Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining / Aug 14, 2021

Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., & Eickhoff, C. (2021). A Transformer-based Framework for Multivariate Time Series Representation Learning. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2114–2124. https://doi.org/10.1145/3447548.3467401

The Lancet Respiratory Medicine / Dec 01, 2018

Meyer, A., Zverinski, D., Pfahringer, B., Kempfert, J., Kuehne, T., Sündermann, S. H., Stamm, C., Hofmann, T., Falk, V., & Eickhoff, C. (2018). Machine learning for real-time prediction of complications in critical care: a retrospective study. The Lancet Respiratory Medicine, 6(12), 905–914. https://doi.org/10.1016/s2213-2600(18)30300-x

Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval / Aug 12, 2012

Eickhoff, C., Harris, C. G., de Vries, A. P., & Srinivasan, P. (2012). Quality through flow and immersion: gamifying crowdsourced relevance assessments. Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, 871–880. https://doi.org/10.1145/2348283.2348400

Information Retrieval / Feb 14, 2012

Eickhoff, C., & de Vries, A. P. (2012). Increasing cheat robustness of crowdsourcing tasks. Information Retrieval, 16(2), 121–137. https://doi.org/10.1007/s10791-011-9181-9

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining / Feb 02, 2018

Eickhoff, C. (2018). Cognitive Biases in Crowdsourcing. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 162–170. https://doi.org/10.1145/3159652.3159654

Proceedings of the 7th ACM international conference on Web search and data mining / Feb 24, 2014

Eickhoff, C., Teevan, J., White, R., & Dumais, S. (2014). Lessons from the journey: a query log analysis of within-session learning. Proceedings of the 7th ACM International Conference on Web Search and Data Mining, 223–232. https://doi.org/10.1145/2556195.2556217

Jan 01, 2011

Vuurens, J. B. P., Eickhoff, C., & de Vries, A. P. (2011). Managing the Quality of Large-Scale Crowdsourcing. National Institute of Standards and Technology (NIST). https://doi.org/10.6028/nist.sp.500-296.crowd-tud_dmir

Proceedings of the 25th International Conference on World Wide Web / Apr 11, 2016

Ganea, O.-E., Ganea, M., Lucchi, A., Eickhoff, C., & Hofmann, T. (2016). Probabilistic Bag-Of-Hyperlinks Model for Entity Linking. Proceedings of the 25th International Conference on World Wide Web, 927–938. https://doi.org/10.1145/2872427.2882988

Jan 01, 2011

Eickhoff, C., Harris, C. G., Srinivasan, P., & de Vries, A. P. (2011). GeAnn at the TREC 2011 Crowdsourcing Track. National Institute of Standards and Technology (NIST). https://doi.org/10.6028/nist.sp.500-296.crowd-geann

Dec 02, 2024

Sienicki, K. (2024). Comment on the Paper Titled ’The Origin of Quantum Mechanical Statistics: Insights from Research on Human Language’ (arXiv preprint arXiv:2407.14924, 2024). https://doi.org/10.20944/preprints202411.2377.v1

Journal of Public Health Policy / Nov 22, 2021

Thomasian, N. M., Eickhoff, C., & Adashi, E. Y. (2021). Advancing health equity with artificial intelligence. Journal of Public Health Policy, 42(4), 602–611. https://doi.org/10.1057/s41271-021-00319-5

Journal of the American Medical Informatics Association / Sep 23, 2022

Golovanevsky, M., Eickhoff, C., & Singh, R. (2022). Multimodal attention-based deep learning for Alzheimer’s disease diagnosis. Journal of the American Medical Informatics Association, 29(12), 2014–2022. https://doi.org/10.1093/jamia/ocac168

npj Digital Medicine / Oct 26, 2020

Rank, N., Pfahringer, B., Kempfert, J., Stamm, C., Kühne, T., Schoenrath, F., Falk, V., Eickhoff, C., & Meyer, A. (2020). Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance. Npj Digital Medicine, 3(1). https://doi.org/10.1038/s41746-020-00346-8

Proceedings of the 20th ACM international conference on Information and knowledge management / Oct 24, 2011

Li, W., Serdyukov, P., de Vries, A. P., Eickhoff, C., & Larson, M. (2011). The where in the tweet. Proceedings of the 20th ACM International Conference on Information and Knowledge Management, 2473–2476. https://doi.org/10.1145/2063576.2063995

Advances in Neural Information Processing Systems 37 / Jan 01, 2024

Eickhoff, C., Merullo, J., & Pavlick, E. (2024). Talking Heads: Understanding Inter-Layer Communication in Transformer Language Models. Advances in Neural Information Processing Systems 37, 61372–61418. https://doi.org/10.52202/079017-1962

Lecture Notes in Computer Science / Jan 01, 2018

Ionescu, B., Müller, H., Villegas, M., García Seco de Herrera, A., Eickhoff, C., Andrearczyk, V., Dicente Cid, Y., Liauchuk, V., Kovalev, V., Hasan, S. A., Ling, Y., Farri, O., Liu, J., Lungren, M., Dang-Nguyen, D.-T., Piras, L., Riegler, M., Zhou, L., Lux, M., & Gurrin, C. (2018). Overview of ImageCLEF 2018: Challenges, Datasets and Evaluation. In Experimental IR Meets Multilinguality, Multimodality, and Interaction (pp. 309–334). Springer International Publishing. https://doi.org/10.1007/978-3-319-98932-7_28

Radiology / Dec 01, 2020

Stib, M. T., Vasquez, J., Dong, M. P., Kim, Y. H., Subzwari, S. S., Triedman, H. J., Wang, A., Wang, H.-L. C., Yao, A. D., Jayaraman, M., Boxerman, J. L., Eickhoff, C., Cetintemel, U., Baird, G. L., & McTaggart, R. A. (2020). Detecting Large Vessel Occlusion at Multiphase CT Angiography by Using a Deep Convolutional Neural Network. Radiology, 297(3), 640–649. https://doi.org/10.1148/radiol.2020200334

