Ma, X., Pradeep, R., Nogueira, R., & Lin, J. (2022). Document Expansion Baselines and Learned Sparse Lexical Representations For MS MARCO V1 and V2 Presented at the Expansion Baselines and Learned Sparse Lexical Representations For MS MARCO V1 and V2 conference. https://doi.org/10.1145/3477495.3531749
References
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2022
Zhang, X., Ogueji, K., Ma, X., & Lin, J. (2022). Towards Best Practices for Training Multilingual Dense Retrieval Models ArXiv, abs/2204.02363. https://doi.org/10.48550/arXiv.2204.02363
Lin, J. (2022). On the Interaction Between Differential Privacy and Gradient Compression In Deep Learning ArXiv, abs/2211.00734. https://doi.org/10.48550/arXiv.2211.00734
Kane, A., Ng, Y. K., & Tompa, F. (2022). Dowsing for Answers to Math Questions: Doing Better With Less Presented at the Dowsing for Answers to Math Questions: Doing Better With Less conference. Retrieved from http://ceur-ws.org/Vol-3180/paper-03.pdf
Dehghan, M., Kumar, D., & Golab, L. (2022). GRS: Combining Generation and Revision in Unsupervised Sentence Simplification Presented at the GRS: Combining Generation and Revision in Unsupervised Sentence Simplification conference. Retrieved from https://aclanthology.org/2022.findings-acl.77
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
Parsa, M. S., Shi, H., Xu, Y., Yim, A., Yin, Y., & Golab, L. (2022). Analyzing Climate Change Discussions on Reddit Presented at the Analyzing Climate Change Discussions on Reddit conference. https://doi.org/10.1109/CSCI58124.2022.00150
Ammar, K., Sahu, S., Salihoglu, S., & Ozsu, T. (2022). Optimizing Differentially-Maintained Recursive Queries on Dynamic Graphs Proceedings of the VLDB Endowment (PVLDB), 15, 3186-3198. Retrieved from https://www.vldb.org/pvldb/vol15/p3186-ammar.pdf
Ilyas, I., & Rekatsinas, T. (2022). Machine Learning and Data Cleaning: Which Serves the Other? Journal of Data and Information Quality, 14, 1-13. https://doi.org/10.1145/3506712
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