论文标题
流式传播Pac-Bayes高斯流程回归，并提供在线决策的性能保证
Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision making
论文作者
论文摘要
作为强大的贝叶斯非参数化算法，高斯工艺（GP）在贝叶斯优化和信号处理中发挥了重要作用。全科医生还具有先进的在线决策系统，因为它们的后验分布具有封闭形式的解决方案。但是，其培训和推理过程需要存储所有历史数据，而GP模型则需要从头开始训练。由于这些原因，已专门为流式设置设计了几种在线GP算法，例如O-SGPR和O-SVGP。在本文中，我们基于在线的在线GPS提出了一个新的理论框架，这可能是近似正确的（PAC）贝叶斯理论。该框架既可以保证广泛的性能和良好的准确性。我们的算法不是最大程度地减少边际可能性，而是优化了经验风险函数和正则化项，这与参数的先前分布和后验分布之间的差异成比例。除了其理论吸引力外，该算法在几个回归数据集上的经验表现良好。与其他在线GP算法相比，我们的算法产生了概括保证和非常有竞争力的准确性。
As a powerful Bayesian non-parameterized algorithm, the Gaussian process (GP) has performed a significant role in Bayesian optimization and signal processing. GPs have also advanced online decision-making systems because their posterior distribution has a closed-form solution. However, its training and inference process requires all historic data to be stored and the GP model to be trained from scratch. For those reasons, several online GP algorithms, such as O-SGPR and O-SVGP, have been specifically designed for streaming settings. In this paper, we present a new theoretical framework for online GPs based on the online probably approximately correct (PAC) Bayes theory. The framework offers both a guarantee of generalized performance and good accuracy. Instead of minimizing the marginal likelihood, our algorithm optimizes both the empirical risk function and a regularization item, which is in proportion to the divergence between the prior distribution and posterior distribution of parameters. In addition to its theoretical appeal, the algorithm performs well empirically on several regression datasets. Compared to other online GP algorithms, ours yields a generalization guarantee and very competitive accuracy.
