Li, H. ., Zhuang, S. ., Mourad, A. ., Ma, X. ., Lin, J. ., & Zuccon, G. . (2021). Improving Query Representations for Dense Retrieval With Pseudo Relevance Feedback: A Reproducibility Study. ArXiv, abs/2112.06400. Retrieved from https://arxiv.org/abs/2112.06400
Publications
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Mackenzie, J. ., Trotman, A. ., & Lin, J. . (2021). Wacky Weights in Learned Sparse Representations and the Revenge Of Score-at-a-Time Query Evaluation. ArXiv, abs/2110.11540. Retrieved from https://arxiv.org/abs/2110.11540
Sheshbolouki, A. ., & Ozsu, T. . (2021). Scale-Invariant Strength Assortativity of Streaming Butterflies. ArXiv, abs/2111.12217. Retrieved from https://arxiv.org/abs/2111.12217
Li, M. ., Li, M. ., Xiong, K. ., & Lin, J. . (2021). Multi-Task Dense Retrieval via Model Uncertainty Fusion for Open-Domain Question Answering. Multi-Task Dense Retrieval via Model Uncertainty Fusion for Open-Domain Question Answering . Presented at the. Retrieved from https://aclanthology.org/2021.findings-emnlp.26
Xia, S. ., Chang, B. ., Knopf, K. ., He, Y. ., Tao, Y. ., & He, X. . (2021). DPGraph: A Benchmark Platform for Differentially Private Graph Analysis. DPGraph: A Benchmark Platform for Differentially Private Graph Analysis. Presented at the. https://doi.org/10.1145/3448016.3452756
Zhang, Y. ., Hu, C. ., Liu, Y. ., Fang, H. ., & Lin, J. . (2021). Learning to Rank in the Age of Muppets: Effectiveness-Efficiency Tradeoffs In Multi-Stage Ranking. Learning to Rank in the Age of Muppets: Effectiveness-Efficiency Tradeoffs In Multi-Stage Ranking. Presented at the. Retrieved from https://aclanthology.org/2021.sustainlp-1.8
Sheshbolouki, A. ., & Ozsu, T. . (2021). Scale-Invariant Strength Assortativity of Streaming Butterflies. ArXiv, abs/2111.12217. Retrieved from https://arxiv.org/abs/2111.12217
Lin, S.-C. ., & Lin, J. . (2021). Densifying Sparse Representations for Passage Retrieval by Representational Slicing. ArXiv, abs/2112.04666. Retrieved from https://arxiv.org/abs/2112.04666
Toman, D. ., & Wedell, G. . (2021). Projective Beth Definability and Craig Interpolation for Relational Query Optimization (Material to Accompany Invited Talk). Projective Beth Definability and Craig Interpolation for Relational Query Optimization (Material to Accompany Invited Talk). Presented at the. Retrieved from http://ceur-ws.org/Vol-3009/invited1.pdf
Zhang, X. ., Yates, A. ., & Lin, J. . (2020). A Little Bit Is Worse Than None: Ranking With Limited Training Data. A Little Bit Is Worse Than None: Ranking With Limited Training Data. Presented at the. https://doi.org/10.18653/v1/2020.sustainlp-1.14