2 papers
cs.LG2026
Formalizing the Sampling Design Space of Diffusion-Based Generative Models via Adaptive Solvers and Wasserstein-Bounded Timesteps
Sangwoo Jo, Sungjoon Choi
Diffusion-based generative models have achieved remarkable performance across various domains, yet their practical deployment is often limited by high sampling costs. While prior w…
cs.LG2025
A diffusion-based generative model for financial time series via geometric Brownian motion
Gihun Kim, Sun-Yong Choi, Yeoneung Kim
We propose a novel diffusion-based generative framework for financial time series that incorporates geometric Brownian motion (GBM), the foundation of the Black--Scholes theory, in…