Her committee members are as follows: Ryan Adams (adviser), Karthik Narasimhan (reader)

Zoom link: https://princeton.zoom.us/j/92754574624

Please see abstract below.  All are welcome to attend.

There exist certain discrepancies between how humans and machine learning models learn and make decisions. These discrepancies can not only make it difficult to model human behavior, but can also decrease the efficiency with which models predict and take actions. This thesis will focus on two of these discrepancies, mainly quickly adapting to new tasks using existing knowledge, and the influence of collective, societal goals in individual decision making. It will do this through 2 projects that attempt to provide machine-learning solutions towards allowing for them. The first project focuses on adaptation on the road. By leveraging meta-learning in autonomous vehicle driving, and treating experiences with other drivers as separate tasks, our model learns to generalize knowledge from across different types of drivers. In turn, it is able to quickly adapt to unseen drivers using limited driving experience with them. The second project focuses on collective goal seeking in a multi-agent environment. We use a group intrinsic reward function that is optimized over time to provide agents with an immediate intrinsic reward that they must maximize along with their selfish reward. By using the expected lifetime reward of an agent in the environment to optimize the intrinsic reward function, our agents learn to take possibly sub-optimal actions that benefit the collective group, rather than just themselves.