论文标题
联合控制变化的速度更快的黑盒变量推断
Joint control variate for faster black-box variational inference
论文作者
论文摘要
使用具有较高差异的梯度估计器有时会阻碍黑框变分推断性能。该方差来自两个随机性来源：数据亚采样和蒙特卡洛采样。虽然现有的控制变化仅解决蒙特卡洛噪声，而增量梯度方法通常仅解决数据子采样，但我们提出了一种新的“关节”控制变量，该变量可共同降低两个噪声源的差异。这大大降低了梯度差异，从而在几种应用中更快地优化。
Black-box variational inference performance is sometimes hindered by the use of gradient estimators with high variance. This variance comes from two sources of randomness: Data subsampling and Monte Carlo sampling. While existing control variates only address Monte Carlo noise, and incremental gradient methods typically only address data subsampling, we propose a new "joint" control variate that jointly reduces variance from both sources of noise. This significantly reduces gradient variance, leading to faster optimization in several applications.
