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Ma, X. ., Fun, H. ., Yin, X. ., Mallia, A. ., & Lin, J. . (2023). Enhancing Sparse Retrieval via Unsupervised Learning. Enhancing Sparse Retrieval via Unsupervised Learning. Presented at the. https://doi.org/10.1145/3624918.3625334

Mousavi, A. ., Zhan, X. ., Bai, H. ., Shi, P. ., Rekatsinas, T. ., Han, B. ., Li, Y. ., Pound, J. ., Susskind, J. M., Schluter, N. ., Ilyas, I. ., & Jaitly, N. . (2023). Construction of Paired Knowledge Graph-Text Datasets Informed by Cyclic Evaluation. ArXiv, abs/2309.11669. https://doi.org/10.48550/arXiv.2309.11669

Lin, J. ., Alfonso-Hermelo, D. ., Jeronymo, V. ., Kamalloo, E. ., Lassance, C. ., Nogueira, R. F., Ogundepo, O. ., Rezagholizadeh, M. ., Thakur, N. ., Yang, J.-H. ., & Zhang, X. . (2023). Simple Yet Effective Neural Ranking and Reranking Baselines for Cross-Lingual Information Retrieval. ArXiv, abs/2304.01019. https://doi.org/10.48550/arXiv.2304.01019

Sheshbolouki, A. ., & Ozsu, T. . (2023). sGrow: Explaining the Scale-Invariant Strength Assortativity of Streaming Butterflies. ACM Transactions on the Web, 17, 1-24. https://doi.org/10.1145/3572408

Lin, S.-C. ., Asai, A. ., Li, M. ., Oguz, B. ., Lin, J. ., Mehdad, Y. ., Yih, W.- tau ., & Chen, X. . (2023). How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval. ArXiv, abs/2302.07452. https://doi.org/10.48550/arXiv.2302.07452

Bauer, C. ., Carterette, B. ., Ferro, N. ., Fuhr, N. ., Beel, J. ., Breuer, T. ., Clarke, C. ., Crescenzi, A. ., Demartini, G. ., Di Nunzio, G. M., Dietz, L. ., Faggioli, G. ., Ferwerda, B. ., Fröbe, M. ., Hagen, M. ., Hanbury, A. ., Hauff, C. ., Jannach, D. ., Kando, N. ., Kanoulas, E. ., Knijnenburg, B. P., Kruschwitz, U. ., Li, M. ., Maistro, M. ., Michiels, L. ., Papenmeier, A. ., Potthast, M. ., Rosso, P. ., Said, A. ., Schaer, P. ., Seifert, C. ., Spina, D. ., Stein, B. ., Tintarev, N. ., Urbano, J. an, Wachsmuth, H. ., Willemsen, M. C., & Zobel, J. . (2023). Report on the Dagstuhl Seminar on Frontiers of Information Access Experimentation for Research and Education. SIGIR Forum, 57, 1-7. https://doi.org/10.1145/3636341.3636351

Ehrlinger, L. ., Harmouch, H. ., Ilyas, I. ., & Naumann, F. . (2023). Preface QDB. Paper Preface QDB. Presented at the. Retrieved from https://ceur-ws.org/Vol-3462/QDB0.pdf

Ozsu, T. . (2023). Data Science: A Systematic Treatment. ArXiv, abs/2301.13761. https://doi.org/10.48550/arXiv.2301.13761

Chen, H. ., Lassance, C. ., & Lin, J. . (2023). End-to-End Retrieval With Learned Dense and Sparse Representations Using Lucene. ArXiv, abs/2311.18503. https://doi.org/10.48550/ARXIV.2311.18503

Hebert, L. ., Golab, L. ., Poupart, P. ., & Cohen, R. . (2023). FedFormer: Contextual Federation With Attention in Reinforcement Learning. FedFormer: Contextual Federation With Attention in Reinforcement Learning. Presented at the. https://doi.org/10.5555/3545946.3598716