7 papers
Guidance for Prior Change via Density Ratio Estimation
Yichen Zang, Song Liu, Jiun-Yi Lin
Simulation-Based Inference (SBI) serves as a vital framework for parameter inference in scientific fields where simulators involve intractable likelihoods, yet while amortized gene…
Zero-Flow Two-Sample Tests
Yakun Wang, Leyang Wang, Song Liu +1
Motivated by the success of modern flow-based generative models in modeling complex data, we study two-sample testing through the lens of flow-based methods. We propose the Zero-Fl…
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…
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…
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…