Learning Diverse Representations for Fast Adaptation to Distribution Shift

Daniel Pace, Alessandra Russo, Murray Shanahan

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 exp…