6 papers
Implicit Variational Rejection Sampling
Jian Xu, Shigui Li, Wei Chen +6
Variational Inference (VI) is a fundamental inference technique in Bayesian machine learning for approximating complex posterior distributions. Traditional VI often relies on the m…
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…
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…
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…
Natural Language Fine-Tuning
Jia Liu, Yue Wang, Zhiqi Lin +3
Large language model fine-tuning techniques typically depend on extensive labeled data, external guidance, and feedback, such as human alignment, scalar rewards, and demonstration.…
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,…