Working Towards Explainable and Data-efficient Machine Learning Models via Symbolic Reasoning

In recent years, we have experienced the success of modern machine learning (ML) models. Many of them have led to unprecedented breakthroughs in a wide range of applications, such as AlphaGo beating a world champion human player or the introduction of autonomous vehicles. There has been a continuous effort, both from industry and academia, in bringing such advances into solving real-life problems. However, converting a successful ML model into a real-world product that leads to improved productivity is still a nontrivial task. First, modern ML methods are known for being data-hungry and inefficient.