Summary of A Generalized Model For Multidimensional Intransitivity, by Jiuding Duan et al.
A Generalized Model for Multidimensional Intransitivity
by Jiuding Duan, Jiyi Li, Yukino Baba, Hisashi Kashima
First submitted to arxiv on: 28 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Science and Game Theory (cs.GT); General Economics (econ.GN)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a probabilistic model to capture multifaceted intransitivity between players or objects in high-dimensional spaces. The model jointly learns player representations and a dataset-specific metric space to systematically capture distances between embedded points. Interestingly, the proposed model degenerates to former models used for intransitive representation learning when additional constraints are imposed on the metric space. A comprehensive quantitative investigation is conducted on various real-world benchmark datasets, showing that the proposed method outperforms competing methods in terms of prediction accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists try to solve a big problem called intransitivity. It’s like when you have to choose between three options and each one is better than another, but none are best overall. They propose a new way to represent things using math and statistics to capture these complex relationships. The method is tested on real-world data from social media, voting systems, and online games, showing it can make more accurate predictions. |
Keywords
» Artificial intelligence » Probabilistic model » Representation learning
