无需梯度信息即可高效生成高质量样本的新型采样方法。
Stein Variational Evolution Strategies
- 结合进化策略更新与斯坦因变分梯度下降
- 在多个挑战性任务上显著优于已有无梯度方法
- 适合无梯度或难以计算梯度的采样场景
斯坦因变分梯度下降（SVGD）是一种高效采样未归一化概率分布的方法。然而，其更新依赖对数密度的梯度，而该梯度有时不可用。现有的无梯度版本多采用简单的蒙特卡洛近似或代理分布的梯度，均存在局限性。为提升无梯度斯坦因变分推断性能，本文将SVGD步骤与进化策略（ES）更新相结合。结果表明，所提算法在无需梯度信息的情况下，能从未归一化目标分布中生成高质量样本。相较于现有无梯度SVGD方法，引入ES更新显著提升了在多个挑战性基准问题上的表现。
原文摘要 · Abstract (English)
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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