How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Prediction

no code implementations • 30 Jan 2020 • Se-eun Yoon, HyungSeok Song, Kijung Shin, Yung Yi

Hypergraphs provide a natural way of representing group relations, whose complexity motivates an extensive array of prior work to adopt some form of abstraction and simplification of higher-order interactions.

Hyperedge Prediction

Solving Continual Combinatorial Selection via Deep Reinforcement Learning

no code implementations • 9 Sep 2019 • Hyungseok Song, Hyeryung Jang, Hai H. Tran, Se-eun Yoon, Kyunghwan Son, Donggyu Yun, Hyoju Chung, Yung Yi

IS-MDP decomposes a joint action of selecting K items simultaneously into K iterative selections resulting in the decrease of actions at the expense of an exponential increase of states.

reinforcement-learning Reinforcement Learning (RL)

Learning Data Dependency with Communication Cost

no code implementations • 29 Apr 2018 • Hyeryung Jang, HyungSeok Song, Yung Yi

In this paper, we consider the problem of recovering a graph that represents the statistical data dependency among nodes for a set of data samples generated by nodes, which provides the basic structure to perform an inference task, such as MAP (maximum a posteriori).