フォロー
Song Liu
Song Liu
Senior Lecturer in Statistical Science, University of Bristol, UK
確認したメール アドレス: bristol.ac.uk - ホームページ
タイトル
引用先
引用先
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
5812013
Density-difference estimation
M Sugiyama, T Kanamori, T Suzuki, MC du Plessis, S Liu, I Takeuchi
Neural Computation 25 (10), 2734-2775, 2013
882013
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
472013
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
442016
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
402014
Heterogeneous model reuse via optimizing multiparty multiclass margin
XZ Wu, S Liu, ZH Zhou
International Conference on Machine Learning, 6840-6849, 2019
372019
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
372014
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
372014
Support consistency of direct sparse-change learning in Markov networks
S Liu, T Suzuki, R Relator, J Sese, M Sugiyama, K Fukumizu
232017
Density-difference estimation
M Sugiyama, T Kanamori, T Suzuki, M Plessis, S Liu, I Takeuchi
Advances in neural information processing systems 25, 2012
212012
Trimmed density ratio estimation
S Liu, A Takeda, T Suzuki, K Fukumizu
Advances in neural information processing systems 30, 2017
192017
Sliced Wasserstein variational inference
M Yi, S Liu
Asian Conference on Machine Learning, 1213-1228, 2023
182023
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
182017
Fisher efficient inference of intractable models
S Liu, T Kanamori, W Jitkrittum, Y Chen
Advances in Neural Information Processing Systems 32, 2019
132019
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
122022
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
122013
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
112022
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
92015
Estimating density models with complex truncation boundaries
S Liu, T Kanamori
arXiv preprint arXiv:1910.03834, 2019
72019
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
52014
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