隐式扩散：通过随机抽样实现高效优化

摘要

我们提出了一种优化分布的新算法，这些分布是由参数化随机扩散隐式定义的。通过这样做，我们可以通过优化参数来修改采样过程的结果分布。我们引入了一个针对这些过程的一阶优化的通用框架，它在单个循环中同时执行优化和采样步骤。这种方法受到双层优化和自动隐式微分的最新进展的启发，利用了将采样视为在概率分布空间上的优化的观点。我们对我们的方法的性能提供了理论保证，以及展示了它在真实环境中有效性的实验结果。

English

We present a new algorithm to optimize distributions defined implicitly by parameterized stochastic diffusions. Doing so allows us to modify the outcome distribution of sampling processes by optimizing over their parameters. We introduce a general framework for first-order optimization of these processes, that performs jointly, in a single loop, optimization and sampling steps. This approach is inspired by recent advances in bilevel optimization and automatic implicit differentiation, leveraging the point of view of sampling as optimization over the space of probability distributions. We provide theoretical guarantees on the performance of our method, as well as experimental results demonstrating its effectiveness in real-world settings.

隐式扩散：通过随机抽样实现高效优化

Implicit Diffusion: Efficient Optimization through Stochastic Sampling

摘要

Support