论文标题
重新访问高斯过程稀疏光谱近似的样品复杂性
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes
论文作者
论文摘要
我们为高斯流程引入了一个新的可扩展近似值，并具有可证明的保证，该过程同时保证了其整个参数空间。我们的近似值是从改进的稀疏光谱高斯过程（SSGP）的样品复杂性分析获得的。特别是，我们的分析表明，在某些数据散开条件下，SSGP的预测和模型证据（用于培训）可以很好地陈述出较低样品复杂性的完整GP的预测。我们还开发了一种新的自动编码算法，该算法发现潜在空间将潜在的输入坐标将其解散到分离良好的群集中，这是我们样本复杂性分析的。我们在几个基准测试基准上验证了我们提出的方法，并有希望的结果支持我们的理论分析。
We introduce a new scalable approximation for Gaussian processes with provable guarantees which hold simultaneously over its entire parameter space. Our approximation is obtained from an improved sample complexity analysis for sparse spectrum Gaussian processes (SSGPs). In particular, our analysis shows that under a certain data disentangling condition, an SSGP's prediction and model evidence (for training) can well-approximate those of a full GP with low sample complexity. We also develop a new auto-encoding algorithm that finds a latent space to disentangle latent input coordinates into well-separated clusters, which is amenable to our sample complexity analysis. We validate our proposed method on several benchmarks with promising results supporting our theoretical analysis.
