepted/in press)
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@inproceedings{Chod_ECAL2021, TITLE = {Exploiting Image-Text Synergy for Contextual Image Captioning}, AUTHOR = {Nag Chowdhury, Sreyasi}, LANGUAGE = {eng}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {LANTERN 2021, The First Workshop Beyond Vision and LANguage: inTEgrating Real-world kNowledge}, ADDRESS = {Virtual}, }
Endnote
%0 Conference Proceedings %A Nag Chowdhury, Sreyasi %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Exploiting Image-Text Synergy for Contextual Image Captioning : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0E60-D %D 2021 %B The First Workshop Beyond Vision and LANguage: inTEgrating Real-world kNowledge %Z date of event: 2021-04-20 - 2021-04-20 %C Virtual %B LANTERN 2021
[21]
T.-P. Nguyen, S. Razniewski, and G. Weikum, “Advanced Semantics for Commonsense Knowledge Extraction,” in Proceedings of The Web Conference 2021 (WWW 2021), Ljubljana, Slovenia. (Accepted/in press)
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@inproceedings{Nguyen_WWW21, TITLE = {Advanced Semantics for Commonsense Knowledge Extraction}, AUTHOR = {Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, PUBLISHER = {ACM}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of The Web Conference 2021 (WWW 2021)}, ADDRESS = {Ljubljana, Slovenia}, }
Endnote
%0 Conference Proceedings %A Nguyen, Tuan-Phong %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Advanced Semantics for Commonsense Knowledge Extraction : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0196-D %D 2021 %B 30th The Web Conference %Z date of event: 2021-04-30 - %C Ljubljana, Slovenia %B Proceedings of The Web Conference 2021 %I ACM
[22]
J. Romero, “Pyformlang: An Educational Library for Formal Language Manipulation,” in SIGCSE ’21, The 52nd ACM Technical Symposium on Computer Science Education, Virtual Event, USA. (Accepted/in press)
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@inproceedings{Romero_SIGCSE21, TITLE = {Pyformlang: {An} Educational Library for Formal Language Manipulation}, AUTHOR = {Romero, Julien}, LANGUAGE = {eng}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {SIGCSE '21, The 52nd ACM Technical Symposium on Computer Science Education}, ADDRESS = {Virtual Event, USA}, }
Endnote
%0 Conference Proceedings %A Romero, Julien %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Pyformlang: An Educational Library for Formal Language Manipulation : %G eng %U http://hdl.handle.net/21.11116/0000-0007-F836-5 %D 2021 %B The 52nd ACM Technical Symposium on Computer Science Education %Z date of event: 2021-03-13 - 2021-03-20 %C Virtual Event, USA %B SIGCSE '21
[23]
A. Tigunova, P. Mirza, A. Yates, and G. Weikum, “Exploring Personal Knowledge Extraction from Conversations with CHARM,” in WSDM ’21, 14th International Conference on Web Search and Data Mining, Jerusalem, Israel (Online). (Accepted/in press)
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@inproceedings{Tigunova_WSDM21, TITLE = {Exploring Personal Knowledge Extraction from Conversations with {CHARM}}, AUTHOR = {Tigunova, Anna and Mirza, Paramita and Yates, Andrew and Weikum, Gerhard}, LANGUAGE = {eng}, PUBLISHER = {ACM}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {WSDM '21, 14th International Conference on Web Search and Data Mining}, ADDRESS = {Jerusalem, Israel (Online)}, }
Endnote
%0 Conference Proceedings %A Tigunova, Anna %A Mirza, Paramita %A Yates, Andrew %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Exploring Personal Knowledge Extraction from Conversations with CHARM : %G eng %U http://hdl.handle.net/21.11116/0000-0007-F850-7 %D 2021 %B 14th International Conference on Web Search and Data Mining %Z date of event: 2021-03-08 - 2021-03-12 %C Jerusalem, Israel (Online) %B WSDM '21 %I ACM
[24]
G. H. Torbati, A. Yates, and G. Weikum, “You Get What You Chat: Using Conversations to Personalize Search-based Recommendations,” in Advances in Information Retrieval (ECIR 2021), Lucca, Italy (Online Event), 2021.
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@inproceedings{Torbati_ECIR2021, TITLE = {You Get What You Chat: {U}sing Conversations to Personalize Search-based Recommendations}, AUTHOR = {Torbati, Ghazaleh Haratinezhad and Yates, Andrew and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-3-030-72112-1}, DOI = {10.1007/978-3-030-72113-8_14}, PUBLISHER = {Springer}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2021)}, EDITOR = {Hiemstra, Djoerd and Moens, Marie-Francine and Mothe, Josiane and Perego, Raffaele and Potthast, Martin and Sebastiani, Fabrizio}, PAGES = {207--223}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {12656}, ADDRESS = {Lucca, Italy (Online Event)}, }
Endnote
%0 Conference Proceedings %A Torbati, Ghazaleh Haratinezhad %A Yates, Andrew %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T You Get What You Chat: Using Conversations to Personalize Search-based Recommendations : %G eng %U http://hdl.handle.net/21.11116/0000-0007-ECA2-8 %R 10.1007/978-3-030-72113-8_14 %D 2021 %B 43rd European Conference on IR Research %Z date of event: 2021-03-28 - 2021-04-01 %C Lucca, Italy (Online Event) %B Advances in Information Retrieval %E Hiemstra, Djoerd; Moens, Marie-Francine; Mothe, Josiane; Perego, Raffaele; Potthast, Martin; Sebastiani, Fabrizio %P 207 - 223 %I Springer %@ 978-3-030-72112-1 %B Lecture Notes in Computer Science %N 12656
[25]
K. H. Tran, A. Ghazimatin, and R. Saha Roy, “Counterfactual Explanations for Neural Recommenders,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Online. (Accepted/in press)
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@inproceedings{tran2021counterfactual, TITLE = {Counterfactual Explanations for Neural Recommenders}, AUTHOR = {Tran, Khanh Hiep and Ghazimatin, Azin and Saha Roy, Rishiraj}, LANGUAGE = {eng}, PUBLISHER = {ACM}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval}, ADDRESS = {Online}, }
Endnote
%0 Conference Proceedings %A Tran, Khanh Hiep %A Ghazimatin, Azin %A Saha Roy, Rishiraj %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Counterfactual Explanations for Neural Recommenders : %G eng %U http://hdl.handle.net/21.11116/0000-0008-5140-4 %D 2021 %B 44th International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2021-07-11 - 2021-07-15 %C Online %B Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval %I ACM