Fri 13 Dec 11 a.m. PST — 2 p.m. PST
East Exhibit Hall A-C #2204

Paper: neurips.cc/virtual/2024...

In this paper, we tackle shifts caused by an unknown attribute with an approach opposite to bootstrapping: we use small samples to generate synthetic environments with different "kinds" of classes and learn more robust data representations.

But in zero-shot, we face new classes at test time. To adapt, we need to know which "kind" of classes to emphasize. But in reality, the shift is often unknown.

Class distribution shifts are often seen as the easiest to handle—that's often true for supervised learning, thanks to reweighting/resampling.

Hi, would love to be added! Thanks!

Hi! Would love to be added. Thanks!

Hi! Would love to be added! Thanks!

Hi! I'd love to be added. Thanks!

Hi! Could you please add me to the starter pack? Thanks!

Hi! Could you please add me to the starter pack? Thanks!