Stein Variational Evolution Strategies

• URL: http://arxiv.org/abs/2410.10390v1
• Date: Mon, 14 Oct 2024 11:24:41 GMT
• Title: Stein Variational Evolution Strategies
• Authors: Cornelius V. Braun, Robert T. Lange, Marc Toussaint,
• Abstract summary: Stein Variational Gradient Descent (SVGD) is a highly efficient method to sample from an unnormalized probability distribution. Existing gradient-free versions of SVGD make use of simple Monte Carlo approximations or gradients from surrogate distributions, both with limitations. We combine SVGD steps with evolution strategy (ES) updates to improve gradient-free Stein variational inference.
• Score: 17.315583101484147
• License: http://creativecommons.org/licenses/by/4.0/
• 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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