Daniel Pace

Learning Diverse Representations for Fast Adaptation to Distribution Shift

2020 · arXiv (Cornell University)

The i.i.d. assumption is a useful idealization that underpins many successful approaches to supervised machine learning. However, its violation can lead to models that learn to exploit spurious correlations in the training data, rendering them …