2021
On the Sample Complexity of Learning under Geometric Stability
NeurIPS 2021poster
Many supervised learning problems involve high-dimensional data such as images, text, or graphs. In order to make efficient use of data, it is often useful to leverage certain geometric priors in the problem at hand, such as invariance to translations, permutation subgroups, or stability to small de…