Zelda Mariet

My research focuses on analyzing strongly Rayleigh (SR) measures as a tool to approach machine-learning problems. These measures, among which Determinantal Point Processes have already proven to be of significant interest to the ML community, encode negative dependence between items in subsets of a ground set: they endow the space with repulsive forces between similar points, enabling a careful balancing of the quality and diversity of a subset. I aim to both develop scalable learning and sampling for SR measures over large datasets, and to leverage their properties to guide machine-learning design and analysis. I did my PhD at MIT, working with Suvrit Sra and as a member of the Machine Learning and Learning and Intelligent Systems groups.