A learned generative model often produces biased statistics relative to the
underlying data distribution. A standard technique to correct this bias is
importance sampling, where samples from the model are weighted by the
likelihood ratio under model and true distributions. When the likelihood ratio
is unknown, it can be estimated by training a probabilistic classifier to
distinguish samples from the two distributions. We employ this likelihood-free
importance weighting method to correct for the bias in generative models. We
find that this technique consistently improves standard goodness-of-fit metrics
for evaluating the sample quality of state-of-the-art deep generative models,
suggesting reduced bias. Finally, we demonstrate its utility on representative
applications in a) data augmentation for classification using generative
adversarial networks, and b) model-based policy evaluation using off-policy
data.

采用似然比未知的情况下可估计的概率分类器进行样本权重优化的无似然比重要加权方法，可以用于校正生成模型中的偏差问题，并提高生成模型的样本质量和性能。

利用无似然重要性加权的方法校正学习生成模型的偏差

Bias Correction of Learned Generative Models using Likelihood-Free  Importance Weighting

In likelihood-free settings where likelihood evaluations are intractable,
approximate Bayesian computation (ABC) addresses the formidable inference task
to discover plausible parameters of simulation programs that explain the
observations. However, they demand large quantities of simulation calls.
Critically, hyperparameters that determine measures of simulation discrepancy
crucially balance inference accuracy and sample efficiency, yet are difficult
to tune. In this paper, we present kernel embedding likelihood-free inference
(KELFI), a holistic framework that automatically learns model hyperparameters
to improve inference accuracy given limited simulation budget. By leveraging
likelihood smoothness with conditional mean embeddings, we nonparametrically
approximate likelihoods and posteriors as surrogate densities and sample from
closed-form posterior mean embeddings, whose hyperparameters are learned under
its approximate marginal likelihood. Our modular framework demonstrates
improved accuracy and efficiency on challenging inference problems in ecology.

本文提出了一种基于核嵌入的似然免费推理框架（KELFI），它自动学习模型超参数以提高推理准确性，同时具有较高的样本效率，可以解决生态学中的复杂推理问题。

自动无似然推理的条件核均值嵌入的贝叶斯学习

Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic  Likelihood-Free Inference

This paper introduces a simple, general framework for likelihood-free
Bayesian reinforcement learning, through Approximate Bayesian Computation
(ABC). The main advantage is that we only require a prior distribution on a
class of simulators (generative models). This is useful in domains where an
analytical probabilistic model of the underlying process is too complex to
formulate, but where detailed simulation models are available. ABC-RL allows
the use of any Bayesian reinforcement learning technique, even in this case. In
addition, it can be seen as an extension of rollout algorithms to the case
where we do not know what the correct model to draw rollouts from is. We
experimentally demonstrate the potential of this approach in a comparison with
LSPI. Finally, we introduce a theorem showing that ABC is a sound methodology
in principle, even when non-sufficient statistics are used.

该论文介绍了一个基于 ABC 的简单通用框架，用于无似然贝叶斯强化学习，该算法主要利用先验分布，能够应用在基于详细模拟模型的领域，试验结果证实了该算法在 LSPI 比较中具有潜在优势，并引入了一个定理，证明了该算法是原则上可行的。