Covariate Shift - Challenges & Good Practice • Joyce Wang • YOW! 2017

Covariate shift occurs when training and test data distributions don’t match, violating a fundamental assumption of supervised learning

Key consequences of covariate shift:

• Models overfit to training examples
• Predictions become unreliable on query/test sets
• Poor generalization to real-world scenarios

Detection methods:

• Visualization of training vs query set distributions
• Membership modeling to classify training vs query samples
• Uncertainty quantification using probabilistic models

Two main strategies to handle covariate shift:

1. Training Sample Reweighting:

   • Adjusts training data distribution to match query set
   • Requires overlap between training and query distributions
   • Not feasible when unable to obtain more samples

2. Active Learning:

   • Selectively chooses most informative new training samples
   • Prioritizes areas of high uncertainty
   • More cost-effective than random sampling
   • Does not require distribution overlap

Best practices:

• Detect covariate shift before model deployment
• Use dimensionality reduction for high-dimensional data visualization
• Consider sample selection bias when collecting training data
• Implement uncertainty quantification to identify unreliable predictions
• Validate model performance on representative test sets

Common real-world causes:

• Limited budgets for data collection
• Biased sampling procedures
• Geographic or demographic differences
• Changes in data distribution over time