| Change-point detection in time-series data by relative density-ratio estimation S Liu, M Yamada, N Collier, M Sugiyama Neural Networks 43, 72-83, 2013 | 616 | 2013 |
| Density-difference estimation M Sugiyama, T Kanamori, T Suzuki, MC du Plessis, S Liu, I Takeuchi Neural Computation 25 (10), 2734-2775, 2013 | 89 | 2013 |
| Direct divergence approximation between probability distributions and its applications in machine learning M Sugiyama, S Liu, MC Du Plessis, M Yamanaka, M Yamada, T Suzuki, ... Journal of Computing Science and Engineering 7 (2), 99-111, 2013 | 49 | 2013 |
| Statistical outlier detection for diagnosis of cyber attacks in power state estimation Y Chakhchoukh, S Liu, M Sugiyama, H Ishii 2016 IEEE Power and Energy Society General Meeting (PESGM), 1-5, 2016 | 46 | 2016 |
| Direct learning of sparse changes in Markov networks by density ratio estimation S Liu, JA Quinn, MU Gutmann, T Suzuki, M Sugiyama Neural computation 26 (6), 1169-1197, 2014 | 43 | 2014 |
| Bias reduction and metric learning for nearest-neighbor estimation of Kullback-Leibler divergence YK Noh, M Sugiyama, S Liu, MC Plessis, FC Park, DD Lee Artificial Intelligence and Statistics, 669-677, 2014 | 38 | 2014 |
| Bias reduction and metric learning for nearest-neighbor estimation of Kullback-Leibler divergence YK Noh, M Sugiyama, S Liu, MC Plessis, FC Park, DD Lee Artificial Intelligence and Statistics, 669-677, 2014 | 38 | 2014 |
| Heterogeneous model reuse via optimizing multiparty multiclass margin XZ Wu, S Liu, ZH Zhou International Conference on Machine Learning, 6840-6849, 2019 | 37 | 2019 |
| Support consistency of direct sparse-change learning in Markov networks S Liu, T Suzuki, R Relator, J Sese, M Sugiyama, K Fukumizu | 24 | 2017 |
| Trimmed density ratio estimation S Liu, A Takeda, T Suzuki, K Fukumizu Advances in neural information processing systems 30, 2017 | 24 | 2017 |
| Sliced Wasserstein variational inference M Yi, S Liu Asian Conference on Machine Learning, 1213-1228, 2023 | 22 | 2023 |
| Two-sample inference for high-dimensional markov networks B Kim, S Liu, M Kolar Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2021 | 22 | 2021 |
| Density-difference estimation M Sugiyama, T Kanamori, T Suzuki, M Plessis, S Liu, I Takeuchi Advances in neural information processing systems 25, 2012 | 22 | 2012 |
| Model reuse with reduced kernel mean embedding specification XZ Wu, W Xu, S Liu, ZH Zhou IEEE Transactions on Knowledge and Data Engineering 35 (1), 699-710, 2021 | 21 | 2021 |
| Learning sparse structural changes in high-dimensional Markov networks: A review on methodologies and theories S Liu, K Fukumizu, T Suzuki Behaviormetrika 44, 265-286, 2017 | 19 | 2017 |
| Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models L Sharrock, J Simons, S Liu, M Beaumont arXiv preprint arXiv:2210.04872, 2022 | 16 | 2022 |
| Estimating density models with truncation boundaries using score matching S Liu, T Kanamori, DJ Williams Journal of Machine Learning Research 23 (186), 1-38, 2022 | 15 | 2022 |
| Fisher efficient inference of intractable models S Liu, T Kanamori, W Jitkrittum, Y Chen Advances in Neural Information Processing Systems 32, 2019 | 15 | 2019 |
| Direct learning of sparse changes in markov networks by density ratio estimation S Liu, JA Quinn, MU Gutmann, M Sugiyama Machine Learning and Knowledge Discovery in Databases: European Conference …, 2013 | 12 | 2013 |
| Support consistency of direct sparse-change learning in Markov networks S Liu, T Suzuki, M Sugiyama Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 9 | 2015 |
