An umbrella for our work on labels that cannot be trusted: blind ensemble learning, robust crowdsourcing in the presence of spammers and adversaries, and constrained crowdsourcing that exploits side information.
Building useful representations from unlabeled data by combining several self-supervised tasks, and using them when labels are scarce, including physics-informed representations for power networks.
Fast spectral embeddings for graphs that evolve over time, inference of the network topology itself, and robust learning when the graph is corrupted or attacked. Supported by the NSF project Robust Learning over Graphs.
Clustering large, high-dimensional datasets that lie near a union of low-dimensional subspaces, using random sketching, sampling and distributed computation.
