3 papers
stat.ML2025
A New Stochastic Approximation Method for Gradient-based Simulated Parameter Estimation
Zehao Li, Yijie Peng
This paper tackles the challenge of parameter calibration in stochastic models, particularly in scenarios where the likelihood function is unavailable in an analytical form. We int…
cs.CV2025
Half-order Fine-Tuning for Diffusion Model: A Recursive Likelihood Ratio Optimizer
Tao Ren, Zishi Zhang, Jingyang Jiang +9
The probabilistic diffusion model (DM), generating content by inferencing through a recursive chain structure, has emerged as a powerful framework for visual generation. After pre-…
stat.ML2024
Beyond likelihood ratio bias: Nested multi-time-scale stochastic approximation for likelihood-free parameter estimation
Zehao Li, Zhouchen Lin, Yijie Peng
We study parameter inference in simulation-based stochastic models where the analytical form of the likelihood is unknown. The main difficulty is that score evaluation as a ratio o…