MS student, Carleton University
2 papers at NeurIPS 2025
We introduce a selector-extractor framework that extracts high-res features without ever seeing full high-res images to save compute.
We unify submodular/supermodular ratio problems and show general algorithms like SuperGreedy++ and min-norm point methods are surprisingly efficient and scalable, outperforming specialized methods in theory and practice