More often than not in benchmark supervised ML, tabular data is flat, i.e.
consists of a single $m \times d$ (rows, columns) file, but cases abound in the
real world where observations are described by a set of tables with structural
relationships. Neural nets-based deep models are a classical fit to incorporate
general topological dependence among description features (pixels, words,
etc.), but their suboptimality to tree-based models on tabular data is still
well documented. In this paper, we introduce an attention mechanism for
structured data that blends well with tree-based models in the training context
of (gradient) boosting. Each aggregated model is a tree whose training involves
two steps: first, simple tabular models are learned descending tables in a
top-down fashion with boosting's class residuals on tables' features. Second,
what has been learned progresses back bottom-up via attention and aggregation
mechanisms, progressively crafting new features that complete at the end the
set of observation features over which a single tree is learned, boosting's
iteration clock is incremented and new class residuals are computed.
Experiments on simulated and real-world domains display the competitiveness of
our method against a state of the art containing both tree-based and neural
nets-based models.

基于树模型的注意机制结合与 (tabular data) 表格数据在 (gradient boosting) 梯度提升训练环境中学习，被证明在多个领域与包含树模型和神经网络模型的现有技术相比具有竞争力。