Invited Talks

International

1. Kim, Y.-G., Luo, S. X., Brandt, L., Cheung, K., Nunes, E. V., Roll, J., and Liu, Y. (2024). “Optimizing Contingency Management Interventions in Substance Use Disorder Treatment with Reinforcement Learning.’’ The Joint Statistical Meetings (JSM), Portland, OR, USA.
2. Kim, Y.-G. and Liu, Y. (2024). “Deep Identifiable Generative Models for Multi-Modal Data Analysis.” The 2024 International Chinese Statistical Association (ICSA) Applied Statistics Symposium, Nashville, TN, USA.
3. Kim, Y.-G. (2024). Reliable Statistical Reasoning with Conditional Generative Models. The 30th Korean International Statistical Society Virtual Seminar.
4. Kim, Y.-G., Kwon, Y., Chang, H., and Paik, M.C. (2019). “Lipschitz continuous autoencoders in application to anomaly detection.” IMS-China International Conference on Statistics and Probability, Dalian, China.

Domestic (Republic of Korea)

1. (Scheduled) Kim, Y.-G. (2025). “Reliable Statistical Reasoning with Conditional Generative Models.” Ulsan National Institute of Science and Technology (UNIST).
2. Kim, Y.-G. (2024). “Reliable Statistical Reasoning with Conditional Generative Models.” Korea University.
3. Kim, Y.-G. (2023). “Recent Topics on Conditional Generative Models: statistical distance of conditional distributions and identifiability.” Ulsan National Institute of Science and Technology (UNIST).
4. Kim, Y.-G. (2023). “Recent Topics on Conditional Generative Models: statistical distance of conditional distributions and identifiability.” Korea Institute for Advanced Study (KIAS).
5. Kim, Y.-G., Lee, K., and Paik, M.C. (2022). “Conditional Wasserstein generator.” Spring Korea Statistical Conference 2022, Seoul.
6. Kim, Y.-G., Chang, H., and Paik, M.C. (2018). “Unsupervised anomaly detection using inverse Christoffel function via kernelized Wasserstein autoencoders.” Fall Korea Statistical Conference 2018, Seoul.