Iterative Chow Filtering for Learning with Distribution Shift
Recent work due to Goel et al. gave the first efficient algorithms for learning with distribution shift in the challenging PQ framework. In this setting, a learner receives labeled training examples, unlabeled test examples, and must make correct predictions on the test set but is allowed to abstain from predicting on out-of-distribution points. Their results rely on ${\cal L}_2$ sandwiching approximations, a strong requirement that leads to poor bounds for several basic function classes such as DNF formulas. Here, we show that the weaker notion of ${\cal L}_1$ sandwiching suffices for efficient PQ learning. As a consequence, we obtain the first quasipolynomial-time PQ learning algorithm ...
| Authors | Gautam Chandrasekaran, Georgios Gkrinias, Adam R. Klivans et al. |
|---|---|
| Primary category | cs.DS |
| Published | 17 May 2026 |
| Last updated at source | 17 May 2026 |
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