3 papers
stat.ML2026
Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures
Chenyang Wang, Weizhong Wang, Yinuo Ren +2
iffusion-based generative models increasingly rely on inference-time guidance, adding a drift term or reweighting mixture of experts, to improve sample quality on task-specific obj…
stat.ME2026
Estimating the Number of Components in Finite Mixture Models via Variational Approximation
Chenyang Wang, Yun Yang
This work introduces a new method for selecting the number of components in finite mixture models (FMMs) using variational Bayes, inspired by the large-sample properties of the Evi…
stat.ML2026
PAC-Bayes Bounds for Gibbs Posteriors via Singular Learning Theory
Chenyang Wang, Yun Yang
We derive explicit non-asymptotic PAC-Bayes generalization bounds for Gibbs posteriors, that is, data-dependent distributions over model parameters obtained by exponentially tiltin…