Stein Variational Adaptive Importance Sampling
This work addresses the need for more flexible and interpretable importance sampling methods in machine learning, though it appears incremental as it combines existing techniques.
The authors tackled the problem of adaptive importance sampling by integrating Stein variational gradient descent with importance sampling, resulting in an algorithm that performs well on evaluating partition functions of restricted Boltzmann machines and testing likelihood of variational auto-encoders.
We propose a novel adaptive importance sampling algorithm which incorporates Stein variational gradient decent algorithm (SVGD) with importance sampling (IS). Our algorithm leverages the nonparametric transforms in SVGD to iteratively decrease the KL divergence between our importance proposal and the target distribution. The advantages of this algorithm are twofold: first, our algorithm turns SVGD into a standard IS algorithm, allowing us to use standard diagnostic and analytic tools of IS to evaluate and interpret the results; second, we do not restrict the choice of our importance proposal to predefined distribution families like traditional (adaptive) IS methods. Empirical experiments demonstrate that our algorithm performs well on evaluating partition functions of restricted Boltzmann machines and testing likelihood of variational auto-encoders.