Ozsu, T. . (2022). Reminiscences on Influential Papers. SIGMOD Record, 51, 44-46. https://doi.org/10.1145/3552490.3552499
Publications
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Hebert, L. ., Golab, L. ., & Cohen, R. . (2022). Predicting Hateful Discussions on Reddit Using Graph Transformer Networks And Communal Context. Predicting Hateful Discussions on Reddit Using Graph Transformer Networks And Communal Context. Presented at the. https://doi.org/10.1109/WI-IAT55865.2022.00012
Lin, S.-C. ., Li, M. ., & Lin, J. . (2022). Aggretriever: A Simple Approach to Aggregate Textual Representation For Robust Dense Passage Retrieval. ArXiv, abs/2208.00511. https://doi.org/10.48550/arXiv.2208.00511
Seltzer, J. ., Cheng, K. ., Zong, S. ., & Lin, J. . (2022). Flipping the Script: Inverse Information Seeking Dialogues for Market Research. Flipping the Script: Inverse Information Seeking Dialogues for Market Research. Presented at the. https://doi.org/10.1145/3477495.3536326
Li, M. ., Zhang, X. ., Xin, J. ., Zhang, H. ., & Lin, J. . (2022). Certified Error Control of Candidate Set Pruning for Two-Stage Relevance Ranking. Certified Error Control of Candidate Set Pruning for Two-Stage Relevance Ranking. Presented at the. Retrieved from https://aclanthology.org/2022.emnlp-main.23
Artikis, A. ., Tatbul, N. ., Golab, L. ., & Sadoghi, M. . (2022). Editorial. Information Systems, 109, 102088. https://doi.org/10.1016/j.is.2022.102088
Li, H. ., Wang, S. ., Zhuang, S. ., Mourad, A. ., Ma, X. ., Lin, J. ., & Zuccon, G. . (2022). To Interpolate or Not to Interpolate: PRF, Dense and Sparse Retrievers. ArXiv, abs/2205.00235. https://doi.org/10.48550/arXiv.2205.00235
Craswell, N. ., Mitra, B. ., Yilmaz, E. ., Campos, D. ., Lin, J. ., Voorhees, E. M., & Soboroff, I. . (2022). Overview of the TREC 2022 Deep Learning Track. Overview of the TREC 2022 Deep Learning Track. Presented at the. Retrieved from https://trec.nist.gov/pubs/trec31/papers/Overview_deep.pdf
Guo, R. ., Guo, V. ., Kim, A. ., Hildred, J. ., & Daudjee, K. . (2022). Hydrozoa: Dynamic Hybrid-Parallel DNN Training on Serverless Containers. Hydrozoa: Dynamic Hybrid-Parallel DNN Training on Serverless Containers. Presented at the. Retrieved from https://proceedings.mlsys.org/paper/2022/hash/ea5d2f1c4608232e07d3aa3d998e5135-Abstract.html
Arabzadeh, N. ., Seifikar, M. ., & Clarke, C. . (2022). Unsupervised Question Clarity Prediction Through Retrieved Item Coherency. Unsupervised Question Clarity Prediction Through Retrieved Item Coherency. Presented at the. https://doi.org/10.1145/3511808.3557719