15 citations · 24 across the 4 of their papers we have counts for
4 papers
Preference Estimation via Opponent Modeling in Multi-Agent Negotiation
Yuta Konishi, Kento Yamamoto, Eisuke Sonomoto +8
Automated negotiation in complex, multi-party and multi-issue settings critically depends on accurate opponent modeling. However, conventional numerical-only approaches fail to cap…
Heterogeneous Domain Adaptation with Positive and Unlabeled Data
Junki Mori, Ryo Furukawa, Isamu Teranishi +1
Heterogeneous unsupervised domain adaptation (HUDA) is the most challenging domain adaptation setting where the feature spaces of source and target domains are heterogeneous, and t…
Personalized Federated Learning with Multi-branch Architecture
Junki Mori, Tomoyuki Yoshiyama, Furukawa Ryo +1
Federated learning (FL) is a decentralized machine learning technique that enables multiple clients to collaboratively train models without requiring clients to reveal their raw da…
Continual Horizontal Federated Learning for Heterogeneous Data
Junki Mori, Isamu Teranishi, Ryo Furukawa
Federated learning is a promising machine learning technique that enables multiple clients to collaboratively build a model without revealing the raw data to each other. Among vari…