@inproceedings{
mittal2024adaptive,
title={Adaptive Labeling for Efficient Out-of-distribution Model Evaluation},
author={Daksh Mittal and Yuanzhe Ma and Shalmali Joshi and Hongseok Namkoong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=uuQQwrjMzb}
}
Datasets often suffer severe selection bias; clinical labels are only available on patients for whom doctors ordered medical exams. To assess model performance outside the support of available data, we present a computational framework for adaptive labeling, providing cost-efficient model evaluations under severe distribution shifts. We formulate the problem as a Markov Decision Process over states defined by posterior beliefs on model performance. Each batch of new labels incurs a “state transition” to sharper beliefs, and we choose batches to minimize uncertainty on model performance at the end of the label collection process. Instead of relying on high-variance REINFORCE policy gradient estimators that do not scale, our adaptive labeling policy is optimized using path-wise policy gradients computed by auto-differentiating through simulated roll-outs. Our framework is agnostic to different uncertainty quantification approaches and highlights the virtue of planning in adaptive labeling. On synthetic and real datasets, we empirically demonstrate even a one-step lookahead policy substantially outperforms active learning-inspired heuristics.
Adaptive Labeling for Efficient Out-of-distribution Model Evaluation
Daksh Mittal Yuanzhe Ma Shalmali Joshi Hongseok Namkoong