Sampling with Mollified Interaction Energy Descent

Understand

Sampling from a target measure whose density is only known up to a normalization constant is a fundamental problem in computational statistics and machine learning.

• In this paper, we present a new optimization-based method for sampling called mollified interaction energy descent (MIED).
• MIED minimizes a new class of energies on probability measures called mollified interaction energies (MIEs).
• These energies rely on mollifier functions -- smooth approximations of the Dirac delta originated from PDE theory.