Title: Correcting Model Misspecification via Generative Adversarial Networks Show Chinese title

Title: 通过生成对抗网络校正模型误指

Abstract: Machine learning models are often misspecified in the likelihood, which leads to a lack of robustness in the predictions. In this paper, we introduce a framework for correcting likelihood misspecifications in several paradigm agnostic noisy prior models and test the model's ability to remove the misspecification. The "ABC-GAN" framework introduced is a novel generative modeling paradigm, which combines Generative Adversarial Networks (GANs) and Approximate Bayesian Computation (ABC). This new paradigm assists the existing GANs by incorporating any subjective knowledge available about the modeling process via ABC, as a regularizer, resulting in a partially interpretable model that operates well under low data regimes. At the same time, unlike any Bayesian analysis, the explicit knowledge need not be perfect, since the generator in the GAN can be made arbitrarily complex. ABC-GAN eliminates the need for summary statistics and distance metrics as the discriminator implicitly learns them and enables simultaneous specification of multiple generative models. The model misspecification is simulated in our experiments by introducing noise of various biases and variances. The correction term is learnt via the ABC-GAN, with skip connections, referred to as skipGAN. The strength of the skip connection indicates the amount of correction needed or how misspecified the prior model is. Based on a simple experimental setup, we show that the ABC-GAN models not only correct the misspecification of the prior, but also perform as well as or better than the respective priors under noisier conditions. In this proposal, we show that ABC-GANs get the best of both worlds. Show Chinese abstract

Abstract: 机器学习模型在似然函数方面常常存在错误设定，这导致预测结果缺乏稳健性。 在本文中，我们引入了一个框架，用于纠正几种与范式无关的噪声先验模型中的似然函数错误设定，并测试模型消除错误设定的能力。 引入的“ABC-GAN”框架是一种新颖的生成建模范式，它结合了生成对抗网络（GANs）和近似贝叶斯计算（ABC）。 这一新范式通过ABC将关于建模过程的任何主观知识作为正则化器引入现有的GANs，从而得到一个部分可解释的模型，在数据量较少的情况下也能良好运行。 同时，与任何贝叶斯分析不同，显式知识不需要完美，因为GAN中的生成器可以变得任意复杂。 ABC-GAN消除了对摘要统计量和距离度量的需求，因为判别器会隐式地学习它们，并且能够同时指定多个生成模型。 在我们的实验中，通过引入具有不同偏差和方差的噪声来模拟模型错误设定。 通过带有跳跃连接的ABC-GAN学习校正项，称为skipGAN。 跳跃连接的强度表明所需的校正量或先验模型的错误设定程度。 基于一个简单的实验设置，我们展示了ABC-GAN模型不仅能够校正先验模型的错误设定，而且在更嘈杂的条件下表现得与相应先验模型相当或更好。 在这个提案中，我们展示了ABC-GAN能够兼得两者的优点。