Lecture Notes in Computer Science / Jan 01, 2017

Ionescu, B., Müller, H., Villegas, M., Arenas, H., Boato, G., Dang-Nguyen, D.-T., Dicente Cid, Y., Eickhoff, C., Seco de Herrera, A. G., Gurrin, C., Islam, B., Kovalev, V., Liauchuk, V., Mothe, J., Piras, L., Riegler, M., & Schwall, I. (2017). Overview of ImageCLEF 2017: Information Extraction from Images. In Experimental IR Meets Multilinguality, Multimodality, and Interaction (pp. 315–337). Springer International Publishing. https://doi.org/10.1007/978-3-319-65813-1_28

Briefings in Bioinformatics / Oct 30, 2020

Dai, Y., Guo, C., Guo, W., & Eickhoff, C. (2020). Drug–drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings. Briefings in Bioinformatics, 22(4). https://doi.org/10.1093/bib/bbaa256

Nature / Jun 29, 2016

Van Noorden, R. (2016). ArXiv preprint server plans multimillion-dollar overhaul. Nature, 534(7609), 602–602. https://doi.org/10.1038/534602a

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) / Jan 01, 2024

Merullo, J., Eickhoff, C., & Pavlick, E. (2024). Language Models Implement Simple Word2Vec-style Vector Arithmetic. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 5030–5047. https://doi.org/10.18653/v1/2024.naacl-long.281

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval / Jul 11, 2021

Rekabsaz, N., Lesota, O., Schedl, M., Brassey, J., & Eickhoff, C. (2021). TripClick: The Log Files of a Large Health Web Search Engine. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2507–2513. https://doi.org/10.1145/3404835.3463242

Lecture Notes in Computer Science / Jan 01, 2018

Vogels, T., Ganea, O.-E., & Eickhoff, C. (2018). Web2Text: Deep Structured Boilerplate Removal. In Advances in Information Retrieval (pp. 167–179). Springer International Publishing. https://doi.org/10.1007/978-3-319-76941-7_13

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval / Aug 09, 2015

Eickhoff, C., Dungs, S., & Tran, V. (2015). An Eye-Tracking Study of Query Reformulation. Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 13–22. https://doi.org/10.1145/2766462.2767703

Proceedings of the sixth ACM international conference on Web search and data mining / Feb 04, 2013

Eickhoff, C., Collins-Thompson, K., Bennett, P. N., & Dumais, S. (2013). Personalizing atypical web search sessions. Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, 285–294. https://doi.org/10.1145/2433396.2433434

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval / Jul 06, 2022

Zerveas, G., Rekabsaz, N., Cohen, D., & Eickhoff, C. (2022). Mitigating Bias in Search Results Through Contextual Document Reranking and Neutrality Regularization. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2532–2538. https://doi.org/10.1145/3477495.3531891

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval / Jul 18, 2019

Hofstätter, S., Rekabsaz, N., Eickhoff, C., & Hanbury, A. (2019). On the Effect of Low-Frequency Terms on Neural-IR Models. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 1137–1140. https://doi.org/10.1145/3331184.3331344

European Radiology / Feb 19, 2022

Cheng, J., Sollee, J., Hsieh, C., Yue, H., Vandal, N., Shanahan, J., Choi, J. W., Tran, T. M. L., Halsey, K., Iheanacho, F., Warren, J., Ahmed, A., Eickhoff, C., Feldman, M., Mortani Barbosa, E., Kamel, I., Lin, C. T., Yi, T., Healey, T., … Wang, J. (2022). COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data. European Radiology, 32(7), 4446–4456. https://doi.org/10.1007/s00330-022-08588-8

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval / Jul 11, 2021

Cohen, D., Mitra, B., Lesota, O., Rekabsaz, N., & Eickhoff, C. (2021). Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 654–664. https://doi.org/10.1145/3404835.3462951

Proceedings of the fourth ACM international conference on Web search and data mining / Feb 09, 2011

Eickhoff, C., Serdyukov, P., & de Vries, A. P. (2011). A combined topical/non-topical approach to identifying web sites for children. Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, 505–514. https://doi.org/10.1145/1935826.1935900

Nature / Nov 07, 2025

Castelvecchi, D. (2025). Preprint site arXiv is banning computer-science reviews: here’s why. Nature. https://doi.org/10.1038/d41586-025-03664-7

Findings of the Association for Computational Linguistics: ACL 2022 / Jan 01, 2022

Rudman, W., Gillman, N., Rayne, T., & Eickhoff, C. (2022). IsoScore: Measuring the Uniformity of Embedding Space Utilization. Findings of the Association for Computational Linguistics: ACL 2022, 3325–3339. https://doi.org/10.18653/v1/2022.findings-acl.262

ACM Transactions on Information Systems / Mar 13, 2018

Yang, J., & Eickhoff, C. (2018). Unsupervised Learning of Parsimonious General-Purpose Embeddings for User and Location Modeling. ACM Transactions on Information Systems, 36(3), 1–33. https://doi.org/10.1145/3182165

npj Digital Medicine / Jan 14, 2022

Kim, C. K., Choi, J. W., Jiao, Z., Wang, D., Wu, J., Yi, T. Y., Halsey, K. C., Eweje, F., Tran, T. M. L., Liu, C., Wang, R., Sollee, J., Hsieh, C., Chang, K., Yang, F.-X., Singh, R., Ou, J.-L., Huang, R. Y., Feng, C., … Bai, H. X. (2022). An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data. Npj Digital Medicine, 5(1). https://doi.org/10.1038/s41746-021-00546-w

Proceedings of the 19th ACM international conference on Information and knowledge management / Oct 26, 2010

Eickhoff, C., Serdyukov, P., & de Vries, A. P. (2010). Web page classification on child suitability. Proceedings of the 19th ACM International Conference on Information and Knowledge Management, 1425–1428. https://doi.org/10.1145/1871437.1871638

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) / Dec 01, 2022

Potter, İ. Y., Zerveas, G., Eickhoff, C., & Duncan, D. (2022). Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEG. 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), 1304–1311. https://doi.org/10.1109/icmla55696.2022.00208

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics / Jan 01, 2023

Kumar, D., Lesota, O., Zerveas, G., Cohen, D., Eickhoff, C., Schedl, M., & Rekabsaz, N. (2023). Parameter-efficient Modularised Bias Mitigation via AdapterFusion. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.eacl-main.201

Findings of the Association for Computational Linguistics: ACL 2022 / Jan 01, 2022

