On the robust learning mixtures of linear regressions

This work addresses robust learning for mixtures of linear regressions, which is an incremental improvement in statistical learning with potential applications in data analysis and machine learning.

The paper tackles the problem of robust learning mixtures of linear regressions by connecting it to mixtures of Gaussians via thresholding, resulting in a quasi-polynomial time algorithm under mild separation conditions that offers significantly better robustness than prior methods.