Selective Regression
Selective regression, also known as selective prediction for regression tasks, focuses on developing machine learning models that can abstain from making predictions when uncertainty is high, prioritizing reliability over always providing an output. Current research emphasizes improving the accuracy of uncertainty estimation, exploring both model-agnostic approaches and incorporating techniques like conformal prediction to provide calibrated confidence measures. This field is gaining traction due to the increasing demand for trustworthy and reliable machine learning systems in high-stakes applications, where avoiding erroneous predictions is crucial. The development of novel algorithms, such as those employing Gumbel-softmax for differentiable training, and standardized evaluation frameworks are driving progress in this area.