Enforcing and Discovering Structure in Machine Learning
This work tackles the challenge of enhancing model performance through structured solutions, which is incremental as it builds on existing methods for incorporating prior beliefs and constraints.
The dissertation addresses the problem of incorporating known or discovered structural properties into machine learning algorithms to improve their speed, accuracy, and flexibility, with potential real-world impact.
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.