改进隐式变分推断，用重要性采样替代马尔可夫链蒙特卡洛。
Revisiting Unbiased Implicit Variational Inference
- 用重要性采样替代原有方法的马尔可夫链蒙特卡洛循环
- 通过最小化前向KL散度稳定学习最优提议分布
- 在多个基准测试中表现优于或媲美顶尖方法
近年来，半隐式变分推断（SIVI）因其能快速生成复杂分布样本而受到关注。然而，高维下样本似然难以估计，现有研究集中于寻找有效的SIVI训练策略。尽管无偏隐式变分推断（UIVI）因内部马尔可夫链蒙特卡洛（MCMC）循环被认为不精确且计算开销大而被忽视，本文重新审视该方法，发现其MCMC循环可通过重要性采样有效替代，且最优提议分布可通过最小化期望前向Kullback-Leibler散度无偏学习。所提出的改进方法在主流SIVI基准上表现出色，性能优于或持平当前最优方法。
原文摘要 · Abstract (English)
Recent years have witnessed growing interest in semi-implicit variational inference (SIVI) methods due to their ability to rapidly generate samples from complex distributions. However, since the likelihood of these samples is non-trivial to estimate in high dimensions, current research focuses on finding effective SIVI training routines. Although unbiased implicit variational inference (UIVI) has largely been dismissed as imprecise and computationally prohibitive because of its inner MCMC loop, we revisit this method and show that UIVI's MCMC loop can be effectively replaced via importance sampling and the optimal proposal distribution can be learned stably by minimizing an expected forward Kullback-Leibler divergence without bias. Our refined approach demonstrates superior performance or parity with state-of-the-art methods on established SIVI benchmarks.
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