Publications
- Shi E., Ding, L., Kong, L. and Jiang, B., 2024. Debiasing with Sufficient Projection: A General Theoretical Framework for Vector Representations. NAACL, 2024. Accepted.
- Jiang, Y., Chang, X., Liu, Y., Ding, L., Kong, L. and Jiang, B., 2023. Gaussian Differential Privacy on Riemannian Manifolds. Advances in Neural Information Processing Systems, 2023. Accepted.
- Liu, Y., Hu, Q., Ding, L., & Kong, L. (2023, July). Online Local Differential Private Quantile Inference via Self-normalization. In International Conference on Machine Learning (pp. 21698-21714). PMLR.
- Mosquera, L., El Emam, K., Ding, L., Sharma, V., Zhang, X. H., Kababji, S. E., … & Eurich, D. T. (2023). A method for generating synthetic longitudinal health data. BMC Medical Research Methodology, 23(1), 1-21.
Liu, M., Ding, L., Yu, D., Liu, W., Kong, L., and Jiang B., (2022). Conformalized Fairness via Quantile Regression. Advances in Neural Information Processing Systems, 35, 11561-11572.
Hu, Y., Tarafdar, M., Al-Ani, J. A., Rets, I., Hu, S., Denier, D., Hughes, K. D., Konnikov, A., & Ding, L. (2022). Gendered STEM workforce in the United Kingdom: The role of gender bias in job advertising. BIAS project evidence submission to the ‘Diversity in STEM’ inquiry, Science and Technology Committee, House of Commons, UK Parliament.
Book Chapter
Konnikov, A., Rets, I., Hughes, K., Alshehabi Al-Ani, J., Denier, N., Ding, L., Hu, S., Hu, Y., Jiang, B., Kong, L. , Tarafdar, M. and Yu, D. (2022). Responsible AI for labour market equality (BIAS), In: How to Manage International Multidisciplinary Research Projects. Edward Elgar, Cheltenham.Ding, L., Yu, D., Xie, J., Guo, W., Hu, S., Liu, M., Kong, L., Dai, H., Bao, Y. and Jiang, B., 2022, June. Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 11, pp. 11864-11872).
Konnikov, A., Denier, N., Hu, Y., Hughes, K.D., Al-Ani, J.A., Ding, L., Rets, I. and Tarafdar, M., 2022. BIAS Word inventory for work and employment diversity,(in) equality and inclusivity (Version 1.0).
Hu, S., Al-Ani, J.A., Hughes, K.D., Denier, N., Konnikov, A., Ding, L., Xie, J., Hu, Y., Tarafdar, M., Jiang, B. and Kong, L., 2022. Balancing Gender Bias in Job Advertisements with Text-Level Bias Mitigation. Frontiers in Big Data.
Zhang, Z., Lan, Q., Ding, L., Wang, Y., Hassanpour, N. and Greiner, R., 2019. Reducing selection bias in counterfactual reasoning for individual treatment effects estimation. arXiv preprint arXiv:1912.09040.
- Ding, L., Li, Y., Wang, H. and Xu, K., 2020, July. Measurement and Analysis of Cloud User Interest: A Glance From BitTorrent. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 1300-1301). IEEE.
On-going papers:
- Ding, L., Jiang, B., Iravani, M. (2022). Evaluation of Machine Learning Methods to Predict Total Soil Carbon: A Case Study of Alberta.