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For many machine learning algorithms, two main assumptions are required toguarantee performance. One is that the test data are drawn from the samedistribution as the training data, and the other is that the model is correctlyspecified. In real applications, however, we often have little prior knowledgeon the test data and on the underlying true model. Under modelmisspecification, agnostic distribution shift between training and test dataleads to inaccuracy of parameter estimation and instability of predictionacross unknown test data. To address these problems, we propose a novelDecorrelated Weighting Regression (DWR) algorithm which jointly optimizes avariable decorrelation regularizer and a weighted regression model. Thevariable decorrelation regularizer estimates a weight for each sample such thatvariables are decorrelated on the weighted training data. Then, these weightsare used in the weighted regression to improve the accuracy of estimation onthe effect of each variable, thus help to improve the stability of predictionacross unknown test data. Extensive experiments clearly demonstrate that ourDWR algorithm can significantly improve the accuracy of parameter estimationand stability of prediction with model misspecification and agnosticdistribution shift.

Stable Prediction with Model Misspecification and Agnostic Distribution Shift