Qi, W. ., & Chelmis, C. . (2023). Online Hierarchical Multi-label Classification. In (p. 10). IEEE International Conference on Big Data. http://doi.org/10.1109/BigData59044.2023.10386110
Wenting Qi
First name
Wenting
Last name
Qi
Qi, W. ., & Chelmis, C. . (2023). Hybrid Loss for Hierarchical Multi–label Classification Network. In (p. 10). IEEE International Conference on Big Data. http://doi.org/10.1109/BigData59044.2023.10386341
Qi, W. ., & Chelmis, C. . (2023). Evaluating algorithmic homeless service allocation. Journal of Computational Social Science, 6(1), 31. http://doi.org/10.1007/s42001-022-00190-8
Qi, W. ., & Chelmis, C. . (2023). Noisy Label Detection and Counterfactual Correction. IEEE Transactions on Artificial Intelligence, 13. http://doi.org/10.1109/TAI.2023.3271963
Qi, W. ., & Chelmis, C. . (2022). Label Denoising and Counterfactual Explanation with A Plug and Play Framework. In IEEE International Conference on Big Data (p. 6). http://doi.org/10.1109/BigData55660.2022.10020488
Qi, W. ., & Chelmis, C. . (2022). Robust Learning with Noisy Label Detection and Counterfactual Correction. In IEEE International Conference on Big Data (p. 6). http://doi.org/10.1109/BigData55660.2022.10020228
Qi, W. ., & Chelmis, C. . (2021). Improving Algorithmic Decision–Making in the Presence of Untrustworthy Training Data. In 2021 IEEE International Conference on Big Data (Big Data) (p. 7). http://doi.org/10.1109/BigData52589.2021.9671677
Chelmis, C. ., & Qi, W. . (2021). Hierarchical MultiClass AdaBoost. In 2021 IEEE International Conference on Big Data (Big Data) (p. 8). http://doi.org/10.1109/BigData52589.2021.9671291
Chelmis, C. ., Qi, W. ., & Lee, W. . (2021). Challenges and Opportunities in Using Data Science for Homelessness Service Provision. In WWW ’21: Companion Proceedings of the Web Conference 2021 (p. 8). http://doi.org/10.1145/3442442.3453454
Chelmis, C. ., Qi, W. ., Lee, W. ., & Duncan, S. . (2021). Smart Homelessness Service Provision with Machine Learning. Procedia Computer Science, 185(C), 10. http://doi.org/10.1016/j.procs.2021.05.002