Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation
arXiv:2003.00343
Abstract
Reliable uncertainty estimates are an important tool for helping autonomous agents or human decision makers understand and leverage predictive models. However, existing approaches to estimating uncertainty largely ignore the possibility of covariate shift--i.e., where the real-world data distribution may differ from the training distribution. As a consequence, existing algorithms can overestimate certainty, possibly yielding a false sense of confidence in the predictive model. We propose an algorithm for calibrating predictions that accounts for the possibility of covariate shift, given labeled examples from the training distribution and unlabeled examples from the real-world distribution. Our algorithm uses importance weighting to correct for the shift from the training to the real-world distribution. However, importance weighting relies on the training and real-world distributions to be sufficiently close. Building on ideas from domain adaptation, we additionally learn a feature map that tries to equalize these two distributions. In an empirical evaluation, we show that our proposed approach outperforms existing approaches to calibrated prediction when there is covariate shift.
arXiv commentAccepted to AISTATS 2020
References in corpus (6)
- On Calibration of Modern Neural Networks
- Conditional Adversarial Domain Adaptation
- Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
- Accurate Uncertainties for Deep Learning Using Calibrated Regression
- PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction
- Estimating Uncertainty Online Against an Adversary
Cited by in corpus (4)
- Calibrating AI Models for Few-Shot Demodulation via Conformal Prediction
- Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration Error
- Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis
- Beyond Confidence: Adaptive Abstention in Dual-Threshold Conformal Prediction for Autonomous System Perception