The world is structured in countless ways. It may be prudent to enforce corresponding structural properties to a learning algorithm's solution, such as incorporating prior beliefs, natural constraints, or causal structures. Doing so may translate to faster, more accurate, and more flexible models, which may directly relate to real-world impact. In this dissertation, we consider two different research areas that concern structuring a learning algorithm's solution: when the structure is known and when it has to be discovered.

翻译：这个世界以无数方式构建。 谨慎的做法是对学习算法的解决方案实施相应的结构性属性,比如整合先前的信念、自然约束或因果结构。 这样做可以转化成更快、更准确、更灵活的模型,这些模型可能直接与现实世界的影响相关。 在这份论文中,我们考虑了两个不同的研究领域,涉及到学习算法的解决方案结构:当该结构为人所知时,当它必须被发现时。