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stat.ML2026
Multilevel and Sequential Monte Carlo for Training-Free Diffusion Guidance
Aidan Gleich, Scott C. Schmidler
We address the problem of accurate, training-free guidance for conditional generation in trained diffusion models. Existing methods typically rely on point-estimates to approximate…
stat.ML2021★ 18 cited
Minimax Mixing Time of the Metropolis-Adjusted Langevin Algorithm for Log-Concave Sampling
Keru Wu, Scott Schmidler, Yuansi Chen
We study the mixing time of the Metropolis-adjusted Langevin algorithm (MALA) for sampling from a log-smooth and strongly log-concave distribution. We establish its optimal minimax…