论文标题
对比度学习的统计应用
Statistical applications of contrastive learning
论文作者
论文摘要
可能性功能在统计推断和实验设计中起着至关重要的作用。但是，对于几种重要类别的统计模型（包括基于能量的模型和基于模拟器的模型），它在计算上是可悲的。对比学习是基于似然学习的直观且计算上可行的替代方法。我们在这里首先提供了对比度学习的介绍，然后展示如何使用它来推导各种统计问题的方法，即基于能量的模型的参数估计，基于模拟器的模型的贝叶斯推断以及实验设计。
The likelihood function plays a crucial role in statistical inference and experimental design. However, it is computationally intractable for several important classes of statistical models, including energy-based models and simulator-based models. Contrastive learning is an intuitive and computationally feasible alternative to likelihood-based learning. We here first provide an introduction to contrastive learning and then show how we can use it to derive methods for diverse statistical problems, namely parameter estimation for energy-based models, Bayesian inference for simulator-based models, as well as experimental design.
