Summary of Kernel Semi-implicit Variational Inference, by Ziheng Cheng et al.
Kernel Semi-Implicit Variational Inference
by Ziheng Cheng, Longlin Yu, Tianyu Xie, Shiyue Zhang, Cheng Zhang
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents kernel semi-implicit variational inference (KSIVI), an extension of traditional variational families that incorporates semi-implicit distributions defined in a hierarchical manner. Unlike classical SIVI, which often relies on surrogates of the evidence lower bound (ELBO) and may introduce biases during training, KSIVI utilizes a kernel trick to eliminate the need for additional optimization steps. The paper shows that optimizing over a reproducing kernel Hilbert space (RKHS) leads to an explicit solution for the lower-level problem, allowing for the use of kernel Stein discrepancy (KSD) as the objective function. Convergence guarantees are established through derivation of an upper bound for the variance of Monte Carlo gradient estimators. The paper demonstrates the effectiveness and efficiency of KSIVI on both synthetic and real-world Bayesian inference tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to do something called “variational inference”. It’s like trying to figure out what’s going on in a complicated system by making some guesses. But instead of just guessing, this new method uses special math tricks to make the process better. The goal is to get more accurate results and be able to work with big datasets. The paper shows that this new method works well on both fake data and real-world problems. |
Keywords
» Artificial intelligence » Bayesian inference » Inference » Kernel trick » Objective function » Optimization
