<ul><li>A unified framework is proposed for approximating transfer operators in machine learning research.</li><li>The framework leverages Monte Carlo sampling to approximate the operator on a finite-dimensional space.</li><li>Convergence of the approximating operator and its spectrum is proven under non-restrictive conditions.</li><li>The study also establishes the convergence relationship between continuous optimization and its discrete counterpart.</li></ul>

Research on Convergence rates in Machine Learning research part1

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