Professor, University of California, San Diego
2 papers at NeurIPS 2025
We introduce a new SGD-based algorithm with delayed projection for training kernel machines that achieves comparable or superior performance while reducing training time from days to under an hour.
Diffusion noise seeds contain universal compositional blueprints that persist across datasets, enabling our Patch PCA framework for zero-shot structure control.
