Suivre
Song Liu
Song Liu
Senior Lecturer in Statistical Science, University of Bristol, UK
Adresse e-mail validée de bristol.ac.uk - Page d'accueil
Titre
Citée par
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Année
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
5962013
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
482013
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
452016
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
422014
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
382014
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
382014
Heterogeneous model reuse via optimizing multiparty multiclass margin
XZ Wu, S Liu, ZH Zhou
International Conference on Machine Learning, 6840-6849, 2019
372019
Support consistency of direct sparse-change learning in Markov networks
S Liu, T Suzuki, R Relator, J Sese, M Sugiyama, K Fukumizu
242017
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
222021
Trimmed density ratio estimation
S Liu, A Takeda, T Suzuki, K Fukumizu
Advances in neural information processing systems 30, 2017
222017
Density-difference estimation
M Sugiyama, T Kanamori, T Suzuki, M Plessis, S Liu, I Takeuchi
Advances in neural information processing systems 25, 2012
222012
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
202021
Sliced Wasserstein variational inference
M Yi, S Liu
Asian Conference on Machine Learning, 1213-1228, 2023
192023
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
192017
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
162022
Fisher efficient inference of intractable models
S Liu, T Kanamori, W Jitkrittum, Y Chen
Advances in Neural Information Processing Systems 32, 2019
142019
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
132022
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
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
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