Learning from Group Comparisons: Exploiting Higher Order Interactions

We study the problem of learning from group comparisons, with applications in predicting outcomes of sports and online games. Most of the previous works in this area focus on learning individual effects--they assume each player has an underlying score, and the "ability" of the team is modeled by the sum of team