450 citations · 495 across the 23 of their papers we have counts for
23 papers
Approximate Stein Classes for Truncated Density Estimation
Daniel J. Williams, Song Liu
Estimating truncated density models is difficult, as these models have intractable normalising constants and hard to satisfy boundary conditions. Score matching can be adapted to s…
Minimizing -Divergences by Interpolating Velocity Fields
Song Liu, Jiahao Yu, Jack Simons +2
Many machine learning problems can be seen as approximating a \textit{target} distribution using a \textit{particle} distribution by minimizing their statistical discrepancy. Wasse…
Density Ratio Estimation and Neyman Pearson Classification with Missing Data
Josh Givens, Song Liu, Henry W J Reeve
Density Ratio Estimation (DRE) is an important machine learning technique with many downstream applications. We consider the challenge of DRE with missing not at random (MNAR) data…
Edgeworth Expansion by Stein's Method
Xiao Fang, Song-Hao Liu
Edgeworth expansion provides higher-order corrections to the normal approximation for a probability distribution. The classical proof of Edgeworth expansion is via characteristic f…
Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models
Louis Sharrock, Jack Simons, Song Liu +1
We introduce Sequential Neural Posterior Score Estimation (SNPSE), a score-based method for Bayesian inference in simulator-based models. Our method, inspired by the remarkable suc…
Sliced Wasserstein Variational Inference
Mingxuan Yi, Song Liu
Variational Inference approximates an unnormalized distribution via the minimization of Kullback-Leibler (KL) divergence. Although this divergence is efficient for computation and…
Score Matching for Truncated Density Estimation on a Manifold
Daniel J. Williams, Song Liu
When observations are truncated, we are limited to an incomplete picture of our dataset. Recent methods propose to use score matching for truncated density estimation, where the ac…
Cramér-type Moderate Deviation for Quadratic Forms with a Fast Rate
Xiao Fang, Song-Hao Liu, Qi-Man Shao
Let be independent and identically distributed random vectors in . Suppose , , where is the $d\times d…
Estimating the Arc Length of the Optimal ROC Curve and Lower Bounding the Maximal AUC
Song Liu
In this paper, we show the arc length of the optimal ROC curve is an -divergence. By leveraging this result, we express the arc length using a variational objective and estimate…
Continual Density Ratio Estimation in an Online Setting
Yu Chen, Song Liu, Tom Diethe +1
In online applications with streaming data, awareness of how far the training or test set has shifted away from the original dataset can be crucial to the performance of the model.…
A General Class of Transfer Learning Regression without Implementation Cost
Shunya Minami, Song Liu, Stephen Wu +2
We propose a novel framework that unifies and extends existing methods of transfer learning (TL) for regression. To bridge a pretrained source model to the model on a target task,…
Posterior Ratio Estimation of Latent Variables
Song Liu, Yulong Zhang, Mingxuan Yi +1
Density Ratio Estimation has attracted attention from the machine learning community due to its ability to compare the underlying distributions of two datasets. However, in some ap…
Estimating Density Models with Truncation Boundaries using Score Matching
Song Liu, Takafumi Kanamori, Daniel J. Williams
Truncated densities are probability density functions defined on truncated domains. They share the same parametric form with their non-truncated counterparts up to a normalizing co…
Two-sample inference for high-dimensional Markov networks
Byol Kim, Song Liu, Mladen Kolar
Markov networks are frequently used in sciences to represent conditional independence relationships underlying observed variables arising from a complex system. It is often of inte…
Fisher Efficient Inference of Intractable Models
Song Liu, Takafumi Kanamori, Wittawat Jitkrittum +1
Maximum Likelihood Estimators (MLE) has many good properties. For example, the asymptotic variance of MLE solution attains equality of the asymptotic Cram{é}r-Rao lower bound (effi…
Trimmed Density Ratio Estimation
Song Liu, Akiko Takeda, Taiji Suzuki +1
Density ratio estimation is a vital tool in both machine learning and statistical community. However, due to the unbounded nature of density ratio, the estimation procedure can be…
Interpreting Outliers: Localized Logistic Regression for Density Ratio Estimation
Makoto Yamada, Song Liu, Samuel Kaski
We propose an inlier-based outlier detection method capable of both identifying the outliers and explaining why they are outliers, by identifying the outlier-specific features. Spe…
Learning Sparse Structural Changes in High-dimensional Markov Networks: A Review on Methodologies and Theories
Song Liu, Kenji Fukumizu, Taiji Suzuki
Recent years have seen an increasing popularity of learning the sparse \emph{changes} in Markov Networks. Changes in the structure of Markov Networks reflect alternations of intera…
Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective
Song Liu, Kenji Fukumizu
Transfer learning assumes classifiers of similar tasks share certain parameter structures. Unfortunately, modern classifiers uses sophisticated feature representations with huge pa…
Structure Learning of Partitioned Markov Networks
Song Liu, Taiji Suzuki, Masashi Sugiyama +1
We learn the structure of a Markov Network between two groups of random variables from joint observations. Since modelling and learning the full MN structure may be hard, learning…
Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
Song Liu, John A. Quinn, Michael U. Gutmann +2
We propose a new method for detecting changes in Markov network structure between two sets of samples. Instead of naively fitting two Markov network models separately to the two da…
Density-Difference Estimation
Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki +3
We address the problem of estimating the difference between two probability densities. A naive approach is a two-step procedure of first estimating two densities separately and the…
Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation
Song Liu, Makoto Yamada, Nigel Collier +1
The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection a…