150 citations · 431 across the 41 of their papers we have counts for
42 papers
A Generalized Model for Multidimensional Intransitivity
Jiuding Duan, Jiyi Li, Yukino Baba +1
Intransitivity is a critical issue in pairwise preference modeling. It refers to the intransitive pairwise preferences between a group of players or objects that potentially form a…
Label Selection Approach to Learning from Crowds
Kosuke Yoshimura, Hisashi Kashima
Supervised learning, especially supervised deep learning, requires large amounts of labeled data. One approach to collect large amounts of labeled data is by using a crowdsourcing…
Mitigating Voter Attribute Bias for Fair Opinion Aggregation
Ryosuke Ueda, Koh Takeuchi, Hisashi Kashima
The aggregation of multiple opinions plays a crucial role in decision-making, such as in hiring and loan review, and in labeling data for supervised learning. Although majority vot…
Regularizing Neural Networks with Meta-Learning Generative Models
Shin'ya Yamaguchi, Daiki Chijiwa, Sekitoshi Kanai +2
This paper investigates methods for improving generative data augmentation for deep learning. Generative data augmentation leverages the synthetic samples produced by generative mo…
Mitigating Observation Biases in Crowdsourced Label Aggregation
Ryosuke Ueda, Koh Takeuchi, Hisashi Kashima
Crowdsourcing has been widely used to efficiently obtain labeled datasets for supervised learning from large numbers of human resources at low cost. However, one of the technical c…
Multiview Representation Learning from Crowdsourced Triplet Comparisons
Xiaotian Lu, Jiyi Li, Koh Takeuchi +1
Crowdsourcing has been used to collect data at scale in numerous fields. Triplet similarity comparison is a type of crowdsourcing task, in which crowd workers are asked the questio…