Bahrainian, S. A., Feucht, S., & Eickhoff, C. (2022). NEWTS: A Corpus for News Topic-Focused Summarization. Findings of the Association for Computational Linguistics: ACL 2022. https://doi.org/10.18653/v1/2022.findings-acl.42

Proceedings of the 4th Information Interaction in Context Symposium / Aug 21, 2012

Eickhoff, C., Dekker, P., & de Vries, A. P. (2012). Supporting children’s web search in school environments. Proceedings of the 4th Information Interaction in Context Symposium, 129–137. https://doi.org/10.1145/2362724.2362748

Information Retrieval Journal / Sep 09, 2017

Eickhoff, C., Gwizdka, J., Hauff, C., & He, J. (2017). Introduction to the special issue on search as learning. Information Retrieval Journal, 20(5), 399–402. https://doi.org/10.1007/s10791-017-9315-9

Proceedings of the First International Workshop on Gamification for Information Retrieval / Apr 13, 2014

Eickhoff, C. (2014). Crowd-powered experts: helping surgeons interpret breast cancer images. Proceedings of the First International Workshop on Gamification for Information Retrieval, 53–56. https://doi.org/10.1145/2594776.2594788

Journal of Ultrasound in Medicine / Oct 10, 2020

Blaivas, M., Blaivas, L., Philips, G., Merchant, R., Levy, M., Abbasi, A., Eickhoff, C., Shapiro, N., & Corl, K. (2020). Development of a Deep Learning Network to Classify Inferior Vena Cava Collapse to Predict Fluid Responsiveness. Journal of Ultrasound in Medicine, 40(8), 1495–1504. Portico. https://doi.org/10.1002/jum.15527

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) / Jan 01, 2020

Webson, A., Chen, Z., Eickhoff, C., & Pavlick, E. (2020). Do “Undocumented Workers” == “Illegal Aliens”? Differentiating Denotation and Connotation in Vector Spaces. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 4090–4105. https://doi.org/10.18653/v1/2020.emnlp-main.335

Bioinformatics / Jul 18, 2018

Mustafa, H., Schilken, I., Karasikov, M., Eickhoff, C., Rätsch, G., & Kahles, A. (2018). Dynamic compression schemes for graph coloring. Bioinformatics, 35(3), 407–414. https://doi.org/10.1093/bioinformatics/bty632

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval / Aug 09, 2015

Eickhoff, C., de Vries, A. P., & Hofmann, T. (2015). Modelling Term Dependence with Copulas. Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 783–786. https://doi.org/10.1145/2766462.2767831

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) / Jan 01, 2023

Blinova, S., Zhou, X., Jaggi, M., Eickhoff, C., & Bahrainian, S. A. (2023). SIMSUM: Document-level Text Simplification via Simultaneous Summarization. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 9927–9944. https://doi.org/10.18653/v1/2023.acl-long.552

Critical Care / Dec 01, 2020

Abbasi, A., Karasu, Y., Li, C., Sodha, N. R., Eickhoff, C., & Ventetuolo, C. E. (2020). Machine learning to predict hemorrhage and thrombosis during extracorporeal membrane oxygenation. Critical Care, 24(1). https://doi.org/10.1186/s13054-020-03403-6

Lecture Notes in Computer Science / Jan 01, 2019

Hofstätter, S., Rekabsaz, N., Lupu, M., Eickhoff, C., & Hanbury, A. (2019). Enriching Word Embeddings for Patent Retrieval with Global Context. In Advances in Information Retrieval (pp. 810–818). Springer International Publishing. https://doi.org/10.1007/978-3-030-15712-8_57

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval / Jul 11, 2021

Lesota, O., Rekabsaz, N., Cohen, D., Grasserbauer, K. A., Eickhoff, C., & Schedl, M. (2021). A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models. Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval, 185–195. https://doi.org/10.1145/3471158.3472229

Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval / Aug 07, 2017

Jagerman, R., Eickhoff, C., & de Rijke, M. (2017). Computing Web-scale Topic Models using an Asynchronous Parameter Server. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1337–1340. https://doi.org/10.1145/3077136.3084135

Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval / Jul 28, 2013

Eickhoff, C., de Vries, A. P., & Collins-Thompson, K. (2013). Copulas for information retrieval. Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, 663–672. https://doi.org/10.1145/2484028.2484066

Scientific Reports / Dec 18, 2023

Singh, N., Eickhoff, C., Garcia-Agundez, A., Bertone, P., Paudel, S. S., Tambe, D. T., Litzky, L. A., Cox-Flaherty, K., Klinger, J. R., Monaghan, S. F., Mullin, C. J., Pereira, M., Walsh, T., Whittenhall, M., Stevens, T., Harrington, E. O., & Ventetuolo, C. E. (2023). Transcriptional profiles of pulmonary artery endothelial cells in pulmonary hypertension. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-48077-6

ACM Transactions on Information Systems / Oct 25, 2021

Bahrainian, S. A., Zerveas, G., Crestani, F., & Eickhoff, C. (2021). CATS: Customizable Abstractive Topic-based Summarization. ACM Transactions on Information Systems, 40(1), 1–24. https://doi.org/10.1145/3464299

Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval / Jul 07, 2016

Han Veiga, M., & Eickhoff, C. (2016). A Cross-Platform Collection of Social Network Profiles. Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, 665–668. https://doi.org/10.1145/2911451.2914666

Lecture Notes in Computer Science / Jan 01, 2013

Eickhoff, C., Li, W., & de Vries, A. P. (2013). Exploiting User Comments for Audio-Visual Content Indexing and Retrieval. In Advances in Information Retrieval (pp. 38–49). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_4

Watching YouTube / Dec 31, 2010

YouTube Videos. (2010). In Watching YouTube (pp. 219–222). University of Toronto Press. https://doi.org/10.3138/9781442687035-013

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval / Jul 25, 2020

Ramos, J., & Eickhoff, C. (2020). Search Result Explanations Improve Efficiency and Trust. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 1597–1600. https://doi.org/10.1145/3397271.3401279

Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval / Aug 12, 2012

Li, W., Eickhoff, C., & de Vries, A. P. (2012). Want a coffee?: predicting users’ trails. Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1171–1172. https://doi.org/10.1145/2348283.2348524

