Data-driven Distributionally robust optimization (DRO).
Optimal Transport Theory.
Robust Statistical Inference.
Simulation
Empirical Likelihood Theory.
Financial/Insurance Network Analysis.
Non-parametric Inference.
Bayesian Computation.
Stochastic Optimization.
Semi-Supervised Learning.
Metric Learning.
Breast Cancer related Lymphedema.
Land-Use Planning.
Please refer to my google scholar for those publications.
Blanchet, J., Kang, Y., Montiel Olea, J., Nguyen, V., & Zhang, X. (2023). Machine Learning's Dropout Training is Distributionally Robust Optimal. Journal of Machine Learning Research(JMLR) 24, no. 180 (2023): 1-60.*
Giri, S., Kang, Y., MacDonald, K., Tippett, M., Qiu, Z., Lathrop, R. G., & Obropta, C. C. (2023). Revealing the sources of arsenic in private well water using Random Forest Classification and Regression. Science of The Total Environment, 857, 159360.
Blanchet, J., & Kang, Y. (2021). Sample out-of-sample inference based on Wasserstein distance. Operations Research, 69(3), 985-1013. *
Xie, L., Qiu, Z., You, L., & Kang, Y. (2020) A Macro Perspective on the Relationship between Farm Size and Agrochemicals Use in China. Sustainability 2020, 12, 9299.
Blanchet, J., Kang, Y. & Murthy, K.(2019). Robust Wasserstein profile inference and applications to machine learning. Journal of Applied Probability 56 (3), 830-857 *
Blanchet, J., Kang, Y., Zhang, F., & Zhang, H. (2019) A Distributionally Robust Boosting Algorithm. Proceedings of the 2019 Winter Simulation Conference (WSC), 3728-3739 *
Blanchet, J., Kang, Y., Zhang, F., & Murthy, K. (2019) Data-driven Optimal Cost Selection for Distributionally Robust Optimization. Proceedings of the 2019 Winter Simulation Conference (WSC), 3740-3751 * (Best Theoretical Paper Award)
Blanchet, J., Kang, Y., Zhang, F., He, F., & Hu, Z. (2019) Doubly Robust Data-Driven Distributionally Robust Optimization. Proceedings of 18th Applied Stochastic Models and Data Analysis International Conference with Demographics Workshop. (ISBN: 978-618-5180-33-1) ,249-262 *
Qiu, Z., Kennen, J., Giri, S., Walter, T., Kang, Y., Zhang, Z. & Walter, M. (2019) Reassessing the Relationship between Landscape Alteration and Aquatic Ecosystem Degradation from a Hydrologically Sensitive Area Perspective. Science of The Total Environment, 2019
Blanchet, J., & Kang, Y. (2018). Semi-Supervised Learning Based on Distributionally Robust Optimization. Proceedings of 5th SMTDA International Conference with Demographics Workshop. 87-116*
Blanchet, J. & Kang, Y. (2017). Distributionally Robust Groupwise Shrinkage Estimators. Proceedings of Machine Learning Research 77:97112, 2017. *
Qiu, Z., Dosskey, M. G., & Kang, Y. (2016). Choosing Between Alternative Placement Strategies for Conservation Buffers Using Borda Count. Landscape and Urban Planning, 153, 66-73.
Qiu, Z., Dosskey, M. G., & Kang, Y. (2016). Data on Four Criteria for Targeting the Placement of Conservation Buffers in Agricultural Landscapes. Data in Brief, 7, 1254-1257.
Radina, M. E., Fu, M. R., Horstman, L., & Kang, Y. (2015). Breast Cancer related Lymphedema and Sexual Experiences: a Mixed-method Comparison Study. PsychoOncology, 24(12), 1655-1662.
Dosskey, M. G., Qiu, Z., & Kang, Y. (2013). A Comparison of DEM-Based Indexes for Targeting the Placement of Vegetative Bu ers in Agricultural Watersheds. JAWRA Journal of the American Water Resources Association, 49(6), 1270-1283.
Fu, M. R., Cleland, C. M., Guth, A. A., Kayal, M., Haber, J., Cartwright, F., Kleinman, R., Kang, Y., Scagliola, J. & Axelrod, D. (2013). L-dex Ratio in Detecting Breast Cancer-related Lymphedema: Reliability, Sensitivity, and Speci city. Lymphology, 46(2), 85.
Fu, M. R., & Kang, Y. (2013). Psychosocial Impact of Living with Cancer-related Lymphedema. In Seminars in oncology nursing (Vol. 29, No. 1, pp. 50-60). WB Saunders.
Ridner, S. H., Qiu, C. M., Kayal, M., Kang, Y., & Fu, M. (2013) Lymphedema Self-management: Lymphedema Management Special Interest Group Newsletter.
Fu, M., & Kang, Y. (2012) Barriers to Self-management of Cancer-related Lymphedema. LymphLink. 2012;24(3):6-7.
Kang, Y. , & Fu, M. (2012). Seroma: A Predictor for Breast Cancer-related Lymphedema. LymphLink, 24(4), 10.
* Equal contribution.
The 19th Conference of Applied Stochastic Models and Data Analysis International Society, Athens, Greece. June, 2021. (Peer-reviewed invited talk.) Title: Dropout Training is Distributional Optimal.
6th SMTDA International Conference with Demographics Workshop, Athens, Greece. June, 2020. (Peer-reviewed invited talk.) Title: Distributional Robust Learning.
The 18th Conference of Applied Stochastic Models and Data Analysis International Society, Florence, Italy. June, 2019. (Peer-reviewed invited talk.) Title: Data-driven Optimal Cost Selection for Distributionally Robust Optimization.
2018 Data Science, Statistics and Visualization Conference, Vienna, Austria. July, 2018. (Peer-reviewed long presentation.) Title: Distributionally Robust Optimization via Optimal Transport and Its Applications
2018 Data Science, Statistics and Visualization Conference, Vienna, Austria. July, 2018. (Peer-reviewed poster session.) Title: Semi-supervised Learning Based on Distributionally Robust Optimization
5th SMTDA International Conference with Demographics Workshop, Chania, Crete, Greece. June, 2018. (Peer-reviewed invited talk.) Title: Data-driven Distributionally Robust Optimization via Optimal Transport
4. 2016 ICSA International Conference, Shanghai, China. Dec. 2016. (Contributed talk.) Title: Robust Wasserstein Profile Inference.
5. 2016 INFORMS Conference, Nashville, TN, USA. Nov. 2016. (Contributed talk.) Title: Sample-out-of-Sample Profile Inference.
6. 2015 Minghui Memorial Conference, New York, NY, USA. Apr. 2015. (Contributed talk.) Title: Optimal Allocation of Capital Funds in Insurance and Reinsurance Networks.
Blanchet, J., Kang, Y., Li, F., & Lam, H. (2018). Quantify Uncertainty for Stochastic Optimiza-tion: Empirical Likelihood Approach. (Working Manuscript)
Blanchet, J.,Kang, Y. ,& Loisel, S. (2018). Optimal Allocation of Capital Funds in Insurance and Reinsurance Networks (Working Manuscript).
Blanchat, J., Kang, Y. & Capponi, A. (2018). Dynamical Models for a Counter-Party Clearing House of an Insurance-Reinsurance Network (Working Manuscript).