About
Mijung’s primary focus is on building privacy-preserving machine learning algorithms using a sophisticated mathematical notion called differential privacy. Her other research interests include: compressing neural network models using Bayesian methods, and; understanding the relationships between differential privacy and other emerging notions in machine learning such as fairness, interpretability, and causality.
Relevant Publications
Harder, F., Adamczewski, Park, M. DP-MERF: Differentially Private Mean Embeddings with Random Features for Practical Privacy-preserving Data Generation, Artificial Intelligence and Statistics 2021, PMLR 130:1819‑1827
Adamczewski K., Park, M. Dirichlet Pruning for Convolutional Neural Networks, Artificial Intelligence and Statistics 2021, PMLR 130:3637‑3645
Harder, F., Bauer, M., Park, M. Interpretable and Differentially Private Predictions, AAAI Conference on Artificial Intelligence 2020, 34(04), 4083‑4090.
Park M., Foulds, J., Chaudhuri, K., Welling, M. Variational Bayes in Private Settings (VIPS), Journal of Artificial Intelligence Research 2020.
Park, M., Foulds, J., Chaudhuri, K., Welling, M. DP-EM: Differentially Private Expectation Maximization, Artificial Intelligence and Statistics (AISTATS) 2017, PMLR 54:896‑904.
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