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing / Jan 01, 2022

Zerveas, G., Rekabsaz, N., Cohen, D., & Eickhoff, C. (2022). CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 10626–10644. https://doi.org/10.18653/v1/2022.emnlp-main.727

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management / Oct 17, 2015

Davtyan, M., Eickhoff, C., & Hofmann, T. (2015). Exploiting Document Content for Efficient Aggregation of Crowdsourcing Votes. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 783–790. https://doi.org/10.1145/2806416.2806460

Lecture Notes in Computer Science / Jan 01, 2014

Li, W., Eickhoff, C., & de Vries, A. P. (2014). Geo-spatial Domain Expertise in Microblogs. In Advances in Information Retrieval (pp. 487–492). Springer International Publishing. https://doi.org/10.1007/978-3-319-06028-6_46

Lecture Notes in Computer Science / Jan 01, 2018

Galkó, F., & Eickhoff, C. (2018). Biomedical Question Answering via Weighted Neural Network Passage Retrieval. In Advances in Information Retrieval (pp. 523–528). Springer International Publishing. https://doi.org/10.1007/978-3-319-76941-7_39

2021 International Conference on Culture-oriented Science & Technology (ICCST) / Nov 01, 2021

Li, Y., Hou, W., & Bai, B. (2021). Named Entity Recognition of traditional architectural text based on BERT. 2021 International Conference on Culture-Oriented Science & Technology (ICCST), 181–186. https://doi.org/10.1109/iccst53801.2021.00047

Lecture Notes in Computer Science / Jan 01, 2020

Experimental IR Meets Multilinguality, Multimodality, and Interaction: 11th International Conference of the CLEF Association, CLEF 2020, Thessaloniki, Greece, September 22–25, 2020, Proceedings. (2020). In A. Arampatzis, E. Kanoulas, T. Tsikrika, S. Vrochidis, H. Joho, C. Lioma, C. Eickhoff, A. Névéol, L. Cappellato, & N. Ferro (Eds.), Lecture Notes in Computer Science. Springer International Publishing. https://doi.org/10.1007/978-3-030-58219-7

Swiss Medical Informatics / Oct 21, 2016

Kuhn, L., & Eickhoff, C. (2016). Implicit Negative Feedback in Clinical Information Retrieval. Swiss Medical Informatics. https://doi.org/10.4414/smi.32.00355

Lecture Notes in Computer Science / Jan 01, 2016

Li, W., Eickhoff, C., & de Vries, A. P. (2016). Probabilistic Local Expert Retrieval. In Advances in Information Retrieval (pp. 227–239). Springer International Publishing. https://doi.org/10.1007/978-3-319-30671-1_17

Lecture Notes in Computer Science / Jan 01, 2011

Eickhoff, C., Polajnar, T., Gyllstrom, K., Torres, S. D., & Glassey, R. (2011). Web Search Query Assistance Functionality for Young Audiences. In Advances in Information Retrieval (pp. 776–779). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-20161-5_92

Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) / Jan 01, 2025

Golovanevsky, M., Rudman, W., Palit, V., Eickhoff, C., & Singh, R. (2025). What Do VLMs NOTICE? A Mechanistic Interpretability Pipeline for Gaussian-Noise-free Text-Image Corruption and Evaluation. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 11462–11482. https://doi.org/10.18653/v1/2025.naacl-long.571

The Information Retrieval Series / Jan 01, 2019

Larsen, B. (2019). The Scholarly Impact of CLEF 2010–2017: A Google Scholar Analysis of CLEF Proceedings and Working Notes. In Information Retrieval Evaluation in a Changing World (pp. 547–554). Springer International Publishing. https://doi.org/10.1007/978-3-030-22948-1_22

Proceedings of the ACM multimedia 2012 workshop on Crowdsourcing for multimedia / Oct 29, 2012

Vliegendhart, R., Dolstra, E., & Pouwelse, J. (2012). Crowdsourced user interface testing for multimedia applications. Proceedings of the ACM Multimedia 2012 Workshop on Crowdsourcing for Multimedia, 21–22. https://doi.org/10.1145/2390803.2390813

Frontiers in Aging Neuroscience / Oct 25, 2021

Garcia-Agundez, A., & Eickhoff, C. (2021). Towards Objective Quantification of Hand Tremors and Bradykinesia Using Contactless Sensors: A Systematic Review. Frontiers in Aging Neuroscience, 13. https://doi.org/10.3389/fnagi.2021.716102

Proceedings of the 4th Information Interaction in Context Symposium / Aug 21, 2012

Eickhoff, C., Azzopardi, L., Hiemstra, D., de Jong, F., de Vries, A., Dowie, D., Duarte, S., Glassey, R., Gyllstrom, K., Kruisinga, F., Marshall, K., Moens, S., Polajnar, T., & van der Sluis, F. (2012). EmSe: initial evaluation of a child-friendly medical search system. Proceedings of the 4th Information Interaction in Context Symposium, 282–285. https://doi.org/10.1145/2362724.2362775

Findings of the Association for Computational Linguistics: EMNLP 2021 / Jan 01, 2021

Bahrainian, S. A., Jaggi, M., & Eickhoff, C. (2021). Self-Supervised Neural Topic Modeling. Findings of the Association for Computational Linguistics: EMNLP 2021, 3341–3350. https://doi.org/10.18653/v1/2021.findings-emnlp.284

Jan 01, 2019

Zerveas, G., Zhang, R., Kim, L., & Eickhoff, C. (2019). Brown University at TREC Deep Learning 2019. National Institute of Standards and Technology (NIST). https://doi.org/10.6028/nist.sp.1250.deep-brown

Sep 17, 2018

Pagnini, G. (2018). Model sensitivity analysis on arxiv [Review of Model sensitivity analysis on arxiv]. Copernicus GmbH. https://doi.org/10.5194/gmd-2018-33-ac4

European Heart Journal / Oct 21, 2019

Meyer, A., Cypko, M. A., Eickhoff, C., Falk, V., & Emmert, M. Y. (2019). Artificial intelligence-assisted care in medicine: a revolution or yet another blunt weapon? European Heart Journal, 40(40), 3286–3289. https://doi.org/10.1093/eurheartj/ehz701

