Yao Xie

Learning generative models for high-dimensional matrices doesn't have to be a computational nightmare: CoreFlow achieves state-of-the-art results in low-data regimes by learning shared low-rank structure.

Generative models offer a powerful toolkit for decision-making under distributional shift, enabling robust optimization and uncertainty quantification by learning and manipulating probability distributions.