论文标题
一个一致的结构化预测的一般框架，并具有隐式损失嵌入
A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings
论文作者
论文摘要
我们提出和分析了一个新型的理论和算法框架，用于结构化预测。虽然到目前为止，该术语已提到离散的输出空间，但在这里我们考虑了更多的一般设置，例如概率度量的歧管或空间。我们将结构化预测定义为输出空间缺乏矢量结构的问题。我们识别并研究了大量的损失函数，这些损失函数暗中定义了有关该问题的合适几何形状。后者是开发算法框架的关键，适合进行尖锐的统计分析并得出有效的计算。在处理无限基数的输出空间时，估计器的合适隐式公式被证明是至关重要的。
We propose and analyze a novel theoretical and algorithmic framework for structured prediction. While so far the term has referred to discrete output spaces, here we consider more general settings, such as manifolds or spaces of probability measures. We define structured prediction as a problem where the output space lacks a vectorial structure. We identify and study a large class of loss functions that implicitly defines a suitable geometry on the problem. The latter is the key to develop an algorithmic framework amenable to a sharp statistical analysis and yielding efficient computations. When dealing with output spaces with infinite cardinality, a suitable implicit formulation of the estimator is shown to be crucial.