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management / Oct 24, 2016

Bansal, P., Eickhoff, C., & Hofmann, T. (2016). Active Content-Based Crowdsourcing Task Selection. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 529–538. https://doi.org/10.1145/2983323.2983716

Jun 01, 2023

Pal, K., Bahrainian, S. A., Mercurio, L., & Eickhoff, C. (2023). Neural Summarization of Electronic Health Records (Preprint). https://doi.org/10.2196/preprints.49544

Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval / Jul 07, 2016

Gwizdka, J., Hansen, P., Hauff, C., He, J., & Kando, N. (2016). Search as Learning (SAL) Workshop 2016. Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1249–1250. https://doi.org/10.1145/2911451.2917766

Proceedings of the 21st ACM international conference on Information and knowledge management / Oct 29, 2012

Gyllstrom, K., Eickhoff, C., de Vries, A. P., & Moens, M.-F. (2012). The downside of markup: examining the harmful effects of CSS and javascript on indexing today’s web. Proceedings of the 21st ACM International Conference on Information and Knowledge Management, 1990–1994. https://doi.org/10.1145/2396761.2398558

Journal of Nutrition Education and Behavior / Sep 01, 2025

Vadiveloo, M. K., Tovar, A., Elenio, E. G., Eickhoff, C., Soucie, J. S., Ewing, S. F., Gans, K. M., & Thorndike, A. N. (2025). Exploring Facilitators and Barriers for Personalized Dietary Incentives Among Online Shoppers at Cardiovascular Risk and Key Informants to Inform an Automated Shopping Platform. Journal of Nutrition Education and Behavior. https://doi.org/10.1016/j.jneb.2025.07.010

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 / Aug 03, 2025

Abdullahi, T., Gemou, I., Nayak, N. V., Murtaza, G., Bach, S. H., Eickhoff, C., & Singh, R. (2025). K-Paths: Reasoning over Graph Paths for Drug Repurposing and Drug Interaction Prediction. Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2, 5–16. https://doi.org/10.1145/3711896.3737011

Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval / Jul 13, 2025

Polyakov, G., Chen, C., & Eickhoff, C. (2025). Towards Best Practices of Axiomatic Activation Patching in Information Retrieval. Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2972–2976. https://doi.org/10.1145/3726302.3730256

Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval / Jul 13, 2025

Heuss, M., Chen, C., Anand, A., Eickhoff, C., & Verberne, S. (2025). Workshop on Explainability in Information Retrieval. Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, 4164–4167. https://doi.org/10.1145/3726302.3730361

Journal of Medical Internet Research / Mar 05, 2025

Berman, E., Sundberg Malek, H., Bitzer, M., Malek, N., & Eickhoff, C. (2025). Retrieval Augmented Therapy Suggestion for Molecular Tumor Boards: Algorithmic Development and Validation Study. Journal of Medical Internet Research, 27, e64364. https://doi.org/10.2196/64364

Journal of Medical Internet Research / Jan 21, 2025

Eisinger, F., Holderried, F., Mahling, M., Stegemann–Philipps, C., Herrmann–Werner, A., Nazarenus, E., Sonanini, A., Guthoff, M., Eickhoff, C., & Holderried, M. (2025). What’s Going On With Me and How Can I Better Manage My Health? The Potential of GPT-4 to Transform Discharge Letters Into Patient-Centered Letters to Enhance Patient Safety: Prospective, Exploratory Study. Journal of Medical Internet Research, 27, e67143. https://doi.org/10.2196/67143

Lecture Notes in Computer Science / Jan 01, 2025

Parry, A., Chen, C., Eickhoff, C., & MacAvaney, S. (2025). MechIR: A Mechanistic Interpretability Framework for Information Retrieval. In Advances in Information Retrieval (pp. 89–95). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-88720-8_16

Oct 04, 2024

Eisinger, F., Holderried, F., Mahling, M., Stegemann–Philipps, C., Herrmann–Werner, A., Nazarenus, E., Sonanini, A., Guthoff, M., Eickhoff, C., & Holderried, M. (2024). What’s Going On With Me and How Can I Better Manage My Health? The Potential of GPT-4 to Transform Discharge Letters Into Patient-Centered Letters to Enhance Patient Safety: Prospective, Exploratory Study (Preprint). https://doi.org/10.2196/preprints.67143

JMIR Medical Education / Aug 16, 2024

Holderried, F., Stegemann-Philipps, C., Herrmann-Werner, A., Festl-Wietek, T., Holderried, M., Eickhoff, C., & Mahling, M. (2024). A Language Model–Powered Simulated Patient With Automated Feedback for History Taking: Prospective Study. JMIR Medical Education, 10, e59213. https://doi.org/10.2196/59213

Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval / Aug 02, 2024

Abdullahi, T., Singh, R., & Eickhoff, C. (2024). Retrieval Augmented Zero-Shot Text Classification. Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval, 195–203. https://doi.org/10.1145/3664190.3672514

Jul 16, 2024

Berman, E., Sundberg Malek, H., Bitzer, M., Malek, N., & Eickhoff, C. (2024). Retrieval Augmented Therapy Suggestion for Molecular Tumor Boards: Algorithmic Development and Validation Study (Preprint). https://doi.org/10.2196/preprints.64364

Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval / Jul 10, 2024

Chen, C., Merullo, J., & Eickhoff, C. (2024). Axiomatic Causal Interventions for Reverse Engineering Relevance Computation in Neural Retrieval Models. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1401–1410. https://doi.org/10.1145/3626772.3657841

Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval / Jul 10, 2024

Chen, C., & Eickhoff, C. (2024). Evaluating Search System Explainability with Psychometrics and Crowdsourcing. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1051–1061. https://doi.org/10.1145/3626772.3657796

JMIR Medical Informatics / Jun 19, 2024

Abdullahi, T., Mercurio, L., Singh, R., & Eickhoff, C. (2024). Retrieval-Based Diagnostic Decision Support: Mixed Methods Study. JMIR Medical Informatics, 12, e50209. https://doi.org/10.2196/50209

Apr 05, 2024

Holderried, F., Stegemann-Philipps, C., Herrmann-Werner, A., Festl-Wietek, T., Holderried, M., Eickhoff, C., & Mahling, M. (2024). A Language Model–Powered Simulated Patient With Automated Feedback for History Taking: Prospective Study (Preprint). https://doi.org/10.2196/preprints.59213

