Jiang, Z., Yang, M. Y. R., Tsirlin, M., Tang, R., & Lin, J. (2022). Less Is More: Parameter-Free Text Classification With Gzip ArXiv, abs/2212.09410. https://doi.org/10.48550/arXiv.2212.09410
References
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Thakur, N., Reimers, N., & Lin, J. (2022). Domain Adaptation for Memory-Efficient Dense Retrieval ArXiv, abs/2205.11498. https://doi.org/10.48550/arXiv.2205.11498
Zhong, W., Xie, Y., & Lin, J. (2022). Applying Structural and Dense Semantic Matching for the ARQMath Lab 2022, Clef Presented at the Applying Structural and Dense Semantic Matching for the ARQMath Lab 2022, Clef conference. Retrieved from http://ceur-ws.org/Vol-3180/paper-09.pdf
Lin, J. (2022). Building a Culture of Reproducibility in Academic Research ArXiv, abs/2212.13534. https://doi.org/10.48550/arXiv.2212.13534
Mazmudar, M., Humphries, T., Liu, J., Rafuse, M., & He, X. (2022). Cache Me if You Can: Accuracy-Aware Inference Engine for Differentially Private Data Exploration Proceedings of the VLDB Endowment (PVLDB), 16, 574-586. Retrieved from https://www.vldb.org/pvldb/vol16/p574-mazmudar.pdf
Yan, X., Luo, C., Clarke, C., Craswell, N., Voorhees, E. M., & Castells, P. (2022). Human Preferences as Dueling Bandits ArXiv, abs/2204.10362. https://doi.org/10.48550/arXiv.2204.10362
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 Presented at the To Interpolate or Not to Interpolate: PRF, Dense and Sparse Retrievers conference. https://doi.org/10.1145/3477495.3531884
Li, Y., Zou, L., Ozsu, T., & Zhao, D. (2022). Space-Efficient Subgraph Search Over Streaming Graph With Timing Order Constraint IEEE Transactions on Knowledge and Data Engineering (TKDE), 34, 4453-4467. https://doi.org/10.1109/TKDE.2020.3035902
Nanayakkara, P., Bater, J., He, X., Hullman, J., & Rogers, J. (2022). Visualizing Privacy-Utility Trade-Offs in Differentially Private Data Releases ArXiv, abs/2201.05964. Retrieved from https://arxiv.org/abs/2201.05964
Shi, P., Zhang, R., Bai, H., & Lin, J. (2022). XRICL: Cross-Lingual Retrieval-Augmented in-Context Learning For Cross-Lingual Text-to-SQL Semantic Parsing Presented at the XRICL: Cross-Lingual Retrieval-Augmented In-Context Learning For Cross-Lingual Text-to-SQL Semantic Parsing conference. Retrieved from https://aclanthology.org/2022.findings-emnlp.384