Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. - Song Liu 002; John A. Quinn; Michael U. Gutmann; Masashi Sugiyama (ECML/PKDD (2), 2013)

Direct Divergence Approximation between Probability Distributions and Its Applications in Machine Learning. - Masashi Sugiyama; Song Liu 002; Marthinus Christoffel du Plessis; Masao Yamanaka; Makoto Yamada; Taiji Suzuki; Takafumi Kanamori (J. Comput. Sci. Eng., 2013)

Density-Difference Estimation. - Masashi Sugiyama; Takafumi Kanamori; Taiji Suzuki; Marthinus Christoffel du Plessis; Song Liu 002; Ichiro Takeuchi (Neural Comput., 2013)

Change-point detection in time-series data by relative density-ratio estimation. - Song Liu 002; Makoto Yamada; Nigel Collier; Masashi Sugiyama (Neural Networks, 2013)

Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. - Song Liu 002; John A. Quinn; Michael U. Gutmann; Taiji Suzuki; Masashi Sugiyama (Neural Comput., 2014)

Support Consistency of Direct Sparse-Change Learning in Markov Networks. - Song Liu 002; Taiji Suzuki; Masashi Sugiyama (AAAI, 2015)

Lazy Transfer Learning. - Song Liu 002; Kenji Fukumizu (CoRR, 2015)

Structure Learning of Partitioned Markov Networks. - Song Liu 002; Taiji Suzuki; Masashi Sugiyama; Kenji Fukumizu (ICML, 2016)

Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective. - Song Liu 002; Kenji Fukumizu (SDM, 2016)

Trimmed Density Ratio Estimation. - Song Liu 002; Akiko Takeda; Taiji Suzuki; Kenji Fukumizu (NIPS, 2017)

Interpreting Outliers: Localized Logistic Regression for Density Ratio Estimation. - Makoto Yamada; Song Liu 002; Samuel Kaski (CoRR, 2017)

Model Inference with Stein Density Ratio Estimation. - Song Liu 002; Wittawat Jitkrittum; Carl Henrik Ek (CoRR, 2018)

Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence. - Yung-Kyun Noh; Masashi Sugiyama; Song Liu 002; Marthinus Christoffel du Plessis; Frank Chongwoo Park; Daniel D. Lee (Neural Comput., 2018)

Fisher Efficient Inference of Intractable Models. - Song Liu 002; Takafumi Kanamori; Wittawat Jitkrittum; Yu Chen (NeurIPS, 2019)

Estimating Density Models with Complex Truncation Boundaries. - Song Liu 002; Takafumi Kanamori (CoRR, 2019)

A General Class of Transfer Learning Regression without Implementation Cost. - Shunya Minami; Song Liu 002; Stephen Wu 001; Kenji Fukumizu; Ryo Yoshida (AAAI, 2021)

Score Matching for Truncated Density Estimation on a Manifold. - Daniel J. Williams; Song Liu 002 (TAG-ML, 2022)

Estimating Density Models with Truncation Boundaries using Score Matching. - Song Liu 002; Takafumi Kanamori; Daniel J. Williams (J. Mach. Learn. Res., 2022)

Approximate Stein Classes for Truncated Density Estimation. - Daniel J. Williams; Song Liu 002 (ICML, 2023)

High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching. - Daniel J. Williams; Leyang Wang; Qizhen Ying; Song Liu 002; Mladen Kolar (AISTATS, 2025)

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold. - Song Liu 002; Leyang Wang; Yakun Wang 005 (UAI, 2025)

Missing Data Imputation by Reducing Mutual Information with Rectified Flows. - Jiahao Yu; Qizhen Ying; Leyang Wang; Ziyue Jiang 006; Song Liu 002 (CoRR, 2025)