New analysis details optimal transport map errors for filtering

Researchers have published an analysis of estimation errors in optimal transport-based algorithms for filtering and data assimilation. This work extends previous error analyses of Brenier maps to conditional Brenier maps, which are relevant for simulation-based inference. The findings are applied to the optimal transport filtering algorithm proposed by Al-Jarrah et al. at ICML 2024, with an enhanced version demonstrating effectiveness on various non-Gaussian and high-dimensional examples. AI

IMPACT Provides theoretical groundwork for improving filtering and data assimilation techniques in machine learning applications.

RANK_REASON The cluster contains a research paper published on arXiv detailing theoretical analysis and algorithmic improvements. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• Al-Jarrah
• arXiv
• Brenier maps
• conditional Brenier maps
• International Conference on Machine Learning
• Michele Martino
• optimal transport

• paper
• other

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