Given a pre-trained classifier and multiple human experts, we investigate the task of online
classification where model predictions are provided for free but querying humans incurs a …

AI errors pose a significant challenge, hindering real-world applications. This work
introduces a novel approach to cope with AI errors using weakly supervised error correctors …

We introduce Frank, a human-in-the-loop system for co-evolutionary hybrid decision-making
aiding the user to label records from an un-labeled dataset. Frank employs incremental …

On the level of decision support, most algorithmic problems encountered in machine
learning are instances of pure prediction or pure automation tasks. This dissertation takes a …

Graph data show relationships between entities in a variety of domains including but not
limited to communication, social, and interaction networks. Representation learning makes …