JMIR Medical Education / Feb 13, 2024

Abdullahi, T., Singh, R., & Eickhoff, C. (2024). Learning to Make Rare and Complex Diagnoses With Generative AI Assistance: Qualitative Study of Popular Large Language Models. JMIR Medical Education, 10, e51391. https://doi.org/10.2196/51391

Information Sciences / Feb 01, 2024

Dai, Y., Guo, W., & Eickhoff, C. (2024). Wasserstein adversarial learning based temporal knowledge graph embedding. Information Sciences, 659, 120061. https://doi.org/10.1016/j.ins.2023.120061

Jan 11, 2024

Kalra, A., Bachina, P., Shou, B. L., Hwang, J., Barshay, M., Kulkarni, S., Sears, I., Eickhoff, C., Bermudez, C. A., Brodie, D., Ventetuolo, C. E., Kim, B. S., Whitman, G. J. R., Abbasi, A., & Cho, S.-M. (2024). Predicting Acute Brain Injury in Venoarterial Extracorporeal Membrane Oxygenation Patients with Tree-Based Machine Learning: Analysis of the Extracorporeal Life Support Organization Registry. https://doi.org/10.21203/rs.3.rs-3848514/v1

Dec 22, 2023

Kalra, A., Bachina, P., Shou, B. L., Hwang, J., Barshay, M., Kulkarni, S., Sears, I., Eickhoff, C., Bermudez, C. A., Brodie, D., Ventetuolo, C. E., Whitman, G. J. R., Abbasi, A., & Cho, S.-M. (2023). Utilizing Machine Learning to Predict Neurological Injury in Venovenous Extracorporeal Membrane Oxygenation Patients: An Extracorporeal Life Support Organization Registry Analysis. https://doi.org/10.21203/rs.3.rs-3779429/v1

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management / Oct 21, 2023

Heuss, M., Cohen, D., Mansoury, M., Rijke, M. de, & Eickhoff, C. (2023). Predictive Uncertainty-based Bias Mitigation in Ranking. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 762–772. https://doi.org/10.1145/3583780.3615011

Jul 30, 2023

Abdullahi, T., Singh, R., & Eickhoff, C. (2023). Learning to Make Rare and Complex Diagnoses With Generative AI Assistance: Qualitative Study of Popular Large Language Models (Preprint). https://doi.org/10.2196/preprints.51391

Jun 25, 2023

Abdullahi, T., Mercurio, L., Singh, R., & Eickhoff, C. (2023). Retrieval-Based Diagnostic Decision Support: Mixed Methods Study (Preprint). https://doi.org/10.2196/preprints.50209

Medical Physics / Oct 26, 2021

Keshavamurthy, K. N., Eickhoff, C., & Juluru, K. (2021). Weakly supervised pneumonia localization in chest X‐rays using generative adversarial networks. Medical Physics, 48(11), 7154–7171. Portico. https://doi.org/10.1002/mp.15185

International Journal of Medical Informatics / Sep 01, 2019

Raiskin, Y., Eickhoff, C., & Beeler, P. E. (2019). Categorization of free-text drug orders using character-level recurrent neural networks. International Journal of Medical Informatics, 129, 20–28. https://doi.org/10.1016/j.ijmedinf.2019.05.020

Dec 26, 2017

Mustafa, H., Schilken, I., Karasikov, M., Eickhoff, C., Rätsch, G., & Kahles, A. (2017). Dynamic compression schemes for graph coloring. https://doi.org/10.1101/239806

Acta Informatica Medica / Jan 01, 2025

Gummadi, R., Pindiprolu, S., & Kumar, C. (2025). The Accuracy And Clinical Relevance of Chat GPT-4 in Triple Negative Breast Cancer Research. Acta Informatica Medica, 33(3), 220. https://doi.org/10.5455/aim.2025.33.220-224

ACM SIGIR Forum / Jun 01, 2025

Trippas, J. R., Culpepper, J. S., Aliannejadi, M., Allan, J., Amigó, E., Arguello, J., Azzopardi, L., Bailey, P., Callan, J., Capra, R., Craswell, N., Croft, B., Dalton, J., Demartini, G., Dietz, L., Dou, Z., Eickhoff, C., Ekstrand, M., Ferro, N., … Zuccon, G. (2025). Report from the 4th Strategic Workshop on Information Retrieval in Lorne (SWIRL 2025). ACM SIGIR Forum, 59(1), 1–68. https://doi.org/10.1145/3769733.3769739

Journal of Cheminformatics / Jul 21, 2025

Rottach, F., Schieferdecker, S., & Eickhoff, C. (2025). The topology of molecular representations and its influence on machine learning performance. Journal of Cheminformatics, 17(1). https://doi.org/10.1186/s13321-025-01045-w

Frontiers in Cardiovascular Medicine / Sep 20, 2024

Hinrichs, N., Meyer, A., Koehler, K., Kaas, T., Hiddemann, M., Spethmann, S., Balzer, F., Eickhoff, C., Falk, V., Hindricks, G., Dagres, N., & Koehler, F. (2024). Artificial intelligence based real-time prediction of imminent heart failure hospitalisation in patients undergoing non-invasive telemedicine. Frontiers in Cardiovascular Medicine, 11. https://doi.org/10.3389/fcvm.2024.1457995

Journal of Affective Disorders / Nov 01, 2024

Bozzay, M. L., Hughes, C. D., Eickhoff, C., Schatten, H., & Armey, M. F. (2024). Identifying momentary suicidal ideation using machine learning in patients at high-risk for suicide. Journal of Affective Disorders, 364, 57–64. https://doi.org/10.1016/j.jad.2024.08.038

Zeitschrift für Herz-,Thorax- und Gefäßchirurgie / Aug 27, 2024

Garcia-Agundez, A., & Eickhoff, C. (2024). Künstliche Intelligenz in der Medizin: Wo stehen wir heute, und was liegt vor uns? Zeitschrift Für Herz-,Thorax- Und Gefäßchirurgie. https://doi.org/10.1007/s00398-024-00664-z

