New complexity analysis for normalizing constant estimation in ML

Researchers have developed a new theoretical framework for analyzing the complexity of estimating normalizing constants in probability distributions. This work focuses on annealed importance sampling methods, providing a non-asymptotic analysis with an oracle complexity of \(\\widetilde{O}(\frac{d\beta^2{\mathcal{A}}^2}{\varepsilon^4})\) for achieving a specified relative error. The analysis leverages Girsanov's theorem and optimal transport, avoiding explicit isoperimetric assumptions. Additionally, a novel algorithm using reverse diffusion samplers is proposed to handle large actions and multimodality, with empirical validation. AI

影响 Provides a theoretical foundation for improving density estimation techniques in machine learning models.

排序理由 The cluster contains a new academic paper detailing theoretical advancements in statistical machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

• Bayesian statistics
• Girsanov's theorem
• Jarzynski equality
• machine learning
• optimal transport
• reverse diffusion samplers
• statistical mechanics
• Wei Guo
• Annealed Importance Sampling

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