Prof. Yaoliang Yu

December 6th, 2024 – 11am-12pm, EC4-2101A

Deep learning has often been criticized as a black box, whose predictions are superb and yet opaque. Lots of efforts have been made to explain/interpret the predictions of machine learning. In this talk I will first give a selected (and biased) overview of some existing approaches to feature and data attribution. Then, I will discuss faster approximation algorithms for computing the probabilistic value (of which the celebrated Shapley value is a special case). Lastly, I will present some limitations of current approaches.