3 papers
stat.CO2026
Bures-Wasserstein Importance-Weighted Evidence Lower Bound: Exposition and Applications
Peiwen Jiang, Takuo Matsubara, Minh-Ngoc Tran
The Importance-Weighted Evidence Lower Bound (IW-ELBO) has emerged as an effective objective for variational inference (VI), tightening the standard ELBO and mitigating the mode-se…
stat.CO2026
On the Convergence of Wasserstein Gradient Descent for Sampling
Van Chien Ta, Thi Mai Hong Chu, Minh-Ngoc Tran
This paper studies the optimization of the KL functional on the Wasserstein space of probability measures, and develops a sampling framework based on Wasserstein gradient descent (…
stat.ML2026
Importance Weighted Variational Inference without the Reparameterization Trick
Kamélia Daudel, Minh-Ngoc Tran, Cheng Zhang
Importance weighted variational inference (VI) approximates densities known up to a normalizing constant by optimizing bounds that tighten with the number of Monte Carlo samples $N…