论文标题
学习与高斯流程不一致的偏好
Learning Inconsistent Preferences with Gaussian Processes
论文作者
论文摘要
我们重审Chu等人（2005年）广泛使用优先使用的高斯流程，并挑战了他们的建模假设，该假设通过潜在效用函数值施加了数据项的排名。我们提出了PGP的概括，该PGP可以在数据中捕获更具表现力的潜在优先结构，因此用于模拟不一致的偏好，即违反传递性的情况，或通过学习的偏好功能的光谱分解来发现可比较项目的簇。我们还考虑了相关的协方差内核函数的属性及其再现的内核Hilbert Space（RKHS），从而提供了一种简单的结构，可满足偏好功能空间的普遍性。最后，我们在模拟和现实世界的数据集上提供了一组广泛的数值实验，以展示我们提出的方法与最先进的方法的竞争力。我们的实验发现支持这样的猜想，即违反排名性能在现实世界的优先数据中无处不在。
We revisit widely used preferential Gaussian processes by Chu et al.(2005) and challenge their modelling assumption that imposes rankability of data items via latent utility function values. We propose a generalisation of pgp which can capture more expressive latent preferential structures in the data and thus be used to model inconsistent preferences, i.e. where transitivity is violated, or to discover clusters of comparable items via spectral decomposition of the learned preference functions. We also consider the properties of associated covariance kernel functions and its reproducing kernel Hilbert Space (RKHS), giving a simple construction that satisfies universality in the space of preference functions. Finally, we provide an extensive set of numerical experiments on simulated and real-world datasets showcasing the competitiveness of our proposed method with state-of-the-art. Our experimental findings support the conjecture that violations of rankability are ubiquitous in real-world preferential data.
