6 papers
Zero-Flow Two-Sample Tests
Yakun Wang, Leyang Wang, Song Liu +1
We propose a new approach to two-sample testing for deciding whether two sets of samples are drawn from the same distribution. The test is built on a statistical discrepancy based…
Zero-Flow Encoders
Yakun Wang, Leyang Wang, Song Liu +1
Flow-based methods have achieved significant success in various generative modeling tasks, capturing nuanced details within complex data distributions. However, few existing works…
Learning Generation Orders for Masked Discrete Diffusion Models via Variational Inference
David Fox, Sam Bowyer, Song Liu +3
Masked discrete diffusion models (MDMs) are a promising new approach to generative modelling, offering the ability for parallel token generation and therefore greater efficiency th…
Missing Data Imputation by Reducing Mutual Information with Rectified Flows
Jiahao Yu, Qizhen Ying, Leyang Wang +2
This paper introduces a novel iterative method for missing data imputation that sequentially reduces the mutual information between data and the corresponding missingness mask. Ins…
Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold
Song Liu, Leyang Wang, Yakun Wang
Optimising probabilistic models is a well-studied field in statistics. However, its connection with the training of generative models remains largely under-explored. In this paper,…
High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching
Daniel J. Williams, Leyang Wang, Qizhen Ying +2
This paper addresses differential inference in time-varying parametric probabilistic models, like graphical models with changing structures. Instead of estimating a high-dimensiona…