
The author explores the dimension dependency of computational complexity in high-dimensional sampling problems, focusing on the benefits of biased samplers over unbiased ones. The modified Langevin algorithm with prior diffusion is shown to achieve dimension-independent convergence for a broader class of target distributions.


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analyzing-langevin-algorithms-with-prior-diffusion-for-non-log-concave-sampling


Analyzing Langevin Algorithms with Prior Diffusion for Non-Log-Concave Sampling


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Prior diffusion in Langevin algorithms can achieve dimension-independent convergence for non-log-concave distributions.


langevin-algorithms-with-prior-diffusion-for-non-log-concave-sampling-dimension-independent-convergence-analysis


Langevin Algorithms with Prior Diffusion for Non-Log-Concave Sampling: Dimension-Independent Convergence Analysis



Prior diffusion in Langevin algorithms can achieve dimension-independent convergence for a broader class of target distributions beyond log-concavity.


langevin-algorithms-with-prior-diffusion-for-non-log-concave-sampling-improved-analysis


Langevin Algorithms with Prior Diffusion for Non-Log-Concave Sampling: Improved Analysis
