sequential-inference

This paper introduces Neural Optimal Particle Filters (NOPFs), which learn amortized proposal distributions to improve sample efficiency and distributional accuracy in sequential inference tasks, enhancing classical particle filters with data-driven methods.

This paper argues for a sequential inference framework to enhance LLM trustworthiness by modeling interactions as dependent stochastic processes, ensuring validity under repeated use, and enabling online monitoring for behavioral shifts.