PLOS Digital Health / Sep 12, 2024

Hinrichs, N., Roeschl, T., Lanmueller, P., Balzer, F., Eickhoff, C., O’Brien, B., Falk, V., & Meyer, A. (2024). Short-term vital parameter forecasting in the intensive care unit: A benchmark study leveraging data from patients after cardiothoracic surgery. PLOS Digital Health, 3(9), e0000598. https://doi.org/10.1371/journal.pdig.0000598

Substance Use & Addiction Journal / Nov 07, 2024

Mercurio, L., Garcia, A., Ruest, S., Duffy, S. J., & Eickhoff, C. (2024). One Third of Alcohol Use Disorder Diagnoses are Missed by ICD Coding. Substance Use & Addiction Journal, 46(2), 328–336. https://doi.org/10.1177/29767342241288112

European Radiology / May 22, 2024

Keshavamurthy, K. N., Eickhoff, C., & Ziv, E. (2024). Pre-operative lung ablation prediction using deep learning. European Radiology, 34(11), 7161–7172. https://doi.org/10.1007/s00330-024-10767-8

The Journal of Thoracic and Cardiovascular Surgery / Jan 01, 2025

Abbasi, A., Li, C., Dekle, M., Bermudez, C. A., Brodie, D., Sellke, F. W., Sodha, N. R., Ventetuolo, C. E., & Eickhoff, C. (2025). Interpretable machine learning-based predictive modeling of patient outcomes following cardiac surgery. The Journal of Thoracic and Cardiovascular Surgery, 169(1), 114-123.e28. https://doi.org/10.1016/j.jtcvs.2023.11.034

Frontiers in Pharmacology / Aug 29, 2023

Garcia-Agundez, A., Garcia-Martin, E., & Eickhoff, C. (2023). Editorial: The Potential of Machine-learning in Pharmacogenetics, Pharmacogenomics and Pharmacoepidemiology: Volume II. Frontiers in Pharmacology, 14. https://doi.org/10.3389/fphar.2023.1277561

Oct 24, 2022

Eickhoff, S., Garcia-Agundez, A., Haidar, D., Zaidat, B., Adjei-Mosi, M., Li, P., & Eickhoff, C. (2022). AI-Controlled Closed-Loop Electrical Stimulation Implants: A Feasibility Study. https://doi.org/10.21203/rs.3.rs-2163679/v1

Frontiers in Neurology / Jun 09, 2023

Ahmed, A., Garcia-Agundez, A., Petrovic, I., Radaei, F., Fife, J., Zhou, J., Karas, H., Moody, S., Drake, J., Jones, R. N., Eickhoff, C., & Reznik, M. E. (2023). Delirium detection using wearable sensors and machine learning in patients with intracerebral hemorrhage. Frontiers in Neurology, 14. https://doi.org/10.3389/fneur.2023.1135472

Frontiers in Pharmacology / Feb 10, 2023

Meyer, C., Adkins, D., Pal, K., Galici, R., Garcia-Agundez, A., & Eickhoff, C. (2023). Neural text generation in regulatory medical writing. Frontiers in Pharmacology, 14. https://doi.org/10.3389/fphar.2023.1086913

Pediatric Emergency Care / Jan 17, 2023

Mercurio, L., Pou, S., Duffy, S., & Eickhoff, C. (2023). Risk Factors for Pediatric Sepsis in the Emergency Department: A Machine Learning Pilot Study. Pediatric Emergency Care, 39(2), e48–e56. https://doi.org/10.1097/pec.0000000000002893

Frontiers in Pharmacology / May 20, 2022

Garcia-Agundez, A., García-Martín, E., & Eickhoff, C. (2022). Editorial: The Potential of Machine Learning in Pharmacogenetics, Pharmacogenomics and Pharmacoepidemiology. Frontiers in Pharmacology, 13. https://doi.org/10.3389/fphar.2022.928527

European Radiology / Mar 23, 2022

Cheng, J., Sollee, J., Hsieh, C., Yue, H., Vandal, N., Shanahan, J., Choi, J. W., Tran, T. M. L., Halsey, K., Iheanacho, F., Warren, J., Ahmed, A., Eickhoff, C., Feldman, M., Barbosa, E. M., Kamel, I., Lin, C. T., Yi, T., Healey, T., … Wang, J. (2022). Correction to: COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data. European Radiology, 32(7), 5034–5034. https://doi.org/10.1007/s00330-022-08680-z

The Oxford Handbook of Virtuality / Dec 16, 2013

Carlisle, P. (2013). On the Role of “Digital Actors” in Entertainment-Based Virtual Worlds. In The Oxford Handbook of Virtuality (pp. 511–525). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199826162.013.040

Ghosh, S. (n.d.). Machine learning and deep learning-based approaches in epilepsy [University of Queensland Library]. https://doi.org/10.14264/e30aba1

Findings of the Association for Computational Linguistics: EMNLP 2025 / Jan 01, 2025

Wang, M., & Spitz, A. (2025). Quantifying the Risks of LLM- and Tool-assisted Rephrasing to Linguistic Diversity. Findings of the Association for Computational Linguistics: EMNLP 2025, 22561–22574. https://doi.org/10.18653/v1/2025.findings-emnlp.1228

Sep 10, 2025

Cheng, Z., Wohnig, S., Gupta, R., Alam, S., Abdullahi, T., Alves, J., Nielsen-Garcia, C., Mir, S., Li, S., Orender, J., Bahrainian, S. A., Kirste, D., Gokaslan, A., Glinka, M., Eickhoff, C., Viswanath, P., & Wolff, R. (2025). Position: Benchmarking is Broken - Don’t Let AI be its Own Judge. https://doi.org/10.36227/techrxiv.175752188.89738992/v1

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing / Jan 01, 2025

Golovanevsky, M., Rudman, W., Lepori, M. A., Bar, A., Singh, R., & Eickhoff, C. (2025). Pixels Versus Priors: Controlling Knowledge Priors in Vision-Language Models through Visual Counterfacts. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 24848–24863. https://doi.org/10.18653/v1/2025.emnlp-main.1262

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing / Jan 01, 2025

Lu, M., Chen, C., & Eickhoff, C. (2025). Pathway to Relevance: How Cross-Encoders Implement a Semantic Variant of BM25. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 25536–25558. https://doi.org/10.18653/v1/2025.emnlp-main.1297

