Learning from group comparisons: Exploiting higher order interactions

Yao Li, Kevin Fujii, Minhao Cheng, Fushing Hsieh, Cho Jui Hsieh

Research output: Contribution to journalConference articlepeer-review

14 Scopus citations


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 members' scores. Therefore, current approaches cannot model deeper interaction between team members: some players perform much better if they play together, while some players perform poorly together. In this paper, we propose a new model that takes the player-interaction effects into consideration. However, under certain circumstances, the total number of individuals can be very large, and number of player interactions grows quadratically, which makes learning intractable. In this case, we propose a latent factor model, and show that the sample complexity of our model is bounded under mild assumptions. Finally, we show that our proposed models have much better prediction power on several E-sports datasets, and furthermore can be used to reveal interesting patterns that cannot be discovered by previous methods.

Original languageEnglish (US)
Pages (from-to)4981-4990
Number of pages10
JournalAdvances in Neural Information Processing Systems
StatePublished - 2018
Event32nd Conference on Neural Information Processing Systems, NeurIPS 2018 - Montreal, Canada
Duration: Dec 2 2018Dec 8 2018

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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