Publications

• Explainability in GeoAI
• Ximeng Cheng, Marc Vischer, Zachary Schellin, Leila Arras, Monique M. Kuglitsch, Wojciech Samek, and Jackie Ma
• Chapter In: Handbook of Geospatial Artificial Intelligence, S. Gao, Y. Hu & W. Li (Eds.), pages 177-200, December 2023. CRC Press. doi.org/10.1201/9781003308423-9
• [bibtex]  paper
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• Explainable sequence-to-sequence GRU neural network for pollution forecasting
• Sara Mirzavand Borujeni, Leila Arras, Vignesh Srinivasan, and Wojciech Samek
• Scientific Reports, vol 13, number 9940, June 2023. doi.org/10.1038/s41598-023-35963-2
• [bibtex]  paper
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• Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome
• Andreas Rieckmann, Piotr Dworzynski, Leila Arras, Sebastian Lapuschkin, Wojciech Samek, Onyebuchi Aniweta Arah, Naja Hulvej Rod, and Claus Thorn Ekstrøm
• International Journal of Epidemiology, vol 51, issue 5, pages 1622-1636, October 2022. doi.org/10.1093/ije/dyac078
• [bibtex]  paper  suppl-mat  slides  webpage  code
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• CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations
• Leila Arras*, Ahmed Osman*, and Wojciech Samek (*equal contribution)
• Information Fusion, vol 81, pages 14-40, May 2022. doi.org/10.1016/j.inffus.2021.11.008
• [bibtex]  paper  poster  dataset  code
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• Explaining the Decisions of Convolutional and Recurrent Neural Networks
• Wojciech Samek, Leila Arras, Ahmed Osman, Grégoire Montavon, and Klaus-Robert Müller
• Chapter In: Mathematical Aspects of Deep Learning, P. Grohs & G. Kutyniok (Eds.), pages 229-266, November 2022. Cambridge University Press. doi.org/10.1017/9781009025096.006
• [bibtex]  paper
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• Towards Ground Truth Evaluation of Visual Explanations
• Ahmed Osman*, Leila Arras*, and Wojciech Samek (*equal contribution)
• arXiv:2003.07258v1, March 2020, Computing Research Repository (CoRR). arxiv.org/abs/2003.07258v1
• [bibtex]  paper  dataset
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• Explaining and Interpreting LSTMs
• Leila Arras*, José Arjona-Medina*, Michael Widrich, Grégoire Montavon, Michael Gillhofer, Klaus-Robert Müller, Sepp Hochreiter, and Wojciech Samek (*equal contribution)
• Chapter In: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Lecture Notes in Computer Science, vol 11700, pages 211-238, September 2019. Springer, Cham. doi.org/10.1007/978-3-030-28954-6_11
• [bibtex]  paper
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• Evaluating Recurrent Neural Network Explanations
• Leila Arras, Ahmed Osman, Klaus-Robert Müller, and Wojciech Samek
• ACL 2019 BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP (oral presentation), pages 113-126, Florence, Italy, August 2019. Association for Computational Linguistics. doi.org/10.18653/v1/W19-4813
• [bibtex]  paper  slides  talk
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• Explaining Recurrent Neural Network Predictions in Sentiment Analysis
• Leila Arras, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek
• EMNLP 2017 Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA), pages 159-168, Copenhagen, Denmark, September 2017. Association for Computational Linguistics. doi.org/10.18653/v1/W17-5221
• [bibtex]  paper  poster  code
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• "What is relevant in a text document?": An interpretable machine learning approach
• Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek
• PLOS ONE, 12(8): e0181142, August 2017. Public Library of Science. doi.org/10.1371/journal.pone.0181142
• [bibtex]  paper  demo
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• Explaining Predictions of Non-Linear Classifiers in NLP
• Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek
• ACL 2016 Workshop on Representation Learning for NLP (RepL4NLP), pages 1-7, Berlin, Germany, August 2016. Association for Computational Linguistics. doi.org/10.18653/v1/W16-1601
• [bibtex]  paper  poster

Previous work

• Theoretical and empirical evaluation of data reduction for exact Kemeny Rank Aggregation
• Nadja Betzler, Robert Bredereck, and Rolf Niedermeier (Leila Arras in the Acknowledgments)
• Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS), vol 28, issue 5, pages 721-748, September 2014. doi.org/10.1007/s10458-013-9236-y

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