论文标题
损失校准的期望传播近似于贝叶斯决策
Loss-calibrated expectation propagation for approximate Bayesian decision-making
论文作者
论文摘要
近似贝叶斯推理方法提供了一套强大的工具，用于寻找棘手的后验分布的近似值。但是，机器学习应用程序通常涉及选择动作，在贝叶斯环境中，仅通过其对预期实用程序的贡献来取决于后验分布。因此，越来越多地在损失损失的近似推理方法上进行工作，试图发展对实用程序函数影响敏感的后近似值。在这里，我们介绍了损失的期望传播（Loss-EP），这是一种经过损失的期望传播变体。该方法类似于标准EP的附加因素，该因素将倾斜的较高效率决策“倾斜”。我们在二进制实用程序功能下向高斯过程分类显示了对假阴性和假阳性错误的不对称惩罚的应用，并显示这种不对称性如何对哪些信息在近似中“有用”具有巨大的后果。
Approximate Bayesian inference methods provide a powerful suite of tools for finding approximations to intractable posterior distributions. However, machine learning applications typically involve selecting actions, which -- in a Bayesian setting -- depend on the posterior distribution only via its contribution to expected utility. A growing body of work on loss-calibrated approximate inference methods has therefore sought to develop posterior approximations sensitive to the influence of the utility function. Here we introduce loss-calibrated expectation propagation (Loss-EP), a loss-calibrated variant of expectation propagation. This method resembles standard EP with an additional factor that "tilts" the posterior towards higher-utility decisions. We show applications to Gaussian process classification under binary utility functions with asymmetric penalties on False Negative and False Positive errors, and show how this asymmetry can have dramatic consequences on what information is "useful" to capture in an approximation.
