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Proposes a variance-reduced zeroth-order Langevin sampling method for non-log-concave distributions, establishing the first non-asymptotic convergence guarantees, and applies it to inverse problems with score-based generative priors.
This paper analyzes zero-shot conditional sampling with pretrained diffusion models for linear inverse problems, providing information-theoretic guarantees and proposing a projected-Langevin initialization method.
OpenAI presents implicit generation and generalization methods for energy-based models (EBMs) that use Langevin dynamics for iterative refinement to generate samples without explicit generator networks. The approach offers advantages including adaptive computation time, flexibility in learning disconnected data modes, and built-in compositionality through product of experts.