| Particle algorithms for maximum likelihood training of latent variable models J Kuntz, JN Lim, AM Johansen International Conference on Artificial Intelligence and Statistics, 5134-5180, 2023 | 25 | 2023 |
| Faking feature importance: A cautionary tale on the use of differentially-private synthetic data O Giles, K Hosseini, G Mingas, O Strickson, L Bowler, CR Smith, H Wilde, ... arXiv preprint arXiv:2203.01363, 2022 | 13 | 2022 |
| Kernel Stein tests for multiple model comparison JN Lim, M Yamada, B Schölkopf, W Jitkrittum Advances in Neural Information Processing Systems 32, 2019 | 13 | 2019 |
| More powerful selective kernel tests for feature selection JN Lim, M Yamada, W Jitkrittum, Y Terada, S Matsui, H Shimodaira International Conference on Artificial Intelligence and Statistics, 820-830, 2020 | 10 | 2020 |
| Momentum Particle Maximum Likelihood JN Lim, J Kuntz, S Power, AM Johansen International Conference on Machine Learning 235, 29816--29871, 2024 | 5 | 2024 |
| Energy discrepancies: a score-independent loss for energy-based models T Schröder, Z Ou, J Lim, Y Li, S Vollmer, A Duncan Advances in Neural Information Processing Systems 37, 2023 | 4 | 2023 |
| Energy-based models for functional data using path measure tilting JN Lim, S Vollmer, L Wolf, A Duncan International Conference on Artificial Intelligence and Statistics, 1904-1923, 2023 | 2 | 2023 |
| Particle semi-implicit variational inference JN Lim, AM Johansen Advances in Neural Information Processing Systems 38, 2024 | 1 | 2024 |
