11 papers
Smoothness criterion for Fano orbifolds and bases of Lagrangian fibrations
Zhiyu Liu
We establish a smoothness criterion for Fano varieties with quotient singularities. As an application, we show that if is a Lagrangian fibration from a compact hyper-Kähle…
One-Step Score-Based Density Ratio Estimation
Wei Chen, Qibin Zhao, John Paisley +2
Density ratio estimation (DRE) is a useful tool for quantifying discrepancies between probability distributions, but existing approaches often involve a trade-off between estimatio…
Towards Disentangled Preference Optimization Dynamics: Suppress the Loser, Preserve the Winner
Wei Chen, Yubing Wu, Junmei Yang +5
Preference optimization is widely used to align large language models (LLMs) with human preferences. However, many margin-based methods also suppress the chosen response when they…
A Minimum Variance Path Principle for Accurate and Stable Score-Based Density Ratio Estimation
Wei Chen, Jiacheng Li, Shigui Li +4
Score-based methods are powerful across machine learning, but they face a paradox: theoretically path-independent, yet practically path-dependent. We resolve this by proving that p…
Diffusion Secant Alignment for Score-Based Density Ratio Estimation
Wei Chen, Shigui Li, Jiacheng Li +6
Estimating density ratios has become increasingly important with the recent rise of score-based and diffusion-inspired methods. However, current tangent-based approaches rely on a…
Dequantified Diffusion-Schr{ö}dinger Bridge for Density Ratio Estimation
Wei Chen, Shigui Li, Jiacheng Li +3
Density ratio estimation is fundamental to tasks involving -divergences, yet existing methods often fail under significantly different distributions or inadequately overlapping…
Flexible Bayesian Last Layer Models Using Implicit Priors and Diffusion Posterior Sampling
Jian Xu, Zhiqi Lin, Shigui Li +4
Bayesian Last Layer (BLL) models focus solely on uncertainty in the output layer of neural networks, demonstrating comparable performance to more complex Bayesian models. However,…
Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling
Jian Xu, Shian Du, Junmei Yang +3
Gaussian Process Latent Variable Models (GPLVMs) have become increasingly popular for unsupervised tasks such as dimensionality reduction and missing data recovery due to their fle…
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations
Jian Xu, Zhiqi Lin, Min Chen +3
Deep Gaussian process models typically employ discrete hierarchies, but recent advancements in differential Gaussian processes (DiffGPs) have extended these models to infinite dept…
Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes
Jian Xu, Shian Du, Junmei Yang +2
Deep Gaussian Process (DGP) models offer a powerful nonparametric approach for Bayesian inference, but exact inference is typically intractable, motivating the use of various appro…
Bayesian Gaussian Process ODEs via Double Normalizing Flows
Jian Xu, Shian Du, Junmei Yang +3
Recently, Gaussian processes have been used to model the vector field of continuous dynamical systems, referred to as GPODEs, which are characterized by a probabilistic ODE equatio…