Learning to Estimate Without Bias

Understand

The Gauss Markov theorem states that the weighted least squares estimator is a linear minimum variance unbiased estimation (MVUE) in linear models.

• In this paper, we take a first step towards extending this result to non linear settings via deep learning with bias constraints.
• The classical approach to designing non-linear MVUEs is through maximum likelihood estimation (MLE) which often involves computationally challenging optimizations.
• On the other hand, deep learning methods allow for non-linear estimators with fixed computational complexity.