Stein Variational Evolution Strategies

# Stein Variational Evolution Strategies

**arXiv ID:** 2410.10390
**Authors:** Cornelius V. Braun, Robert T. Lange, Marc Toussaint
**Published:** 2024-10-14T11:24:41Z
**Abstract:**
Stein Variational Gradient Descent (SVGD) is a highly efficient method to sample from an unnormalized probability distribution. However, the SVGD update relies on gradients of the log-density, which may not always be available. Existing gradient-free versions of SVGD make use of simple Monte Carlo approximations or gradients from surrogate distributions, both with limitations. To improve gradient-free Stein variational inference, we combine SVGD steps with evolution strategy (ES) updates. Our results demonstrate that the resulting algorithm generates high-quality samples from unnormalized target densities without requiring gradient information. Compared to prior gradient-free SVGD methods, we find that the integration of the ES update in SVGD significantly improves the performance on multiple challenging benchmark problems.

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## References

- [arXiv:2410.10390](http://arxiv.org/abs/2410.10390v3)