3 papers
cs.LG2026
Geometric Data Perturbation with Noisy-Anchor Alignment for Privacy-Preserving Collaborative Learning
Keiyu Nosaka, Yamato Suetake, Yuichi Takano +2
Geometric Data Perturbation (GDP) enables one-shot, privacy-preserving collaborative learning: each participant applies a distance-preserving transformation to its private data and…
cs.LG2024
Data Collaboration Analysis with Orthonormal Basis Selection and Alignment
Keiyu Nosaka, Yamato Suetake, Yuichi Takano +1
Data Collaboration (DC) enables multiple parties to jointly train a model by sharing only linear projections of their private datasets. The core challenge in DC is to align the bas…
math.OC2024
Post-Processing with Projection and Rescaling Algorithms for Semidefinite Programming
Shin-ichi Kanoh, Akiko Yoshise
We propose the algorithm that solves the symmetric cone programs (SCPs) by iteratively calling the projection and rescaling methods the algorithms for solving exceptional cases of…