Dual Regression

Dual regression encompasses a range of techniques aiming to improve the efficiency and accuracy of regression models, addressing challenges like limited data, computational constraints, and ill-posed problems. Current research focuses on developing novel algorithms, such as conformal thresholded intervals for precise prediction sets and adaptive compression strategies for distributed regression networks, as well as leveraging dual learning frameworks to constrain the solution space and improve model compactness. These advancements are significant for improving the performance and scalability of regression across diverse applications, from image super-resolution to text generation in low-resource languages.