论文标题
用自适应替代品优化黑盒指标
Optimizing Black-box Metrics with Adaptive Surrogates
论文作者
论文摘要
我们通过将标准作为少量易于优化的替代物的单调函数表达为单调函数来解决用黑框和难以优化指标的训练模型的问题。我们将训练问题作为对放松的替代空间的优化，我们通过估计公制和执行不精确凸投影的本地梯度来解决。我们根据有限的差异和局部线性插值来分析梯度估计，并在平滑度假设相对于替代物下显示我们的方法的收敛性。关于分类和排名问题的实验结果验证该提案的性能与知道数学公式的方法相同，并在未知的指标形式时增加了显着的价值。
We address the problem of training models with black-box and hard-to-optimize metrics by expressing the metric as a monotonic function of a small number of easy-to-optimize surrogates. We pose the training problem as an optimization over a relaxed surrogate space, which we solve by estimating local gradients for the metric and performing inexact convex projections. We analyze gradient estimates based on finite differences and local linear interpolations, and show convergence of our approach under smoothness assumptions with respect to the surrogates. Experimental results on classification and ranking problems verify the proposal performs on par with methods that know the mathematical formulation, and adds notable value when the form of the metric is unknown.
