| 2025 | AISTATS | Bayesian Principles Improve Prompt Learning In Vision-Language Models. | Mingyu Kim, Jongwoo Ko, Mijung Park |
| 2022 | ICML | Hermite Polynomial Features for Private Data Generation. | Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder, Kamil Adamczewski, Mijung Park |
| 2021 | AISTATS | Dirichlet Pruning for Convolutional Neural Networks. | Kamil Adamczewski, Mijung Park |
| 2021 | AISTATS | DP-MERF: Differentially Private Mean Embeddings with RandomFeatures for Practical Privacy-preserving Data Generation. | Frederik Harder, Kamil Adamczewski, Mijung Park |
| 2020 | AAAI | Interpretable and Differentially Private Predictions. | Frederik Harder, Matthias Bauer, Mijung Park |
| 2020 | AAAI | Radial and Directional Posteriors for Bayesian Deep Learning. | ChangYong Oh, Kamil Adamczewski, Mijung Park |
| 2020 | IJCAI | Variational Bayes in Private Settings (VIPS) (Extended Abstract). | James R. Foulds, Mijung Park, Kamalika Chaudhuri, Max Welling |
| 2017 | AISTATS | DP-EM: Differentially Private Expectation Maximization. | Mijung Park, James R. Foulds, Kamalika Choudhary, Max Welling |
| 2016 | AISTATS | K2-ABC: Approximate Bayesian Computation with Kernel Embeddings. | Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic |
| 2013 | AISTATS | Bayesian Structure Learning for Functional Neuroimaging. | Mijung Park, Oluwasanmi Koyejo, Joydeep Ghosh, Russell A. Poldrack, Jonathan W. Pillow |
| 2011 | GLOBECOM | A Machine Learning Approach to Link Adaptation for SC-FDE System. | Zrinka Puljiz, Mijung Park, Robert W. Heath Jr. |