• Corpus ID: 235489741

On the benefits of maximum likelihood estimation for Regression and Forecasting

@article{Awasthi2022OnTB,
  title={On the benefits of maximum likelihood estimation for Regression and Forecasting},
  author={Pranjal Awasthi and Abhimanyu Das and Rajat Sen and Ananda Theertha Suresh},
  journal={ArXiv},
  year={2022},
  volume={abs/2106.10370}
}
We advocate for a practical Maximum Likelihood Estimation (MLE) approach towards designing loss functions for regression and forecasting, as an alternative to the typical approach of direct empirical risk minimization on a specific target metric. The MLE approach is better suited to capture inductive biases such as prior domain knowledge in datasets, and can output post-hoc estimators at inference time that can optimize different types of target metrics. We present theoretical results to… 

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