1 citations · 1 across the 10 of their papers we have counts for
5 papers · 1 filter
FedAlphaEdit: Null-Space-Aligned Merging for Collaborative Knowledge Editing
Sota Sugawara, Yukihiko Okada
Multiple institutions may each hold their own private knowledge edits and wish to integrate them into a single large language model without sharing raw edit requests. Null-space-co…
Geometric Data Perturbation with Noisy-Anchor Alignment for Privacy-Preserving Collaborative Learning
Keiyu Nosaka, Yamato Suetake, Yuichi Takano +2
Geometric data perturbation enables one-shot representation sharing for privacy-preserving collaborative learning: each participant applies a secret distance-preserving transformat…
Covariance-Based Structural Equation Modeling in Small-Sample Settings with
Hiroki Hasegawa, Aoba Tamura, Yukihiko Okada
Factor-based Structural Equation Modeling (SEM) relies on likelihood-based estimation assuming a nonsingular sample covariance matrix, which breaks down in small-sample settings wi…
Interaction Tensor SHAP
Hiroki Hasegawa, Yukihiko Okada
This study proposes Interaction Tensor SHAP (IT-SHAP), a tensor algebraic formulation of the Shapley Taylor Interaction Index (STII) that makes its computational structure explicit…
Single-Round Clustered Federated Learning via Data Collaboration Analysis for Non-IID Data
Sota Sugawara, Yuji Kawamata, Akihiro Toyoda +2
Federated Learning (FL) enables distributed learning across multiple clients without sharing raw data. When statistical heterogeneity across clients is severe, Clustered Federated…