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.
- Pub Date:
- November 2021
- Computer Science - Machine Learning;
- Computer Science - Artificial Intelligence
- Updated version of my PhD thesis, with fixed typos. Will keep updated as new typos are discovered