The TAU Programming Languages and Systems Seminar - Exact Learning in Data-driven Systems

Abstract

Many software systems rely on data-driven models to make decisions. Examples include self-driving cars, malware detection and aircraft collision avoidance detection. Unfortunately, data-driven models often do not generalize well on unseen examples, despite showing high accuracy on test sets. This was demonstrated by showing how to fool these models using adversarial examples. Such adversarial examples may result in disastrous consequences in safety-critical systems that rely on these models. It becomes clear that high accuracy is insufficient in these cases, and exactness is a desired property. In this talk, I will discuss a new approach which recovers exactness in data-driven models. This approach involves interaction with a user to classify examples, a crucial aspect is minimizing the number of questions posed to the user. I will then present two algorithms that guarantee exactness in the setting of program synthesis from examples. I will also show experimental results that support the importance of exactness in practice.