| 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 | 375 | 2013 |
| Density-difference estimation M Sugiyama, T Kanamori, T Suzuki, MC Plessis, S Liu, I Takeuchi Neural Computation 25 (10), 2734-2775, 2013 | 70 | 2013 |
| 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 | 30 | 2014 |
| 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 | 27 | 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 | 22 | 2016 |
| Support consistency of direct sparse-change learning in Markov networks S Liu, T Suzuki, R Relator, J Sese, M Sugiyama, K Fukumizu The Annals of Statistics 45 (3), 959-990, 2017 | 17 | 2017 |
| Density-difference estimation M Sugiyama, T Kanamori, T Suzuki, MD Plessis, S Liu, I Takeuchi Advances in neural information processing systems, 683-691, 2012 | 16 | 2012 |
| 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 | 13 | 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 | 13 | 2014 |
| Heterogeneous model reuse via optimizing multiparty multiclass margin XZ Wu, S Liu, ZH Zhou International Conference on Machine Learning, 6840-6849, 2019 | 12 | 2019 |
| Direct learning of sparse changes in markov networks by density ratio estimation S Liu, JA Quinn, MU Gutmann, M Sugiyama Joint European conference on machine learning and knowledge discovery in …, 2013 | 12 | 2013 |
| Learning sparse structural changes in high-dimensional Markov networks S Liu, K Fukumizu, T Suzuki Behaviormetrika 44 (1), 265-286, 2017 | 10 | 2017 |
| Support consistency of direct sparse-change learning in Markov networks S Liu, T Suzuki, M Sugiyama Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015 | 10 | 2015 |
| Trimmed density ratio estimation S Liu, A Takeda, T Suzuki, K Fukumizu Advances in Neural Information Processing Systems, 4518-4528, 2017 | 9 | 2017 |
| Fisher efficient inference of intractable models S Liu, T Kanamori, W Jitkrittum, Y Chen Advances in Neural Information Processing Systems, 8793-8803, 2019 | 4 | 2019 |
| Generic multiplicative methods for implementing machine learning algorithms on mapreduce S Liu, P Flach, N Cristianini arXiv preprint arXiv:1111.2111, 2011 | 4 | 2011 |
| Estimating Density Models with Complex Truncation Boundaries S Liu, T Kanamori arXiv preprint arXiv:1910.03834, 2019 | 3 | 2019 |
| Interpreting Outliers: Localized Logistic Regression for Density Ratio Estimation M Yamada, S Liu, S Kaski arXiv preprint arXiv:1702.06354, 2017 | 3 | 2017 |
| 非定常環境下での学習: 共変量シフト適応, クラスバランス変化適応, 変化検知 杉山将， 山田誠， ドゥ・プレシマーティヌス・クリストフェル 日本統計学会誌 44 (1), 113-136, 2014 | 2 | 2014 |
| Load pattern analysis of key accounts based on two-step clustering Y Li, Q Huang, S Liu, P Liu Proceedings of the 2016 International Conference on Intelligent Information …, 2016 | 1 | 2016 |
