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Deep Learning: a Non-parametric Statistical Viewpoint

Atkinson Hall, Fourth Floor

ABSTRACT The advent of deep learning has completely revolutionized how we perceive data to obtain superhuman performance across all fields of modern science. However, despite the remarkable empirical successes of deep learners, the theoretical guarantees for their statistical accuracy remain rather pessimistic. In particular, the data distributions on which deep learners are generally applied, such […]

Event Series EnCORE Series

EnCORE : Theoretical Exploration of Foundation Model Adaptation, Kangwook Lee, UW Madison, Feb 9th, 1-2pm

Atkinson Hall, Fourth Floor

Abstract: Due to the enormous size of foundation models, various new methods for efficient model adaptation have been developed. Parameter-efficient fine-tuning (PEFT) is an adaptation method that updates only a tiny fraction of the model parameters, leaving the remainder unchanged. In-context Learning (ICL) is a test-time adaptation method, which repurposes foundation models by providing them with labeled samples as part of the input context. Given the growing importance of this emerging paradigm, developing theoretical foundations for the new paradigm is of utmost importance.