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing / Jan 01, 2025

Polyakov, G., Hepting, C., Eickhoff, C., & Bahrainian, S. A. (2025). Interpretability Analysis of Arithmetic In-Context Learning in Large Language Models. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 1758–1777. https://doi.org/10.18653/v1/2025.emnlp-main.92

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing / Jan 01, 2025

Lu, M., Zhang, R., Eickhoff, C., & Pavlick, E. (2025). Paths Not Taken: Understanding and Mending the Multilingual Factual Recall Pipeline. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 15077–15107. https://doi.org/10.18653/v1/2025.emnlp-main.762

Findings of the Association for Computational Linguistics: EMNLP 2025 / Jan 01, 2025

Esfandiarpoor, R., Zerveas, G., Zhang, R., Mgonzo, M., Eickhoff, C., & Bach, S. (2025). Beyond Contrastive Learning: Synthetic Data Enables List-wise Training with Multiple Levels of Relevance. Findings of the Association for Computational Linguistics: EMNLP 2025, 22860–22882. https://doi.org/10.18653/v1/2025.findings-emnlp.1245

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing / Jan 01, 2024

Forde, J. Z., Zhang, R., Sutawika, L., Aji, A. F., Cahyawijaya, S., Winata, G. I., Wu, M., Eickhoff, C., Biderman, S., & Pavlick, E. (2024). Re-Evaluating Evaluation for Multilingual Summarization. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 19476–19493. https://doi.org/10.18653/v1/2024.emnlp-main.1085

Findings of the Association for Computational Linguistics: ACL 2025 / Jan 01, 2025

Rudman, W., Golovanevsky, M., Bar, A., Palit, V., LeCun, Y., Eickhoff, C., & Singh, R. (2025). Forgotten Polygons: Multimodal Large Language Models are Shape-Blind. Findings of the Association for Computational Linguistics: ACL 2025, 11983–11998. https://doi.org/10.18653/v1/2025.findings-acl.620

Findings of the Association for Computational Linguistics: NAACL 2025 / Jan 01, 2025

Li, J., Hu, X., Yin, X., & Wan, X. (2025). Evaluating Self-Generated Documents for Enhancing Retrieval-Augmented Generation with Large Language Models. Findings of the Association for Computational Linguistics: NAACL 2025, 2741–2775. https://doi.org/10.18653/v1/2025.findings-naacl.149

Biocomputing 2025 / Nov 21, 2024

Golovanevsky, M., Schiller, E., Nair, A., Han, E., Singh, R., & Eickhoff, C. (2024). One-Versus-Others Attention: Scalable Multimodal Integration for Biomedical Data. Biocomputing 2025, 580–593. https://doi.org/10.1142/9789819807024_0041

Findings of the Association for Computational Linguistics ACL 2024 / Jan 01, 2024

Bahrainian, S. A., Dou, J., & Eickhoff, C. (2024). Text Simplification via Adaptive Teaching. Findings of the Association for Computational Linguistics ACL 2024, 6574–6584. https://doi.org/10.18653/v1/2024.findings-acl.392

2013 12th International Conference on Machine Learning and Applications / Dec 01, 2013

Buschermohle, A., & Brockmann, W. (2013). Stable On-Line Learning with Optimized Local Learning, But Minimal Change of the Global Output. 2013 12th International Conference on Machine Learning and Applications, 21–27. https://doi.org/10.1109/icmla.2013.100

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing / Jan 01, 2023

Zerveas, G., Rekabsaz, N., & Eickhoff, C. (2023). Enhancing the Ranking Context of Dense Retrieval through Reciprocal Nearest Neighbors. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 10779–10803. https://doi.org/10.18653/v1/2023.emnlp-main.665

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing / Jan 01, 2023

Rudman, W., Chen, C., & Eickhoff, C. (2023). Outlier Dimensions Encode Task Specific Knowledge. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 14596–14605. https://doi.org/10.18653/v1/2023.emnlp-main.901

Proceedings of the 11th Joint Conference on Lexical and Computational Semantics / Jan 01, 2022

Merullo, J., Ebert, D., Eickhoff, C., & Pavlick, E. (2022). Pretraining on Interactions for Learning Grounded Affordance Representations. Proceedings of the 11th Joint Conference on Lexical and Computational Semantics, 258–277. https://doi.org/10.18653/v1/2022.starsem-1.23

Apr 01, 2008

Detmer, D. (2008). American Medical Informatics Association (AMIA) 2007 Annual Symposium. Defense Technical Information Center. https://doi.org/10.21236/ada480011

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval / Jul 06, 2022

Cohen, D., Du, K., Mitra, B., Mercurio, L., Rekabsaz, N., & Eickhoff, C. (2022). Inconsistent Ranking Assumptions in Medical Search and Their Downstream Consequences. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2572–2577. https://doi.org/10.1145/3477495.3531898

Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023) / Jan 01, 2023

Hewett, F. (2023). APA-RST: A Text Simplification Corpus with RST Annotations. Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023), 173–179. https://doi.org/10.18653/v1/2023.codi-1.23

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies / Jan 01, 2021

Zhang, R., & Eickhoff, C. (2021). SOCCER: An Information-Sparse Discourse State Tracking Collection in the Sports Commentary Domain. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4325–4333. https://doi.org/10.18653/v1/2021.naacl-main.342

ACM SIGHIT Record / Mar 01, 2011

Lai, A. M. (2011). 34th Annual Symposium on Biomedical and Health Informatics (AMIA 2010) conference report. ACM SIGHIT Record, 1(1), 33–37. https://doi.org/10.1145/1971706.1971717

Studies in Health Technology and Informatics / Jan 01, 2019

Restrepo Maria I., McGrath Mary C., Sarkar Indra Neil, & Chen Elizabeth S. (2019). Web-Based Visualization of MeSH-Based PubMed/MEDLINE Statistics. In MEDINFO 2019: Health and Wellbeing e-Networks for All. IOS Press. https://doi.org/10.3233/shti190499

Biomedical Safety & Standards / Mar 15, 2024

Baxter Amia Automated Peritoneal Dialysis Cycler Set. (2024). Biomedical Safety & Standards, 54(5), 27–28. https://doi.org/10.1097/01.bmsas.0001007808.61120.71