The Best Machine Learning Research of June 2019

Machine Learning and the data science industry is always changing. To keep you updated on the most recent discoveries, we've compiled the 5 most exciting machine learning research pieces that expand what we thought we knew about machine learning and the industries to which it relates. Fairness in machine learning has been a heavy topic of discussion since the beginnings of the technology, but now, in a paper by Candice Schumann, Xuezhi Wang, Alex Beutel, Jilin Chen, Hai Qian, and Ed H. Chi we have some theoretical models to ensure fairness across different applications of one machine learning model. They frame this issue as "domain adaptation problems: how can we use what we have learned in a source domain to debias in a new target domain, without directly debiasing on the target domain as if it is a completely new problem?" In the paper, they also offer "a modeling approach to transfer to data-sparse target domains… [and] empirical results validating the theory and showing that these modeling approaches can improve fairness metrics with less data" In a recent paper by Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, and Stefano Ermon, they explore "which uncertainties are needed for model-based reinforcement learning and argues that good uncertainties must be calibrated."