2 papers
cs.CL2026
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…
cs.LG2023
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…