| DP-MERF: Differentially Private Mean Embeddings with RandomFeatures for Practical Privacy-preserving Data Generation F Harder, K Adamczewski, M Park International Conference on Artificial Intelligence and Statistics, 1819-1827, 2021 | 111 | 2021 |
| Interpretable and differentially private predictions F Harder, M Bauer, M Park Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4083-4090, 2020 | 60 | 2020 |
| Pre-trained perceptual features improve differentially private image generation F Harder, M Jalali, DJ Sutherland, M Park Transactions on Machine Learning Research, 2023 | 36* | 2023 |
| Hermite Polynomial Features for Private Data Generation M Vinaroz, MA Charusaie, F Harder, K Adamczewski, MJ Park International Conference on Machine Learning, 22300-22324, 2022 | 24 | 2022 |
| Bayesian importance of features (bif) K Adamczewski, F Harder, M Park arXiv preprint arXiv:2010.13872, 2020 | 3 | 2020 |
| Learning Łukasiewicz logic F Harder, TR Besold Cognitive Systems Research 47, 42-67, 2018 | 2 | 2018 |
| An approach to supervised learning of three valued Lukasiewicz logic in Hölldobler's core method F Harder, TR Besold CEUR Workshop Proceedings 1895, 24-37, 2017 | 1 | 2017 |
| DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning F Harder, J Köhler, M Welling, M Park arXiv preprint arXiv:1910.06924, 2019 | | 2019 |
Mijung ParkUniversity of British ColumbiaVerified email at cs.ubc.ca
