论文标题
可区分的DAG采样
Differentiable DAG Sampling
论文作者
论文摘要
我们建议使用DAG（DP-DAG）的新的可区分概率模型。 DP-DAG允许适合连续优化的快速和可区分的DAG采样。为此，DP-DAG通过（1）采样节点的线性排序和（2）与采样的线性排序一致的采样边来采样DAG。我们进一步提出了VI-DP-DAG，这是一种从观察数据中学习的新方法，将DP-DAG与变异推理结合在一起。因此，鉴于观察到的数据，VI-DP-DAG近似于DAG边缘的后验概率。 VI-DP-DAG可以保证在培训期间的任何时间输出有效的DAG，并且与现有的可区分DAG学习方法相比，不需要任何复杂的增强拉格朗日优化方案。在我们的广泛实验中，我们将VI-DP-DAG与其他可区分的DAG学习基准进行了比较，以合成和真实数据集进行比较。 VI-DP-DAG显着改善了DAG结构和因果机制学习，而训练速度比竞争对手更快。
We propose a new differentiable probabilistic model over DAGs (DP-DAG). DP-DAG allows fast and differentiable DAG sampling suited to continuous optimization. To this end, DP-DAG samples a DAG by successively (1) sampling a linear ordering of the node and (2) sampling edges consistent with the sampled linear ordering. We further propose VI-DP-DAG, a new method for DAG learning from observational data which combines DP-DAG with variational inference. Hence,VI-DP-DAG approximates the posterior probability over DAG edges given the observed data. VI-DP-DAG is guaranteed to output a valid DAG at any time during training and does not require any complex augmented Lagrangian optimization scheme in contrast to existing differentiable DAG learning approaches. In our extensive experiments, we compare VI-DP-DAG to other differentiable DAG learning baselines on synthetic and real datasets. VI-DP-DAG significantly improves DAG structure and causal mechanism learning while training faster